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For analyzing hydrogenation process for treatment of petroleum-based fuels, solubility of hydrogen in the feed should be well correlated. The main correlation factors are temperature and pressure which have a great effect on hydrogen solubility. This research paper presents the development of three models for predicting the solubility of hydrogen gas (H2) in diesel. Pressure and Temperature are the input parameters and solubility is single output. The models were fine-tuned using the Bat Algorithm (BA). The three models include Orthogonal Matching Pursuit Regression (OMP), K Nearest Neighbors Regression (KNN), and Tweedie Regression (TDR). The results of the study revealed that the OMP model achieved the highest level of accuracy, with an R2 score of 0.98, and the least RMSE and MAE error rates of 0.24 and 0.19, respectively. The KNN model also performed well with an R2 score of 0.92, an RMSE of 0.42, and an MAE of 0.37. The TDR model had the lowest accuracy compared to the other two models. These results imply that the OMP model is the most suitable one for predicting H2 solubility. The models can be used to enhance the efficiency of fuel production by providing accurate predictions of H2 solubility.

The advent of Network Function Virtualization (NFV) technology has brought flexible provisioning to Fog–Cloud Computing-based Networks (FCCNs) for enterprises to outsource their network functions to data center networks. Service Function Chaining (SFC) is a networking concept in NFV by which traffic is steered through an ordered set of Virtual Network Functions (VNFs) composing an end-to-end service. When hundreds of users outsource their network functions to FCCN, the optimal placement of VNFs in the network becomes important for assembling SFCs with the aim of resource utilization efficiency. Motivated by the scalability shortcomings of existing schemes, we propose Deep Reinforcement Learning (DRL)-based approaches by simultaneously considering parallelized SFC and reuse of VNFs to solve this problem, i.e., Asynchronous Advantage Actor–Critic (A3C). A parallelized SFC consists of several sub-SFCs, which can reduce delay and guarantee availability. Also, reuse of preliminary VNFs in SFC placement can improve computation acceleration. The proposed scheme pursues the maximization of the long-term cumulative reward for the trade-off between Quality of Service (QoS) and service cost. The results of the experiments show that the proposed scheme performs better than the state-of-the-art methods.

Abstract Selective vascular access to the brain is desirable in metabolic tracer, pharmacological and other studies aimed to characterize neural properties in isolation from somatic influences from chest, abdomen or limbs. However, current methods for artificial control of cerebral circulation can abolish pulsatility-dependent vascular signaling or neural network phenomena such as the electrocorticogram even while preserving individual neuronal activity. Thus, we set out to mechanically render cerebral hemodynamics fully regulable to replicate or modify native pig brain perfusion. To this end, blood flow to the head was surgically separated from the systemic circulation and full extracorporeal pulsatile circulatory control (EPCC) was delivered via a modified aorta or brachiocephalic artery. This control relied on a computerized algorithm that maintained, for several hours, blood pressure, flow and pulsatility at near-native values individually measured before EPCC. Continuous electrocorticography and brain depth electrode recordings were used to evaluate brain activity relative to the standard offered by awake human electrocorticography. Under EPCC, this activity remained unaltered or minimally perturbed compared to the native circulation state, as did cerebral oxygenation, pressure, temperature and microscopic structure. Thus, our approach enables the study of neural activity and its circulatory manipulation in independence of most of the rest of the organism.

Background: Despite cardiovascular disease being the leading cause of death in India, limited data exist regarding the factors associated with outcomes in patients with diabetes who suffer acute myocardial infarction (AMI). Methods: We examined 21,374 patients with AMI enrolled in the ACS QUIK trial. We compared in-hospital and 30-day major adverse cardiac events including death, re-infarction, stroke, or major bleeding in those with and without diabetes. The associations between diabetes and cardiac outcomes were adjusted for presentation and in-hospital management using logistic regression. Results: Mean ± SD age was 60.1 ± 12.0 years, 24.3% were females, and 44.4% had diabetes. Those with diabetes were more likely to be older, female, hypertensive, and have higher Killip class but less likely to present with STEMI. Patients with diabetes had longer symptoms onset-to-arrival (median 225 vs 290 min; P < 0.001) and, in case of STEMI, longer door-to-balloon times (median, 75 vs 91 min; P < 0.001). Diabetes was independently associated with higher in-hospital death (adjusted odds ratio [aOR], 1.46; 95% CI, 1.12–1.89), in-hospital reinfarction (aOR, 1.52; 95% CI, 1.15–2.02), 30-day MACE (aOR, 1.33; 95% CI, 1.14–1.55) and 30-day death (aOR, 1.40; 95%CI, 1.16–1.69) but not 30-day stroke or 30-day major bleeding. Conclusion: Among patients presenting with AMI in Kerala, India, a considerable proportion has diabetes and are at increased risk for in-hospital and 30-day adverse cardiovascular outcomes. Increased awareness of the increased cardiovascular risk and attention to the implementation of established cardiovascular therapies are indicated for patients with diabetes in lower-middle-income countries who develop AMI. Clinical Trial registration: ClinicalTrials.gov Unique identifier: NCT02256658.

