Duranbusch5322

Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious heart failure event such as stroke, heart attack, and myocardial infraction. In Qatar, there is a lack of studies focusing on CVD diagnosis based on non-invasive methods such as retinal image or dual-energy X-ray absorptiometry (DXA). In this study, we aimed at diagnosing CVD using a novel approach integrating information from retinal images and DXA data. We considered an adult Qatari cohort of 500 participants from Qatar Biobank (QBB) with an equal number of participants from the CVD and the control groups. We designed a case-control study with a novel multi-modal (combining data from multiple modalities-DXA and retinal images)-to propose a deep learning (DL)-based technique to distinguish the CVD group from the control group. Uni-modal models based on retinal images and DXA data achieved 75.6% and 77.4% accuracy, respectively. The multi-modal model showed an improved accuracy of 78.3% in classifying CVD group and the control group. We used gradient class activation map (GradCAM) to highlight the areas of interest in the retinal images that influenced the decisions of the proposed DL model most. It was observed that the model focused mostly on the centre of the retinal images where signs of CVD such as hemorrhages were present. This indicates that our model can identify and make use of certain prognosis markers for hypertension and ischemic heart disease. From DXA data, we found higher values for bone mineral density, fat content, muscle mass and bone area across majority of the body parts in CVD group compared to the control group indicating better bone health in the Qatari CVD cohort. This seminal method based on DXA scans and retinal images demonstrate major potentials for the early detection of CVD in a fast and relatively non-invasive manner.Unpredictable web temperature distributions in the dryer and strain deviations in the cross-machine (CMD) and machine (MD) directions could hamper the manufacture of smooth functional layers on polymer-based webs through the roll-to-roll (R2R) continuous process system. However, research on this topic is limited. In this study, we developed a structural analysis model using the temperature distribution of the web as a boundary condition to analyze the drying mechanism of the dryer used in an R2R system. Based on the results of this model, we then applied structural modifications to the flow channel and hole density of the aluminum plate of the dryer. The model successfully predicted the temperature and strain distributions of the web inside the dryer in the CMD and MD by forming a tension according to the speed difference of the driven rolls at both ends of the span. Our structural improvements significantly reduced the temperature deviation of the moving web inside the dryer by up to 74% and decreased the strain deviation by up to 46%. The findings can help prevent web unevenness during the drying process of the R2R system, which is essential to minimize the formation of defects on functional layers built over polymer-based webs.This study investigated eco-friendly antibacterial medical protective clothing via the nonwoven process and characteristic evaluations. Firstly, Tencel® fibers and low melting point polyester (LMPET) fibers (re-sliced and granulated from recycled PET bottles) were mixed at different ratios and then needle punched at diverse needle rolling depths. The influences of manufacturing parameters on the Tencel®/LMPET nonwoven fabrics were examined in terms of mechanical properties, water vapor transmission rate, and stiffness. Next, Tencel®/LMPET nonwoven fabrics were combined with thermoplastic polyurethane (TPU)/Triclosan antibacterial membranes that contained different contents of triclosan using melt processing technology. The resulting Tencel®/LMPET/TPU/Triclosan composites were characterized via different measurements; an optimal bursting strength of 86.86 N, an optimal horizontal tensile strength of 41.90 N, and an optimal stiffness along the MD and CD of 8.60 cm were recorded. Furthermore, the Tencel®/LMPET/TPU/Triclosan composites exhibited a distinct inhibition zone in the antibacterial measurement, and the hydrostatic pressure met the requirements of the EN 141262003 and GB 19082-200 disposable medical protective gear test standards.Serviceability limit states are very important in the design of reinforced concrete elements but they are complex to calculate. Simplified serviceability calculations are provided in EN 1992-1-1 (2013) for steel reinforced elements. The crack widths are assumed to be acceptable if the bar diameters or bar spacings are not too large, while deflections are acceptable if the slenderness is not too large. In recent decades, FRP bars have become an adequate replacement for steel bars, especially in aggressive environments. The calculation procedures for FRP-reinforced concrete elements (FRPRC) were developed from calculation methods for steel reinforced elements. The first part of this paper demonstrates the procedures and parametric investigation for calculating the maximum bar diameter and bar spacing for the purpose of controlling the crack width, focusing on calculations for the maximum bar diameter for which cracks widths are acceptable. The second part of the paper demonstrates the procedures and parametric that class of concrete.High thermal conductivity polymer matrix composites have become an urgent need for the thermal management of modern electronic devices. However, increasing the thermal conductivity of polymer-based composites typically results in loss of lightweight, flexibility and electrical insulation. Herein, the polyvinyl alcohol (PVA)/PVA-chitosan-adsorbed multi-walled carbon nanotubes/PVA (PVA/CS@MWCNTs) composite films with a sandwich structure were designed and fabricated by a self-construction strategy inspired by the surface film formation of milk. The obtained film simultaneously possesses high thermal conductivity, electrical insulation, and excellent flexibility. In this particular structure, the uniform intermediate layer of PVA-CS@MWCNTs contributed to improving the thermal conductivity of composite films, and the PVA distributed on both sides of the sandwich structure maintains the electrical insulation of the films (superior electrical resistivity above 1012 Ω·cm). It has been demonstrated that the fillers could be arranged in a horizontal direction during the scraping process. B02 Thus, the obtained composite film exhibited high in-plane thermal conductivity of 5.312 W·m-1·K-1 at fairly low MWCNTs loading of 5 wt%, which increased by about 1190% compared with pure PVA (0.412 W·m-1·K-1). This work effectively realizes the combination of high thermal conductivity and excellent electrical insulation, which could greatly expand the application of polymer-based composite films in the area of thermal management.The laser-assisted melt electrospinning (LES) method was utilized for the preparation of poly(L-lactide-co-ε-caprolactone) (PLCL) fibers. During the process, a carbon dioxide laser was irradiated, and voltage was applied to the raw fiber of PLCL. In situ observation of fiber formation behavior revealed that only a single jet was formed from the swelling region under the conditions of low laser power and applied voltage and feeding rate, whereas multiple jets and shots were produced with increases in these parameters. The formation of multiple jets resulted in the preparation of thinner fibers, and under the optimum condition, an average fiber diameter of 0.77 μm and its coefficient of variation of 17% was achieved without the formation of shots. The estimation of tension and stress profiles in the spin-line was also carried out based on the result of in situ observation and the consideration that the forces originated from surface tension, electricity, air friction, and inertia. The higher peak values of tension and stress appearing near the apex of the swelling region corresponded to the formation of thinner fibers for the condition of single-jet ejection. Analyses of the molecular orientation and crystallization of as-spun fibers revealed the formation of a wide variation of higher order structure depending on the spinning conditions.Self-assembly of amphiphilic polymers with hydrophilic and hydrophobic units results in micelles (polymeric nanoparticles), where polymer concentrations are above critical micelle concentrations (CMCs). Recently, micelles with metal nanoparticles (MNPs) have been utilized in many bio-applications because of their excellent biocompatibility, pharmacokinetics, adhesion to biosurfaces, targetability, and longevity. The size of the micelles is in the range of 10 to 100 nm, and different shapes of micelles have been developed for applications. Micelles have been focused recently on bio-applications because of their unique properties, size, shape, and biocompatibility, which enhance drug loading and target release in a controlled manner. This review focused on how CMC has been calculated using various techniques. Further, micelle importance is explained briefly, different types and shapes of micelles are discussed, and further extensions for the application of micelles are addressed. In the summary and outlook, points that need focus in future research on micelles are discussed. This will help researchers in the development of micelles for different applications.Geopolymers might be the superlative alternative to conventional cement because it is produced from aluminosilicate-rich waste sources to eliminate the issues associated with its manufacture and use. Geopolymer composites (GPCs) are gaining popularity, and their research is expanding. However, casting, curing, and testing specimens requires significant effort, price, and time. For research to be efficient, it is essential to apply novel approaches to the said objective. In this study, compressive strength (CS) of GPCs was anticipated using machine learning (ML) approaches, i.e., one single method (support vector machine (SVM)) and two ensembled algorithms (gradient boosting (GB) and extreme gradient boosting (XGB)). All models' validity and comparability were tested using the coefficient of determination (R2), statistical tests, and k-fold analysis. In addition, a model-independent post hoc approach known as SHapley Additive exPlanations (SHAP) was employed to investigate the impact of input factors on the CS of GPCs. In predicting the CS of GPCs, it was observed that ensembled ML strategies performed better than the single ML technique. The R2 for the SVM, GB, and XGB models were 0.98, 0.97, and 0.93, respectively. The lowered error values of the models, including mean absolute and root mean square errors, further verified the enhanced precision of the ensembled ML approaches. The SHAP analysis revealed a stronger positive correlation between GGBS and GPC's CS. The effects of NaOH molarity, NaOH, and Na2SiO3 were also observed as more positive. Fly ash and gravel size 10/20 mm have both beneficial and negative impacts on the GPC's CS. Raising the concentration of these ingredients enhances the CS, whereas increasing the concentration of GPC reduces it. Gravel size 4/10 mm has less favorable and more negative effects. ML techniques will benefit the construction sector by offering rapid and cost-efficient solutions for assessing material characteristics.