Abstract
Determining the solubility of non-hydrocarbon gases such as carbon dioxide (CO
2
) and nitrogen (N
2
) in water and brine is one of the most controversial challenges in the oil and chemical ...industries. Although many researches have been conducted on solubility of gases in brine and water, very few researches investigated the solubility of power plant flue gases (CO
2
–N
2
mixtures) in aqueous solutions. In this study, using six intelligent models, including Random Forest, Decision Tree (DT), Gradient Boosting-Decision Tree (GB-DT), Adaptive Boosting-Decision Tree (AdaBoost-DT), Adaptive Boosting-Support Vector Regression (AdaBoost-SVR), and Gradient Boosting-Support Vector Regression (GB-SVR), the solubility of CO
2
–N
2
mixtures in water and brine solutions was predicted, and the results were compared with four equations of state (EOSs), including Peng–Robinson (PR), Soave–Redlich–Kwong (SRK), Valderrama–Patel–Teja (VPT), and Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT). The results indicate that the Random Forest model with an average absolute percent relative error (AAPRE) value of 2.8% has the best predictions. The GB-SVR and DT models also have good precision with AAPRE values of 6.43% and 7.41%, respectively. For solubility of CO
2
present in gaseous mixtures in aqueous systems, the PC-SAFT model, and for solubility of N
2
, the VPT EOS had the best results among the EOSs. Also, the sensitivity analysis of input parameters showed that increasing the mole percent of CO
2
in gaseous phase, temperature, pressure, and decreasing the ionic strength increase the solubility of CO
2
–N
2
mixture in water and brine solutions. Another significant issue is that increasing the salinity of brine also has a subtractive effect on the solubility of CO
2
–N
2
mixture. Finally, the Leverage method proved that the actual data are of excellent quality and the Random Forest approach is quite reliable for determining the solubility of the CO
2
–N
2
gas mixtures in aqueous systems.
Wearable medical devices are widely used for monitoring and treatment of patients. Electrostatic discharge can render these devices unreliable and cause a temporary or permanent disturbance in their ...operation. In a healthcare environment, severe electrostatic discharge (ESD) can occur while a patient, lying down or sitting on a hospital bed with a wearable device, discharges the device via a grounded bedframe. To protect the devices from ESD damage, the worst-case discharge conditions in the usage environment need to be identified. Previous studies by authors revealed that such events could be more severe than the conventional human metal model (HMM). However, the impact of various body postures and device location on the body and the severity of the discharge current compared with HMM have not been investigated for healthcare environments. This study is an attempt to address the gap in the literature by investigating severe discharges in such environments and characterizing their current waveforms for three postures (standing on the floor, sitting, and lying down on a hospital bed), two device locations (hand and waist), and four body voltages (2, 4, 6, and 8 kV). This study highlights that the IEC 61000-4-2 standard may not be sufficient for testing wearable medical devices.
The use of electric vehicles in the mobility of people is on the rise. This trend naturally is reflected in the public transport systems as well and presents a potential for clean transport in the ...cities with the reduction of operational costs for the providers of public transport systems. In this paper, we focus on one of the problems when introducing a fleet of electric buses to the city transport system, specifically the electric bus fleet scheduling problem. In this problem, an assignment of available electric buses to the service trips as well as the charging process and charging scheduling is addressed in order to minimize the number of used electric buses. The specifics of the electric buses such as the restricted driving range and long charging time need to be considered. The used type of charging is the opportunity charging, where the chargers are located at different places of the road network. To solve the problem, we propose a heuristic approach based on the grouping genetic algorithm, which utilizes the grouping character of the scheduling problem. The algorithm was tested on the datasets generated from the public transport system in the city of Žilina and compared to the results obtained by solving the previously presented mathematical model.
Following the increasing threat to the environment, the application of hydrogen as an energy carrier is one of the solutions that has received much attention. Considering the importance of gas ...solubility in separation and gasification processes, the solubility of hydrogen in various solvents has particular importance. In this study, an extensive database containing 580 experimental data points (13 ionic liquids (ILs)) in a vast range of temperature (278.2–453.15 K) and pressure (0.433–552 bar) was employed to determine the solubility of hydrogen in ILs using intelligent models. In this regard, four intelligent models, including Random Forest (RF), Adaptive Boosting-Support Vector for Regression (AdaBoost-SVR), Deep Belief Network (DBN), and Multivariate Adaptive Regression Splines (MARS) models, were developed with two different approaches. In the first method, the chemical substructures were considered as inputs; in the second manner, the thermodynamic attributes of ILs were considered as inputs. Temperature and pressure were also two inputs of both methods. The results show that in both methods, the DBN paradigm has the best proficiency. The best root mean square error (RMSE) and coefficient of determination (R2) values in the former way were 0.00106 and 0.9991, respectively, and in the latter way, 0.00066 and 0.9996, respectively. The findings of sensitivity analysis show that among the chemical substructures and thermodynamic properties, –CH3 and pressure, with absolute relevancy factor values of 0.425 and 0.517, respectively, have the most impression on the hydrogen solubility in ILs. Also, the investigation of the influence of different parameters on hydrogen solubility shows that raising the pressure and alkyl chain length increases the hydrogen solubility in ILs. The acquired findings were also compared with equations of state (EOSs), and it was found that the intelligent models have better performance and high efficiency than EOSs. Finally, to validate the model data, the leverage method was used, which shows that over 96.7% of the data is in the authentic domain and only about 1% of the data is in the suspected data region.
