A systematic literature review for network intrusion detection system (IDS)

International Journal of Information Security - With the recent increase in internet usage, the number of important, sensitive, confidential individual and corporate data passing through internet...     

Preparation and characterization of silica nanoparticles as an efficient carrier for two bio-detergents based enzymes

Bio-detergents are new bio-friendly formulas that contain biobased ingredients, including enzymes. In the present study, alkaline protease and α-amylase were immobilized via physisorption onto silica nanoparticles (SNPs). The derivatized SNPs served as major components of a prepared bio-detergent. Alkaline protease was produced by the recombinant Bacillus subtilis cells that carry the protease genes on a multiple-copy plasmid, while α-amylase was commercially purchased. SNPs were prepared by the sol–gel method and well-characterized through the Brunauer–Emmett–Teller (BET) method, scanning electron microscopy (SEM), transmission electron microscopy (TEM), zeta potential, X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy. The adsorption capacity of the SNPs was determined via colorimetry through the adsorption of methylene blue dye (MB), with approximately 97% adsorption achieved under the conditions employed. The Langmuir isotherm well-described the adsorption of MB on SNPs. High immobilization yield for the enzymes was obtained, and the storage stability of SNP-alkaline protease and SNP-α-amylase was good, reaching 65% and 85% of their initial activities after 6 weeks of storage at 4°C, respectively. The immobilized enzymes could be reused for 7 cycles. Additionally, the immobilized enzymes retained residual activity to a greater extent than free enzymes in simulated basic detergent solutions. SNPs containing adsorbed alkaline protease and α-amylase were mixed with a basic detergent solution, and the washing efficiency of some proteinous and starchy stains was examined through Hunter Lab spectrophotometry. The latter experiments demonstrated that the immobilized enzymes performed well during the washing process.     

Machine-learning modeling of hourly potential thermal refuge area: A case study from the Sainte-Marguerite River (Quebec,Canada)

To understand the temporal and spatial variability of thermal refuges, this study focused on modeling potential thermal refuge area (PTRA) at a sub-daily time-step in two tributary confluences of the Sainte-Marguerite River (Canada) during the summers of 2020 and 2021. Aquatic ectotherm species, such as Atlantic salmon (Salmo salar), seek these refuges to avoid heat stress during high summer river temperatures. To investigate the temporal variability of these PTRA, we employed inverse weighted distance interpolation to delineate the hourly area available at both confluences. We then analyzed the impact of the atypical low flow conditions of summer 2021 on the diel cycle of PTRA extremes using the coefficient of variation and the generalized additive model (GAM). Finally, we used four supervised machine-learning regression models and three to five hydrometeorological predictors to estimate hourly PTRA availability: multivariate adaptive splines regression (MARS), GAM, support vector machine regression (SVM), and random forest regression (RF). The results showed that tree-based and kernel-based regression models, RF and SVM, outperformed GAM and MARS. RF had the highest accuracy at both sites, with a relative root mean square error and Nash–Sutcliffe efficiency coefficient (Nash) of 13% and 93%, respectively. Our study discovered that under warm conditions in August 2021, small perennial tributary inflows in combination with low mainstem discharge could create high and constant PTRA at confluences, potentially providing vital thermal refuges for cold-water taxa. These refuges may be especially important at the local level, within a specific stretch or section of the river. Given the decreasing availability of thermal refuges for salmonids, it is crucial to monitor stream temperatures at small spatial and temporal scales using data-driven techniques in order to understand stream temperature heterogeneity at tributary confluences.     

γ-irradiation hardness investigations of (PANI)1−x(Bi2Te3)x composites for thermistor applications

The (PANI)_1−x(Bi₂Te₃)_x composites were successfully synthesized. These composites have a simple physical combination of intrinsic conducting polymer and nanocrystal topological insulator materials which are promising new materials for unusual applications. The influence of γ-irradiation (100 kGy doses) on the performance of the (PANI)_1−x(Bi₂Te₃)_x composites was described by atypical techniques to assess the radiation hardness of the samples. X-ray diffraction and Raman spectroscopy measurements show the semi-crystalline phases, the other structural parameters, and the phonon modes. The compositional analysis and the morphological properties were investigated by energy-dispersive X-ray, while transmission electron microscopy imaging confirmed the morphology of the dopants of Bi₂Te₃ nanoparticles into the polyaniline (PANI) matrix, which shows the accumulation of the particles. Thermogravimetric analysis figured out the degradation behavior of the compositions. The representative electron spin resonance (ESR) signal exhibits a narrow single-line ESR spectrum for PANI/Bi₂Te₃ nanoparticle concentrations. (PANI)_1−x(Bi₂Te₃)_x illustrates unexpected ferromagnetic behavior that confirms the magnetic performance of the samples without introducing any magnetic dopants. The obtained materials showed promising performance for using such hybrid materials as a positive temperature coefficