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Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk factors and achieving accurate prediction become particularly challenging in the presence of imbalanced and missing data. This study encompasses three imputation techniques to deal with missing data. To tackle data imbalance, it employs the synthetic minority oversampling technique (SMOTE). The study initiates with a baseline model and subsequently employs an extensive range of advanced models. This study thoroughly evaluates the performance of these models by employing k-fold cross-validation on various imbalanced and balanced datasets. The findings reveal that age, body mass index (BMI), average glucose level, heart disease, hypertension, and marital status are the most influential features in predicting strokes. Furthermore, a Dense Stacking Ensemble (DSE) model is built upon previous advanced models after fine-tuning, with the best-performing model as a meta-classifier. The DSE model demonstrated over 96% accuracy across diverse datasets, with an AUC score of 83.94% on imbalanced imputed dataset and 98.92% on balanced one. This research underscores the remarkable performance of the DSE model, compared to the previous research on the same dataset. It highlights the model's potential for early stroke detection to improve patient outcomes.

A sustainable approach was proposed to address environmental pollution, carbon footprint and economic efficiency challenges in Electrical Discharge Machining (EDM). This approach involved the use of Bio-dielectric such as biodiesel and Bio fuel (distilled water with 10% ethanol). The EDM process performance was further optimized by experimenting with both electrodes’ rotation (i.e., in same direction, opposite direction, no rotation) and the use of treated tools (no treatment, heat treatment, cryogenic treatment). Biodiesel as a bio-dielectric showed promise by delivering the highest Material Removal Rate (MRR) and the lowest Tool Wear Rate (TWR). Bio-fuel (distilled water with 10% ethanol) resulted in the lowest Surface Roughness (SR) and cleaner machined surface with least carbon deposition. Additionally, electrode rotation improved flushing and enhanced performance parameters, with opposite direction rotation yielding the highest MRR and the lowest SR. However, no rotation of electrodes resulted in the lowest TWR. The use of treated tools, specifically heat-treated and cryogenically treated tools, also improved performance and reduced energy consumption, with cryogenic treatment providing the highest MRR, heat treatment giving least SR, and no treatment providing least TWR. Certain interactions between factors significantly impacted performance parameters. Grey relational analysis revealed that using distilled water with 10% ethanol as a dielectric, employing cryogenically treated copper tools, and having no rotation of both electrodes yielded the best performance parameters.

Additive manufacturing is a key component of the fourth industrial revolution (IR4.0) that has received increased attention over the last three decades. Metal additive manufacturing is broadly classified into two types: melting-based additive manufacturing and solid-state additive manufacturing. Friction stir additive manufacturing (FSAM) is a subset of solid-state additive manufacturing that produces big area multi-layered components through plate addition fashion using the friction stir welding (FSW) concept. Because of the solid-state process in nature, the part produced has equiaxed grain structure, which leads to better mechanical properties with less residual stresses and solidification defects when compared to existing melting-based additive manufacturing processes. The current review article intends to highlight the working principle and previous research conducted by various research groups using FSAM as an emerging material synthesizing technique. The summary of affecting process parameters and defects claimed for different research materials is discussed in detail based on open access experimental data. Mechanical properties such as microhardness and tensile strength, as well as microstructural properties such as grain refinement and morphology, are summarized in comparison to the base material. Furthermore, the viability and potential application of FSAM, as well as its current academic research status with technology readiness level and future recommendations are discussed meticulously.

The co-torrefaction of several biomasses may be a viable solution in the study area, as it produces biofuels and addresses waste-treatment concerns. This review evaluates biomass through ultimate, proximate, and FTIR analyses, and the mechanism of the co-torrefaction process is observed for product quality with a synergistic effect. Furthermore, the parameters of co-torrefaction, including temperature, reaction time, mass yield, energy yield, and the composition of the H/C and O/C ratio of the co-torrefied materials, are similar to those for coal composition. Different reactor types, such as fixed-bed, fluidized-bed, microwave, and batch reactors, are used for co-torrefaction, in which biomass blends with optimized blend ratios. The co-torrefaction process increases the bio-solid yield and heating value, the capacity to adsorb carbon dioxide, and the renewable fuel used for gasification. One of the objectives of this study is to adopt a process that must be viable, green, and sustainable without generating pollution. For this reason, microwave co-torrefaction (MCT) has been used in many recent studies to transform waste and biomass materials into an alternative fuel using a microwave reactor.

