Experimental study of the effect of various collision angles and critical conditions on marine engine’s twin-spray collision process

To enhance the fuel-gas mixing and phase transition process, the fuel is injected by twin injectors in a large-bore low-speed two-stroke marine engine, while the cylinder condition has reached the transcritical and supercritical conditions. The twin-injector configuration has a great potential for further optimization, but the exploration on the outcome of collision and phase transition was still limited. Therefore, this work aims to study the effect of various collision angles (60°, 90°, 120°, 150°) and critical conditions (sub/trans/supercritical) on the twin-spray collision process using optical techniques. A wide range of experimental cases are conducted to provide an analysis and database for future modeling validation. The post-collisional spray structures, spatial distribution, and periphery features are analyzed to characterize the droplet’s collision. The results show that with the collision angle increasing, the higher collision velocity enhances the mass transfer while the minor vertical component results in a smaller axial dispersion. Because of the trade-off relationship between the vertical velocity component and pre-collision penetration, a higher reduction in droplet momentum results in a slighter collision behavior. At the collision angle of 150°, the subcritical condition tends to result in an off-axis collision. Under the transcritical (P) condition, the probability of head-on collision increases and presents a wider spatial distribution. But under the supercritical condition, because of the existence of the liquid collision, the thermal conversion among phases is accelerated, while the ambient resistance is reduced. Moreover, an exponential correlation of collision liquid length is formulated to predict the axial dispersion based on various critical conditions.

Impact of osmotic pressure on the stability of Taylor vortices

We use linear stability analysis and direct numerical simulations to investigate the coupling between centrifugal instabilities, solute transport and osmotic pressure in a Taylor–Couette configuration that models rotating dynamic filtration devices. The geometry consists of a Taylor–Couette cell with a superimposed radial throughflow of solvent across two semi-permeable cylinders. Both cylinders totally reject the solute, inducing the build-up of a concentration boundary layer. The solute retroacts on the velocity field via the osmotic pressure associated with the concentration differences across the semi-permeable cylinders. Our results show that the presence of osmotic pressure strongly alters the dynamics of the centrifugal instabilities and substantially reduces the critical conditions above which Taylor vortices are observed. It is also found that this enhancement of the hydrodynamic instabilities eventually plateaus as the osmotic pressure is further increased. We propose a mechanism to explain how osmosis and instabilities cooperate and develop an analytical criterion to bound the parameter range for which osmosis fosters the hydrodynamic instabilities.

5-Hydroxymethylcytosine Signatures in Circulating Cell-Free DNA as Early Warning Biomarkers for COVID-19 Progression and Myocardial Injury

Background: The symptoms of coronavirus disease 2019 (COVID-19) range from moderate to critical conditions, leading to death in some patients, and the early warning indicators of the COVID-19 progression and the occurrence of its serious complications such as myocardial injury are limited.Methods: We carried out a multi-center, prospective cohort study in three hospitals in Wuhan. Genome-wide 5-hydroxymethylcytosine (5hmC) profiles in plasma cell-free DNA (cfDNA) was used to identify risk factors for COVID-19 pneumonia and develop a machine learning model using samples from 53 healthy volunteers, 66 patients with moderate COVID-19, 99 patients with severe COVID-19, and 38 patients with critical COVID-19.Results: Our warning model demonstrated that an area under the curve (AUC) for 5hmC warning moderate patients developed into severe status was 0.81 (95% CI 0.77–0.85) and for severe patients developed into critical status was 0.92 (95% CI 0.89–0.96). We further built a warning model on patients with and without myocardial injury with the AUC of 0.89 (95% CI 0.84–0.95).Conclusion: This is the first study showing the utility of 5hmC as an accurate early warning marker for disease progression and myocardial injury in patients with COVID-19. Our results show that phosphodiesterase 4D and ten-eleven translocation 2 may be important markers in the progression of COVID-19 disease.

The Piston Effect inside a microchannel with Carbon Dioxide near Critical conditions

Heat transfer near the critical condition of Carbon Dioxide due to thermo-acoustic waves in a 100-µm high microchannel was numerically studied. The temperature at a point farthest away from the heated surface was compared between computational fluid dynamics (CFD) models and a pure conduction model. The comparison revealed that the CFD model predicted a temperature increase furthest from the surface much faster than the time constant required for such increase purely by conduction. It is believed that another heat transfer process, termed the piston effect (PE), which is associated with pressure waves in the fluid, was responsible for this increase. Explicit unsteady methodology in the fluid model indicated that propagation of pressure waves due to a rapid expansion of the boundary layer and the associate change in the fluid density distribution resulted in this temperature raise. It was confirmed that natural convection wasn’t responsible for the temperature increase under quiescent conditions. In addition, it was discovered that the PE is significant for certain forced convection conditions.

