Effect of weathering on physical and mechanical properties of hybrid nanocomposite based on polyethylene, woodflour and nanoclay

Wood plastic composites have received increasing attention during the last decades, because of many advantages related to their use. However the durability of Wood plastic composites after ultraviolet exposure has become a concern. In this research, hybrid nanocomposites of polyethylene and woodflour with different concentrations of nanoclay were fabricated using melt compounding followed by injection molding. Specimens were exposed for 2000 h to ultraviolet radiation and moisture cycling in a laboratory weathering device to simulate the effects of exposure to sunlight and rain. Physical and mechanical properties of the nanocomposites were evaluated, before and after weathering. The results indicated that the water absorption of wood plastic composites increased after weathering but nanoclay reduced the intensity of weathering to some extent, through decreasing of water absorption. Also results showed that weathering decreased modulus of elasticity values, however good dispersion of clay layers resulted in fewer drop of modulus of elasticity values. Fourier transform infrared spectroscopy showed that lowest carbonyl index is related to the nano wood plastic composites with 2wt% nanoclay. Also X-Ray diffraction patterns revealed that intercalation morphology has been formed for nano particles

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

Precision diagnostics in cardiac tumours: Integrating echocardiography and pathology with advanced machine learning on limited data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25 % and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94 % in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

Effect of weathering on physical and mechanical properties of hybrid nanocomposite based on polyethylene, woodflour and nanoclay

Wood plastic composites have received increasing attention during the last decades, because of many advantages related to their use. However the durability of Wood plastic composites after ultraviolet exposure has become a concern. In this research, hybrid nanocomposites of polyethylene and woodflour with different concentrations of nanoclay were fabricated using melt compounding followed by injection molding. Specimens were exposed for 2000 h to ultraviolet radiation and moisture cycling in a laboratory weathering device to simulate the effects of exposure to sunlight and rain. Physical and mechanical properties of the nanocomposites were evaluated, before and after weathering. The results indicated that the water absorption of wood plastic composites increased after weathering but nanoclay reduced the intensity of weathering to some extent, through decreasing of water absorption. Also results showed that weathering decreased modulus of elasticity values, however good dispersion of clay layers resulted in fewer drop of modulus of elasticity values. Fourier transform infrared spectroscopy showed that lowest carbonyl index is related to the nano wood plastic composites with 2wt% nanoclay. Also X-Ray diffraction patterns revealed that intercalation morphology has been formed for nano particles

Investigation of applying the old corrugated container (OCC) and aspen chips in particleboard production

In this study, aspen chips with OCC (Old Corrugated Container) were used in particleboard production and samples were prepared at two different levels of resin contents (%9 and %10) and three levels of combination: 1- %25 OCC +%75 aspen,       2- %50 OCC + %50 aspen,        3- %75 OCC +%25 aspen. Indeed in this study these two mentioned factors are variable and other factors such as press temperature: 165 ºC, press time: 5 minute, mat moisture %12, board density 0.75 g/cm3 and press pressure 30 kg/cm2 were constant. After the boards were manufactured according to DIN-68763 standard, were undergone different tests such as: bending strength, modulus of elasticity, internal bonding strength and thickness swelling after 2 & 24 hours immersion in water. The results indicate that second ratio(%50 OCC and %50 aspen chips) is proper for bending applications and the first ratio(%25 OCC and %75 aspen chips) is proper for tensile and thickness swelling applications. Therefore, applying the OCC more than %50 in manufacturing combination; result in decreasing the physical and mechanical properties. Moreover the results show that when the resin content increases, the board features improve

Effect of weathering on the properties of hybrid composite based on polyethylene, woodflour, and nanoclay

Hybrid composites of polyethylene/wood flour/nanoclay with different concentrations of nanoclay were fabricated using melt compounding followed by injection molding. Composites were weathered in a xenon-arc type accelerated weathering apparatus for 2000 h. Physical properties of the composites were evaluated by colorimetery and water absorption before and after weathering. Changes in surface chemistry were monitored using spectroscopic techniques. The results indicated that water absorption of the composites increased after weathering, but nanoclay can reduce the intensity of weathering to some extent by decreasing water absorption. Weathering increased the degree of color change and lightness of the samples; however, the lightness of the samples containing nanoclay was less than that of neat wood-plastic composites. Fourier transform infrared spectroscopy revealed a lower carbonyl index of composites containing nanoclay. X-ray diffraction patterns revealed that the nanocomposites formed were intercalated. The order of intercalation for samples containing 2 wt% nanoclay was higher than that of 4 wt% at the same maleic anhydride grafted polyethylene content, due to some agglomeration of the nanoclay

Characterization of Reservoir Heterogeneity by Capacitance-resistance Model in Water-flooding Projects

Tedious calculations and simulations are needed to obtain an efficient production scenario and/orproper field development strategy. Capacitance-resistance model (CRM) is proved to be a fastreservoir simulation tool using just the field-available data of production and injection rates. Thisapproach sets a time-constant and a weighting factor (or well-pair connectivity parameter) betweeneach pair of injection and production wells according to their histories. In this study, we investigatedthe behavior of the CRM parameters in synthetic reservoir models with different porosity andpermeability maps. Four reservoirs are considered with different porosities and permeabilities to studytheir effects on CRM response. We defined a new parameter, named error to mean production ratio(EMPR), to analyze the CRM performance. Some fluctuations are exerted on the production data toevaluate the capability of CRM against variable production records. Porosity showed a stronger effecton CRM parameters than the permeability based on the calculated EMPR. Unstable productionhistory would result in large error which can be corrected with some smoothing techniques onvariable production data. Also, a linear trend of EMPR was obtained with the change of porosity andpermeability or a combination of the two parameters within the reservoir