IntroductionMechanical power of ventilation, a summary parameter reflecting the energy transferred from the ventilator to the respiratory system, has associations with outcomes. INTELLiVENT–Adaptive Support Ventilation is an automated ventilation mode that changes ventilator settings according to algorithms that target a low work–and force of breathing. The study aims to compare mechanical power between automated ventilation by means of INTELLiVENT–Adaptive Support Ventilation and conventional ventilation in critically ill patients.Materials and methodsInternational, multicenter, randomized crossover clinical trial in patients that were expected to need invasive ventilation > 24 hours. Patients were randomly assigned to start with a 3–hour period of automated ventilation or conventional ventilation after which the alternate ventilation mode was selected. The primary outcome was mechanical power in passive and active patients; secondary outcomes included key ventilator settings and ventilatory parameters that affect mechanical power.ResultsA total of 96 patients were randomized. Median mechanical power was not different between automated and conventional ventilation (15.8 [11.5–21.0] versus 16.1 [10.9–22.6] J/min; mean difference –0.44 (95%–CI –1.17 to 0.29) J/min; P = 0.24). Subgroup analyses showed that mechanical power was lower with automated ventilation in passive patients, 16.9 [12.5–22.1] versus 19.0 [14.1–25.0] J/min; mean difference –1.76 (95%–CI –2.47 to –10.34J/min; P < 0.01), and not in active patients (14.6 [11.0–20.3] vs 14.1 [10.1–21.3] J/min; mean difference 0.81 (95%–CI –2.13 to 0.49) J/min; P = 0.23).ConclusionsIn this cohort of unselected critically ill invasively ventilated patients, automated ventilation by means of INTELLiVENT–Adaptive Support Ventilation did not reduce mechanical power. A reduction in mechanical power was only seen in passive patients.Study registrationClinicaltrials.gov (study identifier NCT04827927), April 1, 2021URL of trial registry recordhttps://clinicaltrials.gov/study/NCT04827927?term=intellipower&rank=1
MULTIFILE
The primary aims of this study were (1) to evaluate whole-body mechanical efficiency (ME) in a large group of chronic obstructive pulmonary disease (COPD) patients with a wide range of degrees of illness and (2) to examine how ME in COPD is related to absolute work rate and indices of disease severity during exercise testing. A total of 569 patients (301 male patients; GOLD stage I: 28, GOLD stage II: 166, GOLD stage III: 265, and GOLD stage IV: 110) with chronic obstructive pulmonary disease (COPD) were included in the data analysis. Individual maximal workload (watt), peak minute ventilation ((Equation is included in full-text article.)E, L/min body temperature and pressure, saturated), and peak oxygen uptake ((Equation is included in full-text article.)O2, mL/min standard temperature and pressure, dry) were determined from a maximal incremental cycle ergometer test. Ventilatory and metabolic response parameters were collected during a constant work rate test at 75% of the individual maximal workload. From the exercise responses of the constant work rate test, the gross ME was calculated. The mean whole-body gross ME was 11.0 ± 3.5% at 75% peak power. The ME declined significantly (P < .001) with increasing severity of the disease when measured at the same relative power. Log-transformed absolute work rate (r = .87, P < .001) was the strongest independent predictor of gross ME. Body mass was the single other variable that contributed significantly to the linear regression model. Gross ME in COPD was largely predicted by the absolute work rate (r = .87; P < .001) while indices of the severity of the disease did not predict ME in COPD.
BACKGROUND The mechanical power of ventilation (MP) has an association with outcome in invasively ventilated patients with the acute respiratory distress syndrome (ARDS). Whether a similar association exists in invasively ventilated patients without ARDS is less certain.OBJECTIVE To investigate the association of mechanical power with mortality in ICU patients without ARDS.DESIGN This was an individual patient data analysis that uses the data of three multicentre randomised trials.SETTING This study was performed in academic and nonacademic ICUs in the Netherlands.PATIENTS One thousand nine hundred and sixty-two invasively ventilated patients without ARDS were included in this analysis. The median [IQR] age was 67 [57 to 75] years, 706 (36%) were women.MAIN OUTCOME MEASURES The primary outcome was the all-cause mortality at day 28. Secondary outcomes were the all-cause mortality at day 90, and length of stay in ICU and hospital.RESULTS At day 28, 644 patients (33%) had died. Hazard ratios for mortality at day 28 were higher with an increasing MP, even when stratified for its individual components (driving pressure (P < 0.001), tidal volume (P < 0.001), respiratory rate (P < 0.001) and maximum airway pressure (P = 0.001). Similar associations of mechanical power (MP) were found with mortality at day 90, lengths of stay in ICU and hospital. Hazard ratios for mortality at day 28 were not significantly different if patients were stratified for MP, with increasing levels of each individual component.CONCLUSION In ICU patients receiving invasive ventilation for reasons other than ARDS, MP had an independent association with mortality. This finding suggests that MP holds an added predictive value over its individual components, making MP an attractive measure to monitor and possibly target in these patients.TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02159196, ClinicalTrials.gov Identifier: NCT02153294, ClinicalTrials.gov Identifier: NCT03167580.
