Ventilatory Control Research Articles

Phenotyping Using Polysomnography Attributes Reduced Respiratory Events after CPAP Therapy to Improved Upper Airway Collapsibility.

Rationale: In patients with obstructive sleep apnea (OSA) treated with continuous positive airway pressure (CPAP), the apnea hypopnea index (AHI) measured off CPAP may be decreased relative to baseline AHI preceding CPAP treatment. Semi-invasive "endo-phenotyping" sleep studies attribute this fall in AHI primarily to improved ventilatory control stability. Phenotyping Using Polysomnography (PUP) attempts to reproduce these studies using routine polysomnography (PSG). Objectives: To determine whether changes in AHI following CPAP associate primarily with changes in PUP-estimated ventilatory control stability (loop gain, LG1) or with changes in other PUP-estimated pathophysiologic mechanisms. Methods: PUP analyses were performed on existing PSGs in research participants who underwent baseline PSG, 4.4±2.2 months CPAP therapy, and CPAP withdrawal with repeat PSG on night 2 of withdrawal. Pre-CPAP PUP-estimated LG1, arousal threshold (ArTH), and upper airway collapsibility (Vpassive) and compensation (Vcomp) were compared to corresponding values during CPAP withdrawal. Mixed effects models were constructed to determine which PUP estimate best explained changes in AHI. Results: PSG data were available for 35 participants (age 47±10.8 years; 12 female; BMI 38.5±8.6 kg/m2, AHI3A 58.8±33.1 events/hr, 9 mild/moderate OSA, 26 severe OSA). Following CPAP, AHI decreased, but the change was not statistically significant. However, a significant decrease was observed in those with severe OSA (pre-CPAP 68.2 [32.6-86.3] versus CPAP withdrawal 49.0 [36.1-74.4] events/hr). Across all participants, changes in PUP estimates did not exceed test-retest agreement limits. For those with severe OSA, decrease in LG1 (0.86 [0.61-1.13] pre-CPAP versus 0.71 [0.61-0.99] on CPAP withdrawal) and increase in Vpassive (64.8 [5.4-88.4] %Veupnea pre-CPAP versus 76.4 [20.7-92.7] %Veupnea on CPAP withdrawal) exceeded test-retest agreement limits. Increased Vpassive, decreased LG1, and decreased ArTH were predictors of decreased AHI in mixed effects models. Vpassive had the greatest estimated effect on AHI. After accounting for Vpassive, additional estimates did not improve model performance. However, Vpassive and LG1 were correlated, and post hoc analyses suggest these estimates may be influenced by both upper airway collapsibility and ventilatory control. Conclusions: According to PUP physiologic estimates, decreases in AHI following several months of CPAP therapy are primarily attributable to improved upper airway collapsibility.

Exercise oscillatory ventilation as a predictor of mortality in patients with amyloid cardiomyopathy

Abstract Background Exercise oscillatory ventilation (EOV) signals multiple abnormalities in hemodynamics and ventilatory control which may predict poor outcome in patients with cardiac diseases. Purpose To assess whether EOV (+) patients with amyloid cardiomyopathy show higher mortality rates compared to their counterparts without EOV. Methods We analyzed cardiopulmonary exercise testing results for EOV defined by continuous (>60% of exercise duration) oscillations in minute ventilation (VE), cycling with a frequency of one minute and an amplitude above 15% of resting VE, in patients with amyloid cardiomyopathy. Results 156 patients with amyloid cardiomyopathy, aged 78 (±7) years, 87% male with a NYHA functional class of II (49.4%) and median B-type natriuretic peptide levels of 2548 (1374-4703) ng/L. Comorbidities include hypertension (73.1%), polyneuropathy (45.5%), coronary heart disease (41.0%), diabetes mellitus (17.3%) and chronic obstructive pulmonary disease (10.9%). Patients had a mean peak oxygen consumption of 14.8 (±4.42) ml/kg·min and a VE to carbon dioxide production (VCO2) slope ≥ 40 in 67.9% of cases. EOV was seen in 38 (24.4%), clearly absent in 92 (59.0%) and not clearly differentiable in 26 (16.7%) patients. 33 (21.2%) had a HF-related hospitalization and 31 (19.9%) died. EOV shows a hazard ratio of 2.35 (CI 1.088 – 5.075, p-value <0.05) for all-cause mortality. In fact, the log-rank test showed a difference in survival time between patients with and without EOV (p=0.03) with a median survival time of 46 months. Conclusion EOV may improve the prognostic yield of incremental CPET in patients with amyloid cardiomyopathy.Kaplan-Meier Curve for EOV