The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) attacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantity and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimization algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimization, RF-GA Optimization, and SVM-GA Optimization methods, employing Evolutionary Algorithms (EAs) Optimization with Tree-based Pipelines Optimization Tool (TPOT)-Genetic Programming. Datasets pertaining to DDoS attacks were utilized to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimized using EAs, achieving remarkable accuracy scores: 99.99% with the XGB-GA method, 99.50% with RF-GA, and 99.99% with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats.

Generation of fluid flow due to simultaneous occurrence of heat and mass diffusions caused by buoyancy differences is termed as double diffusion. Pervasive applications of such diffusion arise in numerous natural and scientific systems. This article investigates double diffusion in naturally convective flow of water-based fluid saturated in corrugated enclosure and containing hybrid nano particles composed of Copper (Cu) and Alumina (Al 2 O 3 ). Impact of uniformly applied magnetic field is also accounted. To produce thermosolutal convective potential circular cylinder of constant radius is also adjusted by providing uniform temperature and concentration distributions. Finite element approach is capitalized to provide solution of utilized governing equations by utilizing Multiphysics COMSOL software. Wide-range of physical parameters are incorporated to depict their influence on associated distributions (velocity, temperature and concentration). Interesting physical quantities like Nusselt number, Sherwood numbers are also calculated against involved sundry parameters. It is note worthily observed that maximum strength of stream lines ( ψ max ) is 3.3 at ϕ = 0 and drops to 1.2 when ϕ is increased to 0.04. Furthermore, in the hydrodynamic case (Ha = 0), it is observed that the velocity field exhibits an increasing trend compared to the hydromagnetic case H a ≠ 0 , which is proved from the attained values of stream-function i.e., | ψ | max = 11 (in the absence of a magnetic field) and | ψ | max = 3.5 (in the presence of a magnetic field). It is revealed from the statistics of Nusselt number that increase in volume fraction of nano particles from 0 to 0.4, heat flux coefficient upsurges up to 7% approximately. Since, present work includes novel physical aspects of thermosolutal diffusion generated due to induction of hybrid nanoparticles in water contained in corrugated enclosure, so this study will provide innovative thought to the researchers to conduct research in this direction.

The thermal degradation kinetics of poly(2-ethyl-2-oxazoline) (PEOX) prepared under different pH conditions were studied. Kinetic assessment was conducted by analyzing the thermal degradation of PEOX. The kinetic parameters were estimated using model-free methods; including Friedman, OFW, KAS and Starink methods and model-fitting method; Combined kinetics (Ck). The investigation revealed that the activation energy of PEOX degradation exhibited variability when prepared under different pH conditions. During the process of conversion, the activation energy for PEOX thermal degradation showed an increase when synthesized under neutral conditions, while it remained consistent when synthesized under alkaline conditions. The synthesis of PEOX in an acidic medium led to a compromised thermal stability, as indicated by a gradual reduction in activation energy during its thermal degradation. By employing artificial neural network (ANN) and advanced classification regression tree (C&RT), the prediction of activation energy's evolution was conducted, taking into account pH, conversion, heating rate, and temperature. The predictive performance of the advanced C&RT model surpassed that of ANN in determining the activation energy of PEOX degradation at different pH levels for different heating rates.

In situ post weld heat treatment (PWHT) by applying a second pulse current during resistance spot welding (RSW) provides a new pathway to alter the microstructure of the fusion zone (FZ) and improves the mechanical performance of the RSW joint. In the present study, effect of the second pulse current on microstructural characteristics and mechanical performance of resistance spot weld joint of HSLA350 steel under were investigated. It was observed that after applying the second pulse current during welding process, it subdivides the initial solidified fusion zone into two zones, namely equiaxed grain zone (EGZ) and columnar grain zone (CGZ). The outer layer becomes EGZ consisting of quasi-equiaxed grains of ferrite and martensite whereas the inner core is CGZ solidified with a columnar grain structure consisting of martensite and some bainite. The refinement of microstructure in the case of double pulse weld resulted in enhanced tensile shear strength and failure energy absorption capacity with ductile pullout failure mode.

Patients with CAD and LV dysfunction were assigned to either medical therapy alone or medical therapy plus CABG. At 5 years, there was no significant difference between the two study groups in the rate of death from any cause. It is estimated that 5.8 million patients in the United States 1 and 15 million in Europe 2 have heart failure. Coronary artery disease is the most common substrate for heart failure in industrialized nations. 3 However, the role of coronary-artery bypass grafting (CABG) in the treatment of patients with coronary artery disease and heart failure has not been clearly established. In three landmark clinical trials in the 1970s, a total of 2234 patients with chronic stable angina were randomly assigned to undergo CABG or receive medical therapy alone. 4 – 6 The findings from these trials led to recommendations supporting the use of CABG . . .