Search

Abstract Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil – nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs. Several statistical indices and graphical approaches are used as accuracy performance indicators. The results show that virtually all the gathered datapoints are suitable for the purpose of model development. The sensitivity analysis indicated that pressure, temperature and crude oil API all negatively affect the IFT, with pressure being the most effective factor. The evaluation study proved that Random Forest is the most accurate developed intelligent model as it was characterized with acceptable R-squared (0.959), mean square error (1.65), average absolute relative error (6.85%) of unseen test datapoints as well as with correct trend prediction of IFT with regard to all input parameters of pressure, temperature and crude oil API. The developed model can be considered an accurate an easy-to-use tool for the prediction of crude oil/N2 IFT values for enhance oil recovery study optimization and upstream reservoir investigations.

Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by cognitive deterioration and changes in behavior. Acetylcholinesterase (AChE), which hydrolyzes acetylcholine, is a key drug target for treating AD. This research aimed to identify new AChE inhibitors using the IMPPAT database. We used known drugs as a basis to search for similar chemicals in the IMPPAT database and created a library of 127 plant-based compounds. Initial screening of these compounds was performed using molecular docking, followed by an analysis of their drug-likeness and ADMET properties. Compounds with favorable properties underwent density functional theory (DFT) calculations to assess their electronic properties such as HOMO-LUMO gap, electron density, and molecular orbital distribution. These descriptors provided insights into each compound’s reactivity, stability, and binding potential with AChE. Promising candidates were further evaluated through molecular dynamics (MD) simulations over 100 ns and MMPBSA analysis for the last 30 ns. Two compounds, Biflavanone (IMPHY013027) with a binding free energy of − 130.394 kcal/mol and Calomelanol J (IMPHY007737) with − 107.908 kcal/mol, demonstrated strong binding affinities compared to the reference molecule HOR, which has a binding free energy of − 105.132 kcal/mol. These compounds exhibited promising drug-ability profiles in both molecular docking and MD simulations, indicating their potential as novel AChE inhibitors for AD treatment. However, further experimental validation is necessary to verify their effectiveness and safety.

The present study is an attempt to determine the carbon stock and carbon sequestration potential of forest growing stocks around a thermal power plant near Bilaspur smart city in Chhattisgarh, Central India. The non-destructive sampling method is used to estimate above-ground biomass and below-ground biomass. The height and diameter at breast height (DBH) is measured for each individual tree. The allometric equation was made for estimation of carbon storage of tree species. 35 tree species were recorded around the national thermal power plant at a radius of 30 km in four different directions (east, west, north, and south). The results revealed that Ficus benghalensis was the species that found the maximum amount of carbon storage, followed by Ficus religiosa. According to the present study, the allometric models developed can further estimate carbon stocks in forest vegetation in and around National Thermal Power Corporation power plants, and other tropical deciduous forests.

Aims: Diabetes mellitus (DM) is associated with dry mouth, reduced saliva, taste changes, and periodontal disease. This study aimed to evaluate and determine the correlation between oral health status and glycaemic control levels in patients with type 2 DM (T2DM). Methods: This cross-sectional study was conducted on patients with T2DM aged 35-65 years. Glycemic control levels were categorized as good control (≤7% of HbA1c) and poor control (>7% of HbA1c). Full mouth plaque score (FMPS), full mouth bleeding score (FMBS), probing depth (PD), and clinical attachment level (CAL) were determined, along with xerostomia (using a standard questionnaire) and hyposalivation (using modified Schirmer test, MST). Results: This study included 70 individuals with T2DM, comprising 49 males (70%) and 21 females (30%) with a mean age of 55.36±8.3 and 49.36±6.8 years, respectively. Among those with poor glycemic control, a significantly higher prevalence of xerostomia (52.2%), hyposalivation (47.8%), and periodontitis (moderate; 47.8% and severe; 21.7%) was observed compared with those with good control (p=0.027, 0.001, 0.007, respectively). HbA1c exhibited a significant moderate positive correlation with FMPS (r=0.447; p=0.001) and a low correlation with FMBS (r=0.283; p=0.018) and CAL (r=0.301; p=0.011). Conclusions: The study concluded that individuals with poor glycemic control for T2DM have a higher incidence of xerostomia, hyposalivation, and compromised periodontal health, resulting in a decline in their oral health status.