Combustion efficiency analysis and thermal radiation risk quantification for spill fire in sealed ship cabin

In this study, the authors conducted a series of one-dimensional spill fire experiments in a sealed model-scale ship cabin with different leakage rates to reveal the combustion characteristics. The results showed that a stable combustion stage with a consistent combustion area and flame height appears around the cut-off of the fuel, where the consumption of oxygen increases when the leakage rate increases. And the CO2 concentration increases more rapidly and the upward trend is found to appear earlier. Compared with the film thickness in the open space, that of the spill fire in the sealed ship cabin is slightly thinner because of the ventilation control effect. Using the “oxygen-consumption” method, the η of this stage was calculated and verified with the “mass loss rate” method. With these two methods, the HRR of spill fie when burning steadily is calculated. This work also improved the predicting model for the flame height of rectangular pool fire and developed a predicting model for the flame height of a steadily burning spill fire. The error is 14.35 %. Additionally, a predictive model of thermal radiation risk in ship spill fire is developed, and the death risk and unacceptable risk areas are delimited.

ESTRATÉGIAS DE CONFORTO AMBIENTAL PARA QUADRAS FECHADAS VOLTADAS A ESPORTES DE ALTO RENDIMENTO

Sports architecture, with its particularities, is essential to optimize the efficiency of environments, considering the specific needs of each sport. This article addresses environmental comfort strategies applied to indoor courts intended for high-performance sports, highlighting the importance of an architecturally planned environment to ensure the well-being of athletes and optimize their performance. The study focuses on thermal, visual, acoustic and ergonomic aspects, and analyzes how each of these factors influences sports performance. In addition, it explores the interaction between the body and the physical environment, proposing solutions that favor the creation of functional and comfortable spaces for sports practice and competitive events. The research, of a bibliographic and exploratory nature, brings together concepts of environmental psychology and athletic performance, in addition to highlighting the construction strategies of ventilation, lighting and acoustic control that can be applied to indoor courts with the aim of promoting human well-being. The work proposes solutions so that sports centers offer adequate conditions for sports practice, supporting the creation of environments that favor both the satisfaction and the enhancement of the performance of athletes. From this study, it is possible to define the importance of the preparation and competition environment in the performance of high-performance athletes, and architecture, linked to environmental comfort, is shown to be a tool for the development of these spaces, essential for success in national and international sports competitions.

Exploring the use phase social impacts of smart heating, ventilation and air conditioning control systems: A scoping review

Exploring the use phase social impacts of smart heating, ventilation and air conditioning control systems: A scoping review

Comparison of Awake Respiratory Control Versus Sleep Obstructive Sleep Apnea Endotypes.

Most approaches to advance simplified physiology-based precision medicine strategies for obstructive sleep apnea (OSA) focus on sleep parameters (i.e., OSA endotypes). However, wakefulness physiology measures can also provide prediction insight for certain OSA therapies yet their relationship with sleep parameters has not been extensively investigated. This study aimed to investigate potential relationships between awake ventilatory control parameters and sleep OSA endotypes and their potential to predict changes in OSA severity with morphine. Data were acquired from a randomised, cross-over trial that investigated effects of morphine versus placebo on OSA severity and underlying mechanisms. Here, awake ventilatory chemoreflex testing prior to overnight polysomnography was compared with direct measures of sleep respiratory control (e.g., hypercapnic ventilatory responses and loop gain) and OSA endotypes during a separate overnight physiology study (pharyngeal critical closure pressure-Pcrit, muscle responsiveness via genioglossus intramuscular electromyography and arousal threshold via epiglottic pressure catheter to transient continuous positive airway pressure reductions). Twenty-one men with OSA completed both study arms. During placebo, 1) awake chemosensitivity correlated with Pcrit (r=0.726, p=0.001), 2) arousal threshold correlated with awake CO2 ventilatory response threshold (r=-0.467, p=0.047) and basal ventilation (r=-0.500, p=0.029). Awake chemosensitivity and Pcrit also correlated with the apnea-hypopnea index (p<0.001) during placebo. Awake chemosensitivity was predictive of changes in OSA severity with morphine(r=-0.535, p=0.013). In conclusion, awake measures of respiratory control are related to physiological endotypes such as airway collapsibility and arousal threshold during sleep and OSA severity. Awake ventilatory chemosensitivity has the best potential to predict changes in OSA severity with morphine.