Currently, many novel innovative materials and manufacturing methods are developed in order to help businesses for improving their performance, developing new products, and also implement more sustainability into their current processes. For this purpose, additive manufacturing (AM) technology has been very successful in the fabrication of complex shape products, that cannot be manufactured by conventional approaches, and also using novel high-performance materials with more sustainable aspects. The application of bioplastics and biopolymers is growing fast in the 3D printing industry. Since they are good alternatives to petrochemical products that have negative impacts on environments, therefore, many research studies have been exploring and developing new biopolymers and 3D printing techniques for the fabrication of fully biobased products. In particular, 3D printing of smart biopolymers has attracted much attention due to the specific functionalities of the fabricated products. They have a unique ability to recover their original shape from a significant plastic deformation when a particular stimulus, like temperature, is applied. Therefore, the application of smart biopolymers in the 3D printing process gives an additional dimension (time) to this technology, called four-dimensional (4D) printing, and it highlights the promise for further development of 4D printing in the design and fabrication of smart structures and products. This performance in combination with specific complex designs, such as sandwich structures, allows the production of for example impact-resistant, stress-absorber panels, lightweight products for sporting goods, automotive, or many other applications. In this study, an experimental approach will be applied to fabricate a suitable biopolymer with a shape memory behavior and also investigate the impact of design and operational parameters on the functionality of 4D printed sandwich structures, especially, stress absorption rate and shape recovery behavior.
Most trailer units currently lack a drivetrain to assist the truck. Given that trailers carry heavy loads, significant power is required to propel them especially during acceleration. Equipping trailers with an electric powertrain can help support the truck during traction and regenerate energy during braking, reducing emissions and energy consumption. This also is in line with the CO2 emissions goal of the European Green Deal for heavy duty vehicles . By utilizing a king-pin, the e-trailer can determine the level of support needed, making it fully independent. Currently, a measurement system for measuring forces on the king-pin is being developed. Using the information of forces, the support by the e-trailer during traction can be defined along with the recaptured energy during braking. Although, it will work in longitudinal direction but has its limitations when a lateral force is acting; like during cornering, road camber, turning etc. In reality, vehicles do not only drive in straight line, so dynamics of the vehicle have a significance role. If the support provided by e-trailer during lateral movement of the vehicle is not controlled, it may have an effect on the general ride and handling outside the longitudinal scenarios and in extreme cases lead to dangerous situations (like jack-knifing). Thus, for refine and safe drivability of the truck-e-trailer combination additional information on the manoeuvring status of the truck e-trailer combination is required. This added information will improvise controllability of the e-trailer and provide a safety net over the already defined e-trailer controls in a longitudinal direction, making basic movements involving lateral direction possible. It is proposed to integrate the articulation angle information, such that the complex electro-mechanical system of the e-tailer driveline can recognize the dynamic movements to help refine the control and ensure safety. Validation will be performed in Hardware-in-loop environment.
The demand for mobile agents in industrial environments to perform various tasks is growing tremendously in recent years. However, changing environments, security considerations and robustness against failure are major persistent challenges autonomous agents have to face when operating alongside other mobile agents. Currently, such problems remain largely unsolved. Collaborative multi-platform Cyber- Physical-Systems (CPSs) in which different agents flexibly contribute with their relative equipment and capabilities forming a symbiotic network solving multiple objectives simultaneously are highly desirable. Our proposed SMART-AGENTS platform will enable flexibility and modularity providing multi-objective solutions, demonstrated in two industrial domains: logistics (cycle-counting in warehouses) and agriculture (pest and disease identification in greenhouses). Aerial vehicles are limited in their computational power due to weight limitations but offer large mobility to provide access to otherwise unreachable places and an “eagle eye” to inform about terrain, obstacles by taking pictures and videos. Specialized autonomous agents carrying optical sensors will enable disease classification and product recognition improving green- and warehouse productivity. Newly developed micro-electromechanical systems (MEMS) sensor arrays will create 3D flow-based images of surroundings even in dark and hazy conditions contributing to the multi-sensor system, including cameras, wireless signatures and magnetic field information shared among the symbiotic fleet. Integration of mobile systems, such as smart phones, which are not explicitly controlled, will provide valuable information about human as well as equipment movement in the environment by generating data from relative positioning sensors, such as wireless and magnetic signatures. Newly developed algorithms will enable robust autonomous navigation and control of the fleet in dynamic environments incorporating the multi-sensor data generated by the variety of mobile actors. The proposed SMART-AGENTS platform will use real-time 5G communication and edge computing providing new organizational structures to cope with scalability and integration of multiple devices/agents. It will enable a symbiosis of the complementary CPSs using a combination of equipment yielding efficiency and versatility of operation.