Background: Prenatal arsenic exposure can harm both the mother and foetus by changing gene expression and impacting foetal growth. The study aimed to assess the impact of arsenic exposure on GLI3 expression in feto-placental tissue via maternal blood and record neonatal outcomes. Material and Methods: The study used 54 mother-infant pairs. Atomic absorption spectrophotometer used for measuring arsenic in maternal blood. Based on the median value of arsenic content in maternal blood, samples were split into two groups: the arsenic-low and the arsenic-high group. Maternal age, gestational age, arsenic exposure history, neonatal data were compared between two groups. Using qRT-PCR fold change in GLI3 expression was determined. Results: Many participants resided in arsenic-contaminated regions and consumed underground water. The arsenic high group had a considerably higher mean arsenic content. The arsenic high group had a considerably lower gestational age than the low group. Neonatal birth weight significantly reduced in arsenic high group. Neonatal birth length increased in arsenic high group in comparison to arsenic-low group. Exposure to arsenic significantly decreased the relative expression of GLI3. Conclusion: Prenatal arsenic exposure via maternal blood reduces GLI3 expression and affects neonatal anthropometry.

Numerous studies have explored pricing and lot-sizing strategies for various payment methods, but most have focused primarily on the buyer’s perspective. This study, however, approaches these strategies from a different perspective, incorporating key and relevant factors often overlooked. The volume of sales increases when a seller accepts a buyer’s credit. However, it reduces sales volume when a seller requests a buyer make a payment in advance. To boost sales and profitability, a vendor occasionally provides a price reduction in exchange for a down payment. Demanding a down payment from a customer earns interest and carries without any risk of default. When a vendor offers customers the option to pay with credit, a higher delay payment period facility plan may boost sales volume, but it also increases the risk of default. To maximize profit per unit of time, the vendor aims to simultaneously determine the optimal selling price, replenishment schedule, and payment method. This is achieved by comparing and calculating the vendor’s profit per time unit for credit, cash, and advance payment options. This is done by comparing and calculating the seller’s profit for each piece of time for credit, cash, and advance payments. The following managerial impacts are highlighted by means of numerical analyses: (1) A particular payment type, among the three available options, yields the seller’s highest profit under certain conditions. (2) It is vitally crucial for a vendor to provide a price reduction if an advance payment is required. (3) Advance payment results in higher profit than delayed payment if sales volume does not significantly fall while switching from credit to advance payments, or vice versa. To solve the optimization problem, a popular metaheuristic algorithm (viz., Grey Wolf Optimizer) is used and finally performed a post optimality analysis for making a fruitful conclusion.

Knowing tractor drawbar pull is crucial to ensure the tractor can handle the required workload efficiently and safely, preventing soil damage and optimising field productivity. The present study proposes a novel approach for tractor drawbar pull prediction by utilising the tractor's geometric parameters and forward speed to develop a cloud-infused, server-less, machine learning-based real-time generalised tractor drawbar pull prediction model for any tractor between the 6-58 kW power range. The drawbar pull prediction models from ANN and six ML algorithms were developed, and the data analysis with hyperparameter tuning concluded that the Extreme Gradient Boosting (XGB) ML model outperformed the other ML models. A reasonable accuracy with R2 = 0.93 and MAPE = 6.77% was achieved using the XGB ML model for a separate validation dataset, which was not used for training. Furthermore, a cloud-based serverless Android App integrated with the XGB ML-based drawbar pull prediction model was developed for real-time tractor drawbar pull prediction and monitoring during tillage operations. The field validation demonstrated the XGB ML model's generalisation ability and effectiveness, with R2 = 0.90 and maximum MAPE of 9.86%. It can be used to simulate and optimize tractor performance, guiding manufacturers in selecting geometric parameters for tractor design.