Angiotensin-converting enzyme 2 activation attenuates inflammation and oxidative stress in brain death donor followed by rat lung transplantation

Brain death (BD) provides most of the donor organs destined for lung transplantation (LTx). However, the organs may be affected by inflammatory and oxidative processes. Based on this, we hypothesize that the angiotensin-converting enzyme 2 (ACE2) activation can reduce the lung injury associated with LTx. 3 h after BD induction, rats were injected with saline (BD group) or an ACE2 activator (ACE2a group; 15 mg/kg-1) and kept on mechanical ventilation for additional 3 h. A third group included a control ventilation (Control group) prior to transplant. After BD protocol, left LTx were performed, followed by 2 h-reperfusion. ACE2 activation was associated with better oxygenation after BD management (p = 0.01), attenuating edema (p = 0.05) followed by the reduction in tissue resistance (p = 0.01) and increase of respiratory compliance (p = 0.02). Nrf2 expression was also upregulated in the ACE2a group (p = 0.03). After transplantation, ACE2a group showed lower levels of TNF-α (p = 0.02), IL-6 (p = 0.001), IL-1β (p = 0.01), ROS (p = 0.004) and MDA (p = 0.002), in addition to higher CAT activity (p = 0.04). In conclusion, our study suggests that ACE2 activation improves anti-inflammatory and antioxidant activity in a model of LTx.

Mesh Refinement for Dynamics Airflow in Health Care

This studyexplores using computational fluid dynamics(CFD) tooptimize ventilation in healthcare, with a focus on mesh refinement for accurate airflow analysis. The goal is to reduce airborne contaminant spread, which is crucial during COVID-19. Ventilation in healthcare ensures air quality and minimizes pathogen transmission. The research analyzer analyzes mesh element size's impact on airflow accuracy in simulated hospital wards with two coughing patients. The optimal mesh size is determined for reliable predictions. Mesh refinement enhances accuracy in critical zones. The k-epsilon turbulence model is chosen for low error. Experimental validation shows temperature discrepancies, likely due to simulated manikins lacking clothing insulation. Air velocity measurements show minor variations within an acceptable range. Further research on various ventilation configurations and airflow rates in real hospital conditions is crucial. Addressing these limitations optimizer optimizeshealthcare ventilation, enhancing safety and infection control.

Modulation of gill surface area does not correlate with oxygen loss in Chitala ornata.

Air-breathing fish risk losing aerially sourced oxygen to ambient hypoxic water since oxygenated blood from the air-breathing organ returns through the heart to the branchial basket before distribution. This loss is thought to help drive the evolutionary reduction in gill size with the advent of air-breathing. In many teleost fish, gill size is known to be highly plastic by modulation of their anatomic diffusion factor (ADF) with inter-lamellar cell mass (ILCM). In the anoxia-tolerant crucian carp, ILCM recedes with hypoxia but regrows in anoxia. The air-breathing teleost Chitala ornata has been shown to increase gill ADF from normoxic to mildly hypoxic water by reducing ILCM. Here, we test the hypothesis that ADF is modulated to minimize oxygen loss in severe aquatic hypoxia by measuring ADF, gas-exchange, and by using computed tomography scans to reveal possible trans-branchial shunt vessels. Contrary to our hypothesis, ADF does not modulate to prevent oxygen loss and despite no evident trans-branchial shunting, C. ornata loses only 3% of its aerially sourced O2 while still excreting 79% of its CO2 production to the severely hypoxic water. We propose this is achieved by ventilatory control and by compensating the minor oxygen loss by extra aerial O2 uptake.

