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Underwater Wireless Sensor Networks (UWSNs) are an enabling technology for many applications in commercial, military, and scientific domains. In some emergency response applications of UWSN, data dissemination is more important, therefore these applications are handled differently as compared to energy-focused approaches, which is only possible when propagation delay is minimized and packet delivery at surface sinks is assured. Packet delivery underwater is a serious concern because of harsh underwater environments and the dense deployment of nodes, which causes collisions and packet loss. Resultantly, re-transmission causes energy loss and increases end-to-end delay ( D E 2 E ). In this work, we devise a framework for the joint optimization of sink mobility, hold and forward mechanisms, adoptive depth threshold ( d t h ) and data aggregation with pattern matching for reducing nodal propagation delay, maximizing throughput, improving network lifetime, and minimizing energy consumption. To evaluate our technique, we simulate the three-dimensional (3-D) underwater network environment with mobile sink and dense deployments of sensor nodes with varying communication radii. We carry out scalability analysis of the proposed framework in terms of network lifetime, throughput, and packet drop. We also compare our framework to existing techniques, i.e., Mobicast and iAMCTD protocols. We note that adapting varying d t h based on node density in a range of network deployment scenarios results in a reduced number of re-transmissions, good energy conservation, and enhanced throughput. Furthermore, results from extensive simulations show that our proposed framework achieves better performance over existing approaches for real-time delay-intolerant applications.

Stock market forecasting is one of the most challenging problems in today’s financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured over a 1-day time frame. Then, the models are trained using the proposed methodology. Following the traditional methodology, Logistic Regression achieved the highest accuracy of 85.51% followed by XG Boost and Random Forest. With the proposed strategy, the Random Forest model achieved the highest accuracy of 91.27% followed by XG Boost, ADA Boost and ANN. In the later part of the paper, it is shown that only classification report is not sufficient to validate the performance of ML model for stock market prediction. A simulation model of the financial market is used in order to evaluate the risk, maximum draw down and returns associate with each ML model. The overall results demonstrated that the proposed strategy not only improves the stock market returns but also reduces the risks associated with each ML model.

Background & Objectives India is considered a region with intermediate to high endemicity for the carriage of Hepatitis B surface antigen (HBsAg). Epidemiological updates are crucial to monitor the progress towards the global commitment to eliminate hepatitis by 2030. This study was designed to analyse the demographic, epidemiological, laboratory, virological, clinical, and genotypic characteristics of the patients with Chronic Hepatitis B (CHB) in North India. Methods One hundred and eighty-three HBsAg-positive patients were enrolled in the study between October 2019 and October 2022. Inclusion criteria required patients to have HBsAg detectable in serum for more than six months. The genotype of Hepatitis B virus (HBV) was determined by using polymerase chain reaction (PCR)- based method. To validate the findings, 20 samples were selected for HBV DNA polymerase genes (S/POL) sequencing, which is crucial for accurately classifying of the virus and its genotypic characteristics. Sequences were manually edited with the BioEdit Sequence Editor (version 7.2.5) and analysed via BLAST. Results Among 183 HBsAg-positive chronic liver disease patients, 77.5 per cent clinically presented as HBeAg negative chronic hepatitis. The identified genotypes were predominantly D (170; 92.2%), followed by A (11; 6%) and C (2; 1.1%). Of the total patients, 102 (55.7%) were male, with the majority within the 0-45 years age group (83.4%). The most common risk factor was surgical intervention (77; 42.1%), followed by tattooing and body piercing (39; 21.3%), blood transfusion (14; 7.7%), dialysis (33; 18%), mother-to-child transmission (4; 2.2%), IV drug abuse (10; 5.5%), and dental procedures (3; 1.6%). Familial transmission was observed in 11.8 per cent of spouses. Interpretation & conclusions The study highlighted that genotype D was the most prevalent and acquired commonly through the parenteral routes, with severe disease phase, while genotype A was the next frequent genotype associated with vertical or familial spread, with the most patients seen in the immune-tolerant phase.

In the current study, APOB ( rs1052031) genotype-guided proteomic analysis was performed in a cohort of Pakistani population. A total of 700 study subjects, including Coronary Artery Disease (CAD) patients (n = 480) and healthy individuals (n = 220) as a control group were included in the study. Genotyping was carried out by using tetra primer-amplification refractory mutation system-based polymerase chain reaction (T-ARMS-PCR) whereas mass spectrometry (Orbitrap MS) was used for label free quantification of serum samples. Genotypic frequency of GG genotype was found to be 90.1%, while 6.4% was for GA genotype and 3.5% was for AA genotypes in CAD patients. In the control group, 87.2% healthy subjects were found to have GG genotype, 11.8% had GA genotype, and 0.9% were with AA genotypes. Significant ( p  = 0.007) difference was observed between genotypic frequencies in the patients and the control group. The rare allele AA was found to be strongly associated with the CAD [OR: 4 (1.9–16.7)], as compared to the control group in recessive genetic model ( p  = 0.04). Using label free proteomics, altered expression of 60 significant proteins was observed. Enrichment analysis of these protein showed higher number of up-regulated pathways, including phosphatidylcholine-sterol O -acyltransferase activator activity, cholesterol transfer activity, and sterol transfer activity in AA genotype of rs562338 (G>A) as compared to the wild type GG genotype. This study provides a deeper insight into CAD pathobiology with reference to proteogenomics, and proving this approach as a good platform for identifying the novel proteins and signaling pathways in relation to cardiovascular diseases.