Atherosclerotic plaque formation is a leading cause of arterial thrombosis that significantly impacts global health by instigating major adverse cardiovascular events (MACE) like myocardial infarction (MI) and stroke. Platelets are central to this process, leading to the development of antiplatelet therapies, to mitigate MACE risks. The combination of aspirin with a potent P2Y12 inhibitor known as dual antiplatelet therapy (DAPT) is the standard for post-percutaneous coronary intervention (PCI) aimed at reducing ischemic events. However, DAPT’s associated bleeding risks, particularly in high bleeding risk (HBR) patients, require a balanced approach to optimize therapeutic outcomes. Recent advancements have led to the exploration of ticagrelor monotherapy as a promising strategy after short-term DAPT to reduce bleeding risks while preserving ischemic protection. This review manuscript focuses on ticagrelor monotherapy for HBR patients with discussion on optimal timing, patient selection, and treatment duration. It highlights ticagrelor’s broad efficacy in diverse patient sub-groups and outlines its superiority over aspirin (ASA) and clopidogrel monotherapies. Trials such as TICO, TWILIGHT, GLOBAL LEADERS, and ULTIMATE-DAPT as well as literature meta-analyses validate ticagrelor monotherapy’s role in lowering mortality and clinical adverse events versus conventional DAPT. The review endorses a personalized treatment regimen, beginning with DAPT before moving to ticagrelor monotherapy, as a balanced method for managing both bleeding and ischemic risks in post-PCI acute coronary syndrome (ACS) patients, especially those facing higher bleeding threats.

Diffuse alveolar hemorrhage is a life-threatening condition that could be due to various factors, such as infections, vasculitis, malignancy, etc., Immune thrombocytopenic purpura (ITP) is an autoimmune disorder characterized by persistent thrombocytopenia. Diffuse alveolar hemorrhage is one of the dreadful complications of ITP, and only fewer than 10 case reports have been documented so far. Adult ITP is more common in women than in men. We present a case of a 35-year-old man who was diagnosed with ITP and presented with diffuse alveolar hemorrhage.

Introduction: Orthodontic treatment can alter the oral environment, leading to plaque accumulation and changes in microbial colonization. This study aimed to assess microbiological changes in 10 to 16-year-old children undergoing fixed orthodontic treatment. Methodology: The study included children aged 10–16 years, with one group wearing fixed orthodontic appliances and a control group without brackets. Data on salivary pH, buffer capacity, Streptococcus mutans, and Lactobacillus spp were collected at baseline and after 6 months. Logistic regression models evaluated associations between orthodontic treatment and elevated bacterial levels, adjusting for age, gender, pH, and buffer capacity. Results: Wearing fixed orthodontic appliances was significantly associated with elevated Streptococcus mutans (aOR: 6.62, 95% CI: 1.91–21.88) and Lactobacillus spp (aOR: 9.49, 95% CI: 2.57–35.07). Gender showed a significant association with Lactobacillus spp levels (aOR: 0.28, 95% CI: 0.09–0.87). Conclusion: Fixed orthodontic appliances significantly impact oral microbial changes, underscoring the need for proper oral hygiene during orthodontic treatment to reduce dental caries risk.