Heating, cooling and energy management of cold climate educational built environments using green roofs

Educational spaces in the world with a student population of about 600 million people are significant energy consumers. Also, 66 percent of schools are among energy consumers in the lower categories of energy consumption management. Meanwhile, the world's student population is expanding while nonrenewable energy sources are running out. Recent investigations highlight schools’ focus on energy efficiency in construction, which is driven by escalating energy expenses and depleting resources. This study presents a model of green schools featuring green roof coverings interconnected with controllable window atriums, leveraging green architecture as a renewable energy solution. It assesses the impact of green roofs coupled with atriums on summer ventilation and winter heat control in cold climate regions, contrasting with conventional school designs. This study aims to improve the thermal performance of schools, reduce heat losses from the roof and walls of schools, and also help improve natural ventilation in classrooms. The research followed a descriptive-analytical approach and employed simulation techniques. The model is simulated using the Carrier HAP, and its analyses are validated by the U.S. Green Building Council's LEED certification. Then, a comparison is made between the thermal loads of the proposed model and the thermal loads of the conventional school model in this climate. The results demonstrate the advantage of using green roofs in improving thermal loads in winter and cooling loads in summer in classroom models with green roofs connected to atriums compared to classrooms with conventional 40 cm thick roofs or 66 cm thick roofs without greenery. Energy savings are achieved by creating schools with green roofs associated with atriums while maintaining students' connection with nature and utilizing this cover as an auxiliary learning space. Energy savings are 54.95 % in winter and 76.11 % in summer compared to conventional 40 cm thick roofs and 66 cm thick roofs without greenery.

In-tunnel pollutant concentration measurement and ventilation control indexes for highway tunnels in mountainous area: A case study of no.1 Qinling tunnel, China

The pollutant concentration and hygiene standards in mountain tunnels are key factors determining the ventilation effectiveness of tunnel operation. Previous studies have mainly focused on the spatiotemporal distribution patterns of in-tunnel pollutants, while research on ventilation control indexes for pollutants in long mountain tunnels are relatively limited. Therefore, this paper aims to discussing the hygiene control indexes for operational ventilation of mountain tunnels. Field tests were conducted in the No.1 Qinling Tunnel of Xi'an to Han zhong Highway in China, and the distribution laws of traffic flow, wind speed, and concentrations of particulate matter (PM), carbon monoxide (CO), and nitrogen oxide (NOX) were on-site measured. The spatiotemporal distribution characteristics of pollutants in the long mountain tunnel were revealed, and the ventilation hygiene control indexes for harmful gases were calculated and compared to those of urban highway tunnels. The results show that the traffic volume of operational highway tunnels in mountainous areas of China is increasing rapidly, and the particulate matter emitted from diesel vehicle exhaust will pose a threat to the normal environmental quality in the tunnel. The average value of CO base emission factor in 2000 for the upline of No.1 Qinling tunnel was 0.0019 m3/(veh·km) by back-calculation, and the annual decline rate of CO in the ventilation design for mountain highway tunnels should be 6 %–8 % and revised every 8 years. It is recommended to consider the geographical location and traffic conditions of mountain tunnels when calculating the air demand required for diluting NO2. NOx has basically replaced CO as the primary pollutant with the most significant impact on human health in mountain tunnels. It is recommended that NOX be considered as the primary hygiene control indicator in the ventilation design of mountain tunnels in China. The pollutant distribution and ventilation control indexes of mountain and urban highway tunnels are quite different, which should be treated differently in engineering applications. The research results can provide a significant reference for ventilation design and environmental evaluation of highway tunnels in mountainous areas.

Development and evaluation of a buoyancy-driven airflow window with multioperation modes

This study introduces a novel, cost-effective window-integrated passive system (WIPS) that enhances indoor air quality and maintains comfortable temperatures throughout both summer and winter. The WIPS utilizes buoyancy-driven air flow through dual air cavities that alternately preheat or cool the incoming air depending on the season. The system’s design was optimized for Seoul, Korea, through computational fluid dynamics (CFD) simulations using Fluent 2021 R2 software, which employed a Reynolds-Averaged Navier-Stokes (RANS) model with RNG κ-ε turbulence modeling. Key design adjustments included the use of glass and unplasticized polyvinyl chloride (UPVC) to minimize unintended airflow, as well as modifications to the geometry and size of the air cavities to maximize efficient air flow and thermal effectiveness. The optimized design demonstrated significant improvements in thermal performance, achieving a preheating effect up to 35 °C in winter and reducing indoor temperatures by 10 °C in summer. These results underline the potential of WIPS to provide a sustainable solution for building ventilation and climate control.

