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Simulation and experimental study of shell-kernel separation device for household sunflower based on CFD-DEM coupling
Protein kinase C epsilon activation improves early survival in an acute porcine model of controlled hemorrhage
Author Correction: Effect of stearic acid and sodium stearate on hydrophobicity of nano calcium carbonate and mechanism of water vapor adsorption
Optimizing deep belief network for concrete crack detection via a modified design of ideal gas molecular dynamics
Research on the impact of smart logistics on the the manufacturing industry chain resilience
Performance analysis of hyperbolic graded topological resonator for biosensing applications
Abstract In this manuscript, a novel hyperbolic-graded nano-photonic resonator structure based on the photonic crystal is proposed for biosensing applications. The graded refractive index is realized by considering a porous silicon material having a deliberately modulated local refractive index. The introduction of grading effectively modifies its dispersion characteristics. These crystals exhibit overlapping bandgaps and opposite Zak phases, enabling the manifestation of unique topological properties. The design parameters are optimized to excite a topological edge state at a 1521 nm operating wavelength, whereas a resonating TES is excited at a 1533 nm. The structure performance is analyzed using the finite element method. The analytical results exhibit an improved sensitivity of 1806 nm/RIU (refractive index unit) and a Figure of Merit (FOM) of 4030 RIU−1, which are 151% and 2483% higher than recently reported values. With its remarkable performance metrics, the proposed device holds significant promise for accurately detecting and sensing biochemical samples with very high efficiency.
Enhancing malware detection with feature selection and scaling techniques using machine learning models
Abstract The increasing prevalence of malware presents a critical challenge to cybersecurity, emphasizing the need for robust detection methods. This study uses a binary tabular classification dataset to evaluate the impact of feature selection, feature scaling, and machine learning (ML) models on malware detection. The methodology involves experimenting with three feature scaling techniques (no scaling, normalization, and min-max scaling), three feature selection methods (no selection, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA)), and twelve ML models, including traditional algorithms and ensemble methods. A publicly available dataset with 11,598 samples and 139 features is utilized, and model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Results reveal that the Light Gradient Boosting Machine (LGBM) achieves the highest accuracy of 97.16% when PCA and either min-max scaling or normalization are applied. Additionally, ensemble models consistently outperform traditional ML models, demonstrating their effectiveness in enhancing malware detection. These findings offer valuable insights into optimizing preprocessing and model selection strategies for developing reliable and efficient malware detection systems.