Introduction: Endometriosis is a recurring chronic inflammatory condition that affects females of reproductive age. Endometriosis-associated Pelvic Pain (EAPP) adversely impacts physical, mental, and social well-being. Currently, there is no ideal treatment option. Dienogest, a selective progestin, shows a pronounced effect on endometriosis. Aim: To compare the efficacy and safety of Dienogest at doses of 2 mg and 4 mg/day orally in the treatment of endometriosis. Materials and Methods: An observer-blind, parallel-group, randomised clinical trial was conducted in the Department of Obstetrics and Gynaecology at IPGME&R, SSKM Hospital, Kolkata, West Bengal, India, from April 2016 to December 2017. A total of 190 women aged 20-45 years suffering from endometriosis were recruited and divided into two treatment groups, A and B, receiving daily doses of 2 mg and 4 mg, respectively, for 24 weeks. Relief of EAPP was assessed using the Visual Analogue Scale (VAS) score, and improvement in Quality of Life (QoL) was measured. Treatment-related adverse events were also recorded. Numerical variables were compared using Student’s t-test or the Mann-Whitney U Test, and categorical variables were compared using the Chi-square test or Fisher’s-exact test. Results: The absolute reduction in pelvic pain VAS score was 39.71±8.60 at 24 weeks from the initial score of 70.88 (Mean VAS score before treatment with Dienogest) in Group A, compared to 34.80±6.45 from 69.34 (Mean VAS score before treatment with Dienogest) in Group B (p=0.0001). The difference in mean VAS at 24 weeks between the two groups was statistically significant (p=0.0002). At 24 weeks, 18 (24.66%) patients in Group A experienced an irregular bleeding pattern compared to 27 (40.30%) in Group B, with spotting being the most common issue. Adverse effects such as weight gain, acne, alopecia, depression, and decreased libido were observed in both groups, but they were more pronounced in the 4 mg group. Conclusion: The efficacy and safety results from this dose-ranging study of Dienogest indicate that 2 mg/day is the effective dose for treating endometriosis and offers better tolerability compared to the 4 mg dose.

ABSTRACT The host immune responses play a pivotal role in the establishment of long-term memory responses, which effectively aids in infection clearance. However, the prevailing anti-tuberculosis therapy, while aiming to combat tuberculosis (TB), also debilitates innate and adaptive immune components of the host. In this study, we explored how the front-line anti-TB drugs impact the host immune cells by modulating multiple signaling pathways and subsequently leading to disease relapse. Administration of these drugs led to a reduction in innate immune activation and also the cytokines required to trigger protective T cell responses. Moreover, these drugs led to activation-induced cell death in the mycobacterial-specific T cell leading to a reduced killing capacity. Furthermore, these drugs stalled the T cell differentiation into memory subsets by modulating the activation of STAT3, STAT4, FOXO1, and NFκB transcription factors and hampering the Th1 and Th17-mediated long-term host protective memory responses. These findings suggest the urgent need to augment directly observed treatment, short-course (DOTS) therapy with immunomodulatory agents to mitigate the adverse effects linked to the treatment.IMPORTANCEAs a central component of TB eradication initiatives, directly observed treatment, short-course (DOTS) therapy imparts immune-dampening effects during the course of treatment. This approach undermines the host immune system by delaying the activation process and lowering the immune response. In our investigation, we have unveiled the impact of DOTS on specific immune cell populations. Notably, the signaling pathways involving STAT3 and STAT4 critical for memory responses and NFκβ associated with pro-inflammation were substantially declined due to the therapy. Consequently, these drugs exhibit limited effectiveness in preventing recurrence of the disease. These observations highlight the imperative integration of immunomodulators to manage TB infection.

Several factors affect engine performance, including fuel injection pressure, injection angle, and injector orifice diameter. Any deviation from normal conditions in any of these aspects can disrupt optimal engine performance, resulting in inefficient combustion and increased exhaust emissions. To investigate the effect of injector hole number, injection hole angle, and injection pressure on the performance and emissions of a diesel engine operating on a diesel/hydrogen blend (10 % hydrogen and 90 % diesel), a single-cylinder direct injection diesel engine was used. Three injector nozzle configurations just for diesel injector with different hole diameters (0.6, 0.3, and 0.2 mm) were used at injection angles of 0, 15, and 30 degrees, respectively. Three injection pressures (200, 400, and 600 bar) were tested, with results monitored for brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), smoke, and NOx emissions.Optimal results were achieved with a maximum injection pressure of 400 bar and a nozzle angle of 15 degrees, resulting in improved engine performance and BTE, along with a 6.5 % reduction in BSFC. Increasing the number of injector holes, injection pressure, and injection angle resulted in reduced BSFC and smoke emissions, but with a significant increase in NOx emissions. Notably, this study deviates from traditional combustion methods by introducing air from a 1.1-atmosphere tank instead of relying solely on natural intake. In addition, hydrogen fuel is introduced into the air manifold via a separate injector with an injection pressure of 20 bar, while diesel fuel is injected directly into the combustion chamber.