AI-driven ventilation control policy proximal optimization coupled with dynamic-informed real-time model calibration for healthy and sustainable indoor PM2.5 management

Indoor air quality (IAQ) is an important factor for determining quality of life and urban sustainability. In underground subway stations, improving IAQ through ventilation systems remains challenging due to the complexity and nonstationary nature of IAQ resulting from diverse influential factors such as subway schedules, passenger volume, and outdoor air quality (OAQ). Therefore, this study aimed to develop a novel artificial intelligence (AI)-driven ventilation system for healthy and sustainable IAQ management in subway stations. First, an IAQ mechanistic model coupled with genetic algorithm (GA)-driven rolling-horizon calibration was developed from the collected IAQ big dataset, and global sensitivity analysis was then employed to identify the dominant variables in IAQ dynamics. Subsequently, proximal policy optimization (PPO), one of the reinforcement learning (RL) algorithms, was employed to control the ventilation system in both the lobby and platform areas of a subway station. The results demonstrated that the IAQ mechanistic model can capture IAQ dynamics with acceptable modeling performance, achieving around 19 % of mean absolute percentage error (MAPE). Furthermore, the PPO-driven ventilation control system can reduce energy consumption by around 22 % while maintaining IAQ at an acceptable level.

Towards various occupants with different thermal comfort requirements: A deep reinforcement learning approach combined with a dynamic PMV model for HVAC control in buildings

Reinforcement learning (RL) has great potential in achieving energy-efficient, comfortable and intelligent control of heating, ventilation and air conditioning (HVAC) systems. Although research on RL-based HVAC control has attracted increasing interest, current studies generally use simple building simulation as the environment to train agents, and the definition of thermal comfort is limited to a wide temperature range, which cannot meet the different thermal comfort requirements of various occupants. This study proposes a deep reinforcement learning (DRL) control framework based on the Dueling Deep Q-network (DQN) algorithm, combined with a self-designed environmental model and reward function, for HVAC control meeting different thermal comfort requirements. Specifically, based on the theory of building thermal dynamics, a nonlinear equation modified by experimental data is used for the environmental model that reflects the actual thermal change of building. Different thermal comfort requirements are considered and analysed through a dynamic predicted mean vote (PMV) model that focuses on the metabolic rate and clothing level of occupants. By systematically exploring different heating modes for occupants and control time intervals, the proposed framework demonstrates that heating energy consumption can be reduced by 4.8%-39.58% under various conditions compared to rule-based control. In addition, the study found that the HVAC control based on DRL has greater potential in saving energy when the heating demand of building is higher. Our study is helpful for researchers to make HVAC control more energy-efficient and user-friendly with the help of artificial intelligence.

Mitigating COVID-19 in meat processing plants: what have we learned from cluster investigations?

Several COVID-19 outbreaks have been reported in meat processing plants in different countries. The aim of this study was to assess the environmental and socio-economic risk factors favouring the transmission of SARS-CoV-2 in meat processing plants and to describe the prevention measures implemented. Data from epidemiological investigations of COVID-19 clusters in France, the scientific literature, structured interviews and site visits were collected and summarised to investigate the main risk factors for SARS-CoV-2 infection in meat processing plants, including determinants within and outside the workplace. An increased risk of infection was identified among workers with unfavourable socio-economic status (temporary/non-permanent workers, migrants, ethnic minorities, etc.), possibly related to community activities (house-sharing, car-sharing, social activities). Working conditions (proximity between workers) and environmental factors (low temperatures and inadequate ventilation) also appear to be important risk factors. These environmental conditions are particularly prevalent in cutting and boning plants, where the majority of reported cases are concentrated. Preventive measures applied included screening for COVID-19 symptoms, testing, wearing masks, increased hygiene and sanitation, physical and temporal distancing, control of ventilation. Certain food safety hygiene measures were compatible with protecting workers from SARS-CoV-2. The hygiene culture of agri-food workers made it easier to implement preventive measures after adaptation. This study made it possible to identify the environmental and socio-economic factors conducive to the transmission of SARS-CoV-2 in meat processing plants. The knowledge gained from this work was used in simulations to understand the transmission of the virus in the plants.