Biotic and abiotic stresses adversely upset plant growth and development via nutrient deficiencies, hormonal imbalances, ion toxicity as well as osmotic and oxidative anxiety. The most effective mechanism is the biosynthesis of secondary metabolites at cellular level which included organic compounds that help plants to cope with stress conditions by reducing the intensity of stress through enhancing antioxidants activities, detoxifying toxic ions, regulating the uptake of nutrients and by mediating the transport and distribution of different hormones. Caffeic acid is actively involved in plant physiology and mechanisms of stress tolerance primarily utilized by plants for the synthesis of lignin which ultimately thickened cell walls and plant become resistant to ion toxicity sodium and heavy metal stress. It also reconciles the absorption of high energy radiations in mesophyll cells under drought stress, mechanism involves the production of ferulic acid through the methylation of caffeic acid catalyzed by O-methyltransferase. It has been concluded that exogenous application of cafeic acid may be a best option to cope with salinity, ion toxicity, drought and heavy metal stress.

Nanotechnology has become a transformative field in modern science and engineering, offering innovative approaches to enhance conventional thermal and fluid systems. Heat and mass transfer phenomena, particularly fluid motion across various geometries, play a crucial role in industrial and engineering processes. The inclusion of nanoparticles in base fluids significantly improves thermal conductivity and enables advanced phase-change technologies. The current work examines Powell–Eyring nanofluid’s heat transmission properties on a stretched Riga plate, considering the effects of magnetic fields, porosity, Darcy–Forchheimer flow, thermal radiation, and activation energy. Using the proper similarity transformations, the pertinent governing boundary-layer equations are converted into a set of ordinary differential equations (ODEs), which are then solved using the boundary value problem fourth-order collocation (BVP4C) technique in the MATLAB program. Tables and graphs are used to display the outcomes. Due to their significance in the industrial domain, the Nusselt number and skin friction are also evaluated. The velocity of the nanofluid is shown to decline with a boost in the Hartmann number, porosity, and Darcy–Forchheimer parameter values. Moreover, its energy curves are increased by boosting the values of thermal radiation and the Biot number. A stronger Hartmann number M decelerates the flow (thickening the momentum boundary layer), whereas increasing the Riga forcing parameter Q can locally enhance the near-wall velocity due to wall-parallel Lorentz forcing. Visual comparisons and numerical simulations are used to validate the results, confirming the durability and reliability of the suggested approach. By using a systematic design technique that includes training, testing, and validation, the fluid dynamics problem is solved. The model’s performance and generalization across many circumstances are assessed. In this work, an artificial neural network (ANN) architecture comprising two hidden layers is employed. The model is trained with the Levenberg–Marquardt scheme on reliable numerical datasets, enabling enhanced prediction capability and computational efficiency. The ANN demonstrates exceptional accuracy, with regression coefficients R≈1.0 and the best validation mean squared errors of 8.52×10−10, 7.91×10−9, and 1.59×10−8 for the Powell–Eyring, heat radiation, and thermophoresis models, respectively. The ANN-predicted velocity, temperature, and concentration profiles show good agreement with numerical findings, with only minor differences in insignificant areas, establishing the ANN as a credible surrogate for quick parametric assessment and refinement in magnetohydrodynamic (MHD) nanofluid heat transfer systems.

Electrical grids are one of the major sources of emissions of greenhouse gases (GHG), which are harmful to the environment because they contribute to global warming. As the geographical, environmental, political, and policy constraints are different, policies and research frameworks from developed countries cannot be used directly in developing countries. This paper suggests a completely integrated quantification approach (IQA) and sub-methodologies, such as SM1, SM2, and SM3, that consider the limitations, evaluates the effects, and suggest a way to deal with climate change problems on the power grid. From the perspective of renewable energy (RE) integration and GHG emissions (mainly CO2), the proposed approach addresses the limitations in the policy framework extending to 2030. In addition, the effects of the changes in the ambient temperature, from 0.5 °C to 2 °C, have been examined for thermal power generation and transformers. Lastly, the proposed method considers how energy-efficient devices (EEDs) affect the residential load sector. The results show that households used 10.7% less energy and their costs decreased significantly. This work’s quantitative approach gives a specific way to reduce the carbon footprint of the electrical grid.

