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Data-driven framework for prediction of mechanical properties of waste glass aggregates concrete
Performance of polyvinyl alcohol graphene oxide membrane for microplastic removal in wastewater with an IoT based monitoring approach
Study of optimization of process parameters on the wear behaviour of Al7075–aluminium oxide composites using Taguchi approach
Abstract Aluminium alloy based composites are employed in numerous applications that require outstanding performance due to their superior mechanical characteristics, including higher strength, stiffness, and wear resistance. They are used in engine parts like pistons and connecting rods to improve performance and durability. In this work, Al7075 is employed as the matrix material. Aluminium oxide particulates were chosen as the reinforcing particles. The Al7075–6%Al2O3 composites were manufactured using the stir casting technology. Scanning electron microscopic instrument was employed to investigate the microstructure of Al7075–6%Al2O3 composites. The microstructure analysis of Al7075–6%Al2O3 composites revealed the even dispersion of Al2O3 particulates throughout the Al7075 matrix. The Pin on disc apparatus was utilized to conduct a wear experiment on Al7075–Al2O3 composites. Taguchi methodology was employed to optimize the wear process factors of the produced composites for enhanced performance. According to ANOVA outcomes, the most impacting factor was the sliding distance 87.057% then speed 7.165% and lastly load 0.435%. The R-Sq value and R-Sq (adj) value for wear response obtained using Minitab 16 Taguchi software are 95.05% and 92.08% respectively. The delta values for load, speed and sliding distance are 0.668, 2.830 and 10.734 respectively. The results of this demonstrated that the factor that has the greatest impact is sliding distance. The wear response values provided by OA experimental and regression equation are 3.6131 × 10−3 mm3/m and 3.3062 × 10−3 mm3/m respectively. A difference of 8.49% between the experimental and Taguchi analysis value gives the maximum permissible difference.
Predicting cisplatin response in cholangiocarcinoma patients using chromosome pattern and related gene expression
Research on dimension measurement algorithm for parcel boxes in high-speed sorting system
An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images
Abstract Ovarian Cancer is a malignancy that develops from ovarian cells and is frequently characterized by aberrant cell proliferation that leads to the creation of tumors within the ovaries. The high death rate and often delayed discovery of Ovarian Cancer make it a serious healthcare concern. Due to the annual 207,000 fatalities and 314,000 new cases worldwide, Ovarian Cancer poses a serious threat to public health, making quick and precise detection and classification techniques more essential. This work discusses the importance of Ovarian Cancer diagnosis and presents a new model for Ovarian Cancer classification. It also showcases a comparative analysis with other state-of-the-art models for Ovarian Cancer. Using an Ovarian Cancer image dataset which has data samples named Clear Cell, Endometri, Mucinous, Serous, and Non-Cancerous, it compares the proposed OvCan-FIND model to a wide range of CNN-based architectures, such as Inception V3, different EfficientNet variants, ResNet152V2, MobileNet, MobileNetV2, VGG16, VGG19, and Xception. The study examines the most recent Ovarian Cancer classification algorithms in this context to increase prognosis and diagnostic accuracy; our proposed OvCan-FIND model outperforms base models with an exceptional accuracy of 99.74%. This model presents significant prospects for enhancing ovarian cancer early identification and diagnosis, which will ultimately enhance patient outcomes.
Insights of the asynchronous strain evolution of lean coal in uniaxial compression creep
Toll like receptor 2 mediated exacerbation of sepsis associated acute kidney injury by renal congestion in mice
Fibrosis: cross-organ biology and pathways to development of innovative drugs
Molecular basis for presentation of N-myristoylated peptides by the chicken YF1∗7.1 molecule
The effect of Animal-assisted therapy on prosocial behavior and emotional regulation in autistic children with varying verbal abilities: A pilot study
Background The use of animal-assisted therapy (AAT) has increased in the pediatric autism population. However, studies detailing differences in human-animal interaction between autistic children and animals along with the longevity of reported outcomes associated with AAT need further exploration. The purpose of this pilot study was to evaluate these factors. Methods A quantitative research design with convenience sampling was used to categorize pediatric participants into two groups (nonverbal or verbal) based on their verbality. Two human-animal ethogram and two questionnaires were utilized to assess behavior during and apart from AAT sessions. A total of 2,281 interactions and behaviors occurring during AAT sessions were examined. Results Both groups interacted well with the canine. The verbal group interacted mostly with commands while the nonverbal group showed more affectionate behaviors. Conclusion Mental health practitioners can use canines to enhance therapeutic outcomes in autistic children regardless of the child’s verbality.
Artificial intelligence outperforms humans in morphology-based oocyte selection in cattle
Dynamic load balancing in cloud computing using predictive graph networks and adaptive neural scheduling
Experimental study of inductively coupled plasma etching of patterned single crystal diamonds
Integrated approach of extreme learning machines and locally weighted linear regression for improved discharge coefficient prediction
Abstract Accurate determination of the discharge coefficient (Cd) is essential for calculating discharge over side weirs. The current study aims to enhance the prediction accuracy of Cd for rectangular sharp-crested side weirs by addressing the limitation of the output layer of the Extreme learning machine (ELM). The output layer of ELM depends mainly on the linear system which limits its generalization capabilities. Therefore, this study uses Locally Weighted Linear Regression (LWLR) with radial basis kernel function instead of the linear system to effectively capture nonlinear relationships and enhance local data pattern recognition. The proposed model (ELM-LWLR) has been validated against classic multiple linear regression (MLR), ELM, LWLR, and Extreme Gradient Boosting (XGBoost). The quantitative results showed that the ELM-LWLR model has a superior performance, achieving higher prediction accuracy with a correlation coefficient of 0.968, and percentage bias (PBIAS) of -0.130%. Moreover, the accuracy of Cd prediction using the ELM-LWLR model improved by 37.21% compared to LWLR, 28.95% compared to XGBoost, 48.08% compared to ELM, and 64.94% compared to MLR. Additionally, sensitivity analysis identified the ratio of weir height to length and dimensionless length as critical factors affecting Cd estimation. Overall, the findings demonstrate that the ELM-LWLR model is a practical and robust tool for Cd modeling, offering significant advantages in cost reduction and enhanced hydraulic modeling for complex engineering applications.
RETRACTED ARTICLE: Artificial intelligence-augmented smart grid architecture for cyber intrusion detection and mitigation in electric vehicle charging infrastructure
Impact of industrial activities on physicochemical properties, mineralogical, and elemental composition in sediments of Puliyanthangal lake, Ranipet, India
Some new QEC MDS codes with large minimum distance
Abstract The advancement of Quantum Error-Correcting (QEC) Maximum Distance Separable (MDS) codes holds substantial importance in practical applications, substantially augmenting the reliability and efficiency of quantum communication and computing. This paper introduces two new classes of QEC MDS codes, which are devised through the utilization of generalized Reed–Solomon (GRS) codes and the Hermitian construction approach. The novelty of our QEC MDS codes lies in their parameters being distinct from all previously reported codes. Moreover, most of our codes possess a considerably greater minimum distance in comparison to existing codes of the same length.