Multi-sensor fault detection and correction for automated IAQ monitoring in smart buildings through attention-aware autoencoders with spatial prediction module

The integration of interconnected monitoring and control devices has significantly enhanced the management of indoor building environments, notably in underground subway stations. In these sophisticated settings, the accuracy of monitoring systems is critical, as data-driven control systems rely extensively on sensor feedback. However, sensor faults arising from hardware damage, wear, and cyberattacks compromise the reliability of these complex systems. Despite extensive efforts, existing methods still face challenges in accurately detecting and reconstructing faults, particularly when dealing with multiple sensor failures. This study introduces a hybrid sensor validation framework that combines a multi-head attention-based denoising autoencoder (AE) with a deep learning prediction module. The performance assessment of the proposed gated recurrent unit (GRU) integrated attention-based fault detection, and reconstruction network (GADRN) shows a significant 33 % and 21 % improvement in critical success index compared to Independent Component Analysis and GRU-AE frameworks. GADRN consistently identified faulty sensors across various fault periods, achieving the lowest false alarm rate of 11 % against several conventional and machine learning-based models. A ventilation control analysis highlights the significance of incorporating a sensor validation framework in modern buildings. The outputs by the GADRN led to a 22.5 % decrease in energy consumption by preventing unnecessary resource use attributed to sensor faults.

Reliability and Safety Testing of a Ventilator Remote Control System Against Communication Failures and Network Disruptions.

The need for remote ventilator control has been highlighted by the COVID-19 Public Health Emergency. Remote ventilator control from outside a patient's room can improve response time to patient needs, protect health care workers, and reduce personal protective equipment (PPE) consumption. Extending remote control to distant locations can expand the capabilities of frontline health care workers by delivering specialized clinical expertise to the point of care, which is much needed in diverse health care settings, such as tele-critical care and military medicine. However, the safety and effectiveness of remote ventilator control can be affected by many risk factors, including communication failures and network disruptions. Consensus safety requirements and test methods are needed to assess the resilience and safety of remote ventilator control under communication failures and network disruptions. We designed two test methods to assess the robustness, usability, and safety of a remote ventilator control prototype system jointly developed by Nihon Kohden OrangeMed, Inc. and DocBox, Inc. ("the NK-DocBox system") to control the operation of an NKV-550 critical care ventilator under communication failures and network disruptions. First, the robustness of the NKV-550 ventilator was tested using a remote-control application developed on OpenICE - an open-source medical device interoperability platform - to transmit customized high-frequency and erroneous remote-control commands that could be caused by communication failures in a real-world environment. The second method utilized a network emulator to create different types and severity of network quality of service (QoS) degradation, including bandwidth throttling, network delay and jitter, packet drop and reordering, and bit errors, in the NKV-DocBox system to quantitatively assess the impact on system usability and safety. The NKV-550 ventilator operated as expected when remote-control commands arrived as fast as once per second. It ignored erroneous commands attempting to adjust invalid ventilation parameters. When facing commands that set the ventilation mode and parameters to invalid values, it reset the ventilation mode or parameters to default values, the safety implication of which may merit further evaluation. When any network QoS attribute (except for packet reordering) started to degrade, the NK-DocBox System experienced interference to its remote-control function, such as delays in the transmission of ventilator data and remote-control commands within the system. When the network QoS was worse than 500 ms network delay, 100 ms network jitter, 1% data drop rate, 12Mbps minimal bandwidth, or 1e-6 bit error rate, the system became unsafe to use. For example, ventilator waveforms visualized on the remote-control application demonstrated freezes, out-of-synchronization, and moving backwards; and the connection between the ventilator and the remote-control application became unstable. The presented test methods confirmed the robustness of the NKV-550 ventilator against high-frequency and erroneous remote control, quantified the impact of network disruptions on the usability, reliability, and safety of the NK-DocBox system and identified the minimum network QoS requirements for it to function safely. These generalizable test methods can be customized to evaluate other remote ventilator control technologies and remote control of other types of medical devices against communication failures and network disruptions.

Pressure Control Ventilation Versus Volume Control Ventilation in Laparoscopic Surgery: A Narrative Review.