Background: Glioblastoma (GBM) harbors significant genetic heterogeneity, high infiltrative capacity, and patterns of relapse following many therapies. The expression of nuclear factor kappa-B (NF-κB p65 (RelA)) and signaling pathways is constitutively activated in GBM through inflammatory stimulation such as tumor necrosis factor-alpha (TNFα), cell invasion, motility, abnormal physiological stimuli, and inducible chemoresistance. However, the underlying anti-tumor and anti-proliferative mechanisms of NF-κB p65 (RelA) and TNFα are still poorly defined. This study aimed to investigate the expression profiling of NF-κB p65 (RelA) and TNFα as well as the effectiveness of celecoxib along with temozolomide (TMZ) in reducing the growth of the human GBM cell line SF-767. Methods: genome-wide expression profiling, enrichment analysis, immune infiltration, quantitative expression, and the Microculture Tetrazolium Test (MTT) proliferation assay were performed to appraise the effects of celecoxib and TMZ. Results: demonstrated the upregulation of NF-κB p65 (RelA) and TNFα and celecoxib reduced the viability of the human glioblastoma cell line SF-767, cell proliferation, and NF-κB p65 (RelA) and TNFα expression in a dose-dependent manner. Overall, these findings demonstrate for the first time how celecoxib therapy could mitigate the invasive characteristics of the human GBM cell line SF-767 by inhibiting the NF-κB mediated stimulation of the inflammatory cascade. Conclusion: based on current findings, we propose that celecoxib as a drug candidate in combination with temozolomide might dampen the transcriptional and enzymatic activities associated with the aggressiveness of GBM and reduce the expression of GBM-associated NF-κB p65 (RelA) and TNFα inflammatory genes expression.

Genetic polymorphisms of apolipoprotein B gene (APOB) may result into serum proteomic perturbance in Coronary Artery Disease (CAD). The current case–control cohort of Pakistani subjects was designed to analyze the genetic influence of APOB rs1042031, (G/T) genotype on serum proteome. Subjects were categorized into two groups: CAD patients (n = 480) and healthy individuals (n = 220). For genotyping, tetra ARMS-PCR was carried out and validated through sequencing, whereas LC/MS-based proteomic analysis of serum samples was performed through label-free quantification. In initial step of genotyping, the frequencies of each genotype GG, GT, and TT were 70%, 27%, and 30% in CAD patients, while in control group, the subjects were 52%, 43%, and 5%, respectively, in CAD patients. The genotypic frequencies in patients vs. control groups found significantly different (p = 0.004), and a strong association of dominant alleles GG with the CAD was observed in both dominant (OR: 2.4 (1.71–3.34), p = 0.001) and allelic genetic models (OR: 2.0 (1.45–2.86), p = 0.001). In second step of label-free quantitation, a total of 40 significant proteins were found with altered expression in CAD patients. The enriched Gene Ontology (GO) terms of molecular functions and pathways of these protein showed upregulated pathways as follows: chylomicron remodeling and assembly, complement cascade activation, plasma lipoprotein assembly, apolipoprotein-A receptor binding, and metabolism of fat-soluble vitamins in G allele carrier of rs1042031 (G > T) vs. mutant T-allele carriers. This study provides better understanding of CAD pathobiology by proteogenomics of APOB. It evidences the influence of APOB rs1042031-dominant (GG) genotype with CAD patients.

ABSTRACT Objective: To share our experience related to molecular detection of Severe Acute Respiratory Syndrome Coronavirus-2(SARS-CoV-2) RNA in COVID-19 suspected patients reported at CMH Malir. Study Design: Cross-sectional study. Place and Duration of Study: Department of Microbiology, Pathology Laboratory of Combined Military Hospital Malir, from March to May 2020. Methodology: Individuals with signs and symptoms of Coronavirus Disease-19(COVID-19) and asymptomatic patients with history of having close contact to confirmed COVID-19 patients or travelling history were considered for SARS-CoV-2 Polymerase chain reaction(PCR) assay. Total of 1330 nasopharyngeal swabs were collected for qualitative detection of COVID-19 viral RNA by real-time reverse transcription polymerase chain reaction(RT-PCR) assay. Results: Out of 1330 tests, 74 patients were found to be SARS-CoV 2 PCR positive. Average age of patients was 30.45 ± 31.9 years with predominance of 55(74.3%) male patients. Within 74 patients, six(8%) died in age group ≥ 40 years. Time duration of positive PCR after initial positive PCR varied between 8 days to 45 days. Conclusion: In this study, we noticed male predominance as they are more exposed to outside environment and susceptible to acquire the virus. Therefore, they were screened in majority. Also, we need a reliable and globally accepted test like SARS COV-2 RT-PCR for early detection of both asymptomatic and symptomatic cases. This will help us in taking appropriate steps to prevent its spread further.