•H2 solubility in ionic liquids (ILs) is predicted.•Machine learning approaches are used: RF, AdaBoost-SVR, DBN, MARS.•DBN as a deep learning type model has the best performance.•Better performance of machine learning approaches compared to equations of state.•-CH3 has the highest positive effect on solubility.
This paper presents a globe coverage of constellation satellite in one revisit and the regional coverage at defined latitude. This constellation seems to be very close to optimal under the maximum ...revisit time comparing to the results in other papers. They are produced easily by using tables and data achievements. These satellites are fully connected by crosslinks sweeping the Earth. This model is more optimal comparing to the conventional constellations that distribute satellites evenly in the space. Since satellites on the LEO orbit are side by side, in most cases, they can maintain communication continuously. This special feature allows all satellites in the constellation connecting ground at any time when a satellite is available to the stations. Length of the crosslink is allowed to reject location connection or real-time data transmission. For example, six satellites in the constellation cover the whole Earth within one revisit time and all the data are collected by two Earth stations for keeping continuous coverage. Thus, the adjacent satellites may be more efficient and provide more coverage.
Transcription factors specify the fate and connectivity of developing neurons. We investigate how a lineage-specific transcription factor, Acj6, controls the precise dendrite targeting of Drosophila ...olfactory projection neurons (PNs) by regulating the expression of cell-surface proteins. Quantitative cell-surface proteomic profiling of wild-type and acj6 mutant PNs in intact developing brains, and a proteome-informed genetic screen identified PN surface proteins that execute Acj6-regulated wiring decisions. These include canonical cell adhesion molecules and proteins previously not associated with wiring, such as Piezo, whose mechanosensitive ion channel activity is dispensable for its function in PN dendrite targeting. Comprehensive genetic analyses revealed that Acj6 employs unique sets of cell-surface proteins in different PN types for dendrite targeting. Combined expression of Acj6 wiring executors rescued acj6 mutant phenotypes with higher efficacy and breadth than expression of individual executors. Thus, Acj6 controls wiring specificity of different neuron types by specifying distinct combinatorial expression of cell-surface executors.
In 2010, the Addendum D to ASHRAE Standard 170, “Ventilation of healthcare facilities,” lowered the minimum relative humidity (RH) requirement of anesthetizing locations (including operating rooms, ...operating/surgical cystoscopic rooms, delivery rooms (Caesarean), recovery rooms, critical and intensive care, newborn intensive care, treatment rooms, trauma rooms (crisis or shock), laser eye rooms, newborn nursery suites, and endoscopy rooms) from 30 % to 20 %. The new minimum limit was adopted based on the results of a review paper that suggested that lowering humidity levels will have little or no impact on providing a safe environment for patients, staff, or medical equipment. That review paper reached this conclusion by assuming that there were no medical device failures due to electrostatic discharge (ESD). However, in an examination of the FDA’s MAUDE database of reported defects and recalls, we identified numerous medical device failures explicitly due to ESD. This paper presents technical reliability and safety concerns regarding the new guidelines and recommends that such changes should not be implemented and that the guidelines should be revoked.
Future long baseline neutrino experiments such as the Deep Underground Neutrino Experiment (DUNE) pose challenges for development of readout techniques for multi-kiloton LAr Time Projection Chambers ...(TPCs). In contrast to wire/strip anode readout, a pixelated readout eliminates disadvantages such as disambiguation in 2D track reconstruction. The Q-Pix Consortium, established in 2019, is developing a pixelated readout technique for LAr TPCs based on charge-integrate/reset (CIR) circuits. The CIR blocks generate a sequence of reset pulses with time intervals corresponding to fixed charge integrals, allowing signal reconstruction without continuous digitization. The Q-Pix ASIC, intended for reading out pixel arrays, comprises CIR blocks along with digital components responsible for communication and reconfigurable data routing. This work is devoted to give an overview of the Q-Pix project, its status, and prospects, with emphasis on the development and prototyping of the Q-Pix readout ASICs.
A nanofinger gate vacuum field-emission transistor with a vertical channel (FGVFET) is proposed herein. The reduction of the gate leakage current is investigated to obtain an optimum structure. The ...proposed three-terminal metal–insulator–metal device with a 43-nm vertical vacuum channel is capable of operating in air ambient and provides a high anode drive current (101 µA), while both the gate and anode voltages are small at about 5 V. Meanwhile, the gate leakage current of the FGVFET is reduced by about sevenfold compared with conventional structures. Also, this vacuum transistor exhibits a low threshold voltage (0.55 V) that is comparable to modern solid-state devices. As a result, a significant cutoff frequency (
f
T
) of 1.13 THz is obtained. Other electrical characteristics of the FGVFET, such as the on–off current ratio and transconductance, are also calculated. The introduced modification could be applied to other vacuum vertical-channel transistors to provide a new class of high-speed low-power transistors for digital applications.
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