This review compares the safety and effectiveness of volume control ventilation (VCV) and pressure control ventilation (PCV) during laparoscopic surgery. Nine studies were chosen for in-depth examination following the application of stringent inclusion and exclusion criteria to the 184 publications that the literature search turned up. PCV is well-known for its capacity to preserve lower peak airway pressures during laparoscopic procedures, lowering the risk of volutrauma and barotrauma and enhancing oxygenation under these conditions of elevated intra-abdominal pressures. On the other hand, VCV guarantees a constant tidal volume and offers accurate ventilation management, both of which are essential for preserving stable carbon dioxide levels. VCV, however, may result in higher peak airway pressures, raising the risk of lung damage brought on by a ventilator. Research indicates that PCV provides better respiratory mechanics management during laparoscopic surgery, but VCV consistent tidal volume delivery is useful in some clinical situations. When choosing between PCV and VCV, the anesthesia team's experience, the demands of each patient, and the surgical circumstances should all be taken into consideration. Real-time monitoring tools and sophisticated ventilatory technology are essential for maximizing ventilation techniques. Further improving patient outcomes can be achieved by incorporating multimodal anesthesia approaches, such as the use of muscle relaxants and customized intraoperative fluid management. Muscle relaxants optimize conditions for mechanical ventilation by ensuring adequate muscle relaxation, reducing the risk of ventilator-associated lung injury, and enabling more precise control of ventilation parameters. Tailored intraoperative fluid management helps maintain optimal lung mechanics by avoiding fluid overload, which can lead to pulmonary edema and compromised gas exchange, necessitating adjustments in ventilation strategy. While both ventilation modalities can be utilized efficiently, the research suggests that PCV may be more advantageous in controlling oxygenation and airway pressures. In the dynamic and demanding world of laparoscopic surgery, ongoing research and clinical innovation are crucial to improving these tactics and guaranteeing the best possible treatment. In order to obtain the best possible patient outcomes during laparoscopic surgeries, this review emphasizes the significance of customized breathing techniques.

Comparing mechanical ventilation control strategies for indoor air quality: Monitoring and simulation results of a school building in northern Italy

Demand-controlled ventilation systems and carbon dioxide monitoring are critical to ensure indoor air quality (IAQ) comfort conditions. Their importance has become significantly more evident since the COVID-19 pandemic crisis. Control strategies for mechanical ventilation based on CO2 concentration are commonly used, although they are rarely applied in Italian and other Southern European country schools. Moreover, optimised ventilation systems can help maintain indoor air quality while reducing energy consumption for heating (i.e. exploiting heat recovery). The proposed paper aims to compare different remote-control strategies to manage three detached mechanical ventilation units (DMV) installed in a junior-high-school demo building in Torre Pellice (Northwest Italy), monitored with room detail since April 2021. Eight IAQ control logics are analysed considering both in situ tests, handling DMV accordingly to room sensors, and simulation tests using the Energy Management System of EnergyPlus in the neutral season. All different solutions are compared by considering air quality, temperature, and energy need indicators. Additionally, a yearly energy verification via hourly simplified analyses is performed by considering fan consumption and energy conservation. Threshold-based control logics result to be more effective than time-dependent ones, while all cases guarantee very high IAQ levels, although energy savings are limited. The choice of the correct control threshold(s) is site-dependent being influenced by user local habits.

Comprehensive analysis of classroom microclimate in context to health-related national and international indoor air quality standards.

Indoor air quality (IAQ) and indoor air pollution are critical issues impacting urban environments, significantly affecting the quality of life. Nowadays, poor IAQ is linked to respiratory and cardiovascular diseases, allergic reactions, and cognitive impairments, particularly in settings like classrooms. Thus, this study investigates the impact of indoor environmental quality on student health in a university classroom over a year, using various sensors to measure 19 environmental parameters, including temperature, relative humidity, CO2, CO, volatile organic compounds (VOCs), particulate matter (PM), and other pollutants. Thus, the aim of the study is to analyze the implications of the indoor microclimate for the health of individuals working in the classroom, as well as its implications for educational outcomes. The data revealed frequent exceedances of international standards for formaldehyde (HCHO), VOC, PM2.5, NO, and NO2. HCHO and VOCs levels, often originating from building materials and classroom activities, were notably high. PM2.5 levels exceeded both annual and daily standards, while NO and NO2 levels, possibly influenced by inadequate ventilation, also surpassed recommended limits. Even though there were numerous exceedances of current international standards, the indoor microclimate quality index (IMQI) score indicated a generally good indoor environment, remaining mostly between 0 and 50 for this indicator. Additionally, analyses indicate a high probability that some indicators will exceed the current standards, and their values are expected to trend upwards in the future. The study highlighted the need for better ventilation and pollutant control in classrooms to ensure a healthy learning environment. Frequent exceedances of pollutant standards can suggest a significant impact on student health and academic performance. Thus, the present study underscored the importance of continuous monitoring and proactive measures to maintain optimal indoor air quality.