Background: Limited data exist on the risk profile and outcomes among young patients with acute myocardial infarction(AMI) in low-and middle-income countries(LMICs). This study explored differences in the clinical characteristics, medical care, and outcomes of AMI in young adults in India with a subanalysis focusing on sex disparities amongst the young. Methods: Using the Acute Coronary Syndrome Quality Improvement in Kerala trial database, we compared baseline characteristics, management, and outcomes amongst the young patients(≤50 years) and their older counterparts. The primary outcomes were the rates of in-hospital and 30-day composite of in-hospital major adverse cardiovascular events(MACE). Results: Of the 21,374 adults enrolled, 4762(22%) were young, of which 614 (12.9%) were females. Young patients with AMI were more likely to be smokers(41.9% vs. 27.8%;P

Abstract Background Public acceptance of governmental measures are key to controlling the spread of infectious diseases. The COVID-19 pandemic has placed a significant burden on healthcare systems for high-income countries as well as low- and middle-income countries (LMICs). The ability of LMICs to respond to the challenge of the COVID-19 pandemic has been limited and may have affected the impact of governmental strategies to control the spread of COVID-19. This study aimed to evaluate and compare public opinion on the governmental COVID-19 response of high and LMICs in the Middle East and benchmark it to international countries. Methods An online, self-administered questionnaire was distributed among different Middle Eastern Arab countries. Participants’ demographics and level of satisfaction with governmental responses to COVID-19 were analyzed and reported. Scores were benchmarked against 19 international values. Results A total of 7395 responses were included. Bahrain scored highest for satisfaction with the governmental response with 38.29 ± 2.93 on a scale of 40, followed by the Kingdom of Saudi Arabia (37.13 ± 3.27), United Arab Emirates (36.56 ± 3.44), Kuwait (35.74 ± 4.85), Jordan (23.08 ± 6.41), and Lebanon (15.39 ± 5.28). Participants’ country of residence was a significant predictor of the satisfaction score (P

Solubility data for ANA (Anastrozole) drug in supercritical solvent was investigated in this study, and models were developed to estimate the solubility values. The main aim was to provide a predictive methodology for determination of drug solubility in wide range of operational parameters for advanced green pharmaceutical manufacture. The properties used are temperature and pressure which were considered as the models’ inputs. Modeling has been done using three models based on the support vector regression. These models include support vector regression (with polynomial kernel), boosted support vector machine with AdaBoost, and improved support vector machine with bagging. These models were evaluated after optimization, and all three models have a coefficient of determination (R2) higher than 0.98. Also considering RMSE, AdaBoosted SVR, Bagging SVR, and SVR have error rates of 2.31E-01, 4.31E-01, and 5.01E-01.

Abstract Background Rheumatic heart disease (RHD) is the most common acquired heart disease occurring in children and adolescents. RHD is associated with significant morbidity and mortality particularly in low and middle- income countries (LMICs) where the burden is estimated to be higher compared to high income countries. Subclinical RHD is the presence of valvular lesion diagnosed by echocardiography in a person with no clinical manifestation of RHD. This study aimed at determining the prevalence, types and factors associated with subclinical RHD among primary school children in Dar Es Salaam, Tanzania. Methods A descriptive community-based cross-sectional study was conducted in primary school children from February to May 2019. A standardized structured questionnaire was used to collect demographic characteristics, history of upper respiratory tract infections (URTIs), anthropometric measurements, and chest auscultation findings. Moreover echocardiographic screening was done to all children recruited into the study. World Heart Federation echocardiographic classification was used to define the types and prevalence of subclinical RHD. Results A total of 949 primary school children were enrolled with females being predominant (57.1%). The prevalence of subclinical RHD was 34 per 1000. All the participants had mitral valve disease only whereby 17 had definite disease and 15 had a borderline disease. The associated factors for subclinical RHD were older age of more than 9 years (OR 10.8, 95% CI 1.4–82.2, P = 0.02) having three or more episodes of URTI in previous six months (OR 21, 95% CI 9.6–46, P = 0.00) and poor hygiene (OR 3, 95% CI 1.3–6.8, P = 0.009). Conclusion Subclinical RHD as detected by echocardiographic screening is prevalent in primary school children, uniformly affects the mitral valve, and is associated with potentially modifiable risk factors. Children with a history of more than three episodes of URTI in six months represents a high-risk population that should be targeted for RHD screening.

In this study, a new optimization approach is introduced to reduce vibration exerted on a double crank-rocker mechanism. A dynamic analysis based on planar methods of this mechanism is suggested, and formulation of shaking forces and shaking moments inducing vibration on this system is presented. A two-step optimization technique is suggested to study system sensitivity to the components of shaking forces and moments to enhance the mechanismâs balancing process. This leads to identification of the most dominant parameters which are then used to formulate the objective functions of effective optimization. Each objective function is studied for each individual case, after which the outcome results of the mechanismâs balancing optimization are introduced and discussed.