Maternal and Fetal Outcome of Pregnancy with COVID- 19 in A Tertiary Level Hospital, Bangladesh

Introduction: Pregnant women have long been recognized as a vulnerable population during infectious disease pandemics due to physiological changes in the immune, pulmonary, cardiac and coagulation systems. It is essential to acquire knowledge of pregnancy outcomes, potential complications and neonatal health conditions born to an infected mother with COVID-19. Material and methods: This cross-sectional observational study was conducted in Combined Military Hospital (CMH), Jashore from June 2020 to July 2021 among 100 hospitalized laboratory-confirmed COVID-19 positive pregnant women, patients who had clinical symptoms of COVID but RT PCR negative were excluded. The aim of the study was to evaluate the clinical profile and maternal and fetal outcome of pregnancy. Relevant data were recorded in a preformed data collection sheet and analyzed by SPSS version 20. Results: Among 100 COVID-19 positive hospitalized pregnant women, the mean age of participants was 27years (range 19-40 years), Maximum infection rate observed during 12 to 28 weeks of gestation among the participants, 21% got infected at 37 to 40 weeks of gestation and 20% got infected at 32 to 36 weeks. Seventy-four percent patients underwent delivery during the study & 23% of them continued with ongoing pregnancy; 67 of the participants underwent LUCS and 7 vaginal deliveries were done, 3% had abortion and IUFD 1% ,61% were multipara and 39% were Primipara, associated co-morbidities were subclinical hypothyroidism(15%), pregnancy induced HTN(12%) and GDM(8%); 36% participants were asymptomatic and 44% had mild symptoms, rate of LUCS was higher than (90.64%) vaginal delivery. Among the 73 live births, 80.82% were term and 10.18% were preterm of neonates, small for gestational was seen in the case of 20.55% neonates. Testing for SARS-CoV-2 was performed in all neonatal throat swabs and found positive in one case only. Eighty-six percent neonates were well-baby and 9.58% neonates required NICU admission. There were 2 neonatal deaths due to severe prematurity and 2 babies were found to have congenital cardiac anomaly and cleft lip, cleft palate. Though 36% of patients were asymptomatic but 10% were severe and in the critical stage. HDU support needed for 8% of patients and ICU support for 6%. Conclusion: This cross-sectional study supports that pregnant women with COVID-19 infection are at increased risk of adverse pregnancy and birth outcomes and a low risk of congenital transmission. Availability of ICU in critical conditions is needed for better pregnancy outcomes. J Bangladesh Coll Phys Surg 2022; 40: 10-16

Grow-IoT (smart analytics app for comprehensive plant health analysis and remote farm monitoring using smart sensors)

Agriculture is the primary source of livelihood for a large section of the society in India, and the ever-increasing demand for high quality and high quantity yield calls for highly efficient and effective farming methods. Grow-IoT is a smart analytics app for comprehensive plant health analysis and remote farm monitoring platform to ensure that the farmer is aware of all the critical factors affecting the farm status. The cameras installed on the field facilitate capturing images of the plants to determine plant health based on phenotypic characteristics. Visual feedback is provided by the computer vision algorithm using image segmentation to classify plant health into three distinct categories. The sensors installed on the field relay crucial information to the Cloud for real-time optimized farm status management. All the data relayed can then be viewed using the user-friendly Grow-IoT app to remotely monitor integral aspects of the farm and take the required actions in case of critical conditions. Thus, the mobile platform combined with computer vision for plant health analysis and smart sensor modules gives the farmer a technical perspective. The simplistic design of the application makes sure that the user has the least cognitive load while using it. Overall, the smart module is a significant technical step to facilitate efficient produce across all seasons in a year.

Design and Simulation of Air-Fuel Percentage Sensors in Drone Engine Controlling

This paper presents the design and simulation of air-fuel percentage sensors in drone engine control using Matlab. The applications of sensor engineering system have been pioneer in technology development and advancement of automated machine as complex systems. The integration of drone fuel sensor system is the major series components such as injector, pumps and switches. The suggested model is tuned to interface drone fuel system with fuel flow in order to optimize efficient monitoring. The sensor system is improved and virtualized in Simulink block set by varying the parameters with high range to observe the fuel utilization curves and extract the validated results. The obtained results show that the possibility of engine operation in critical conditions such as takeoff, landing, sharp maneuver and performance is applicable to turn off the system in case of break down in the sensor to ensure the safety of drone engine. HIGHLIGHTS The drone engine fuel rate sensor is designed and examined to determine the air-to-fuel ratio The suggested model is tuned to interface drone fuel system with fuel flow in order to optimize efficient monitoring The obtained results show that the possibility of using engine with different failure mode and fault considerations The represented control structure is simple, efficient and provides the required air-to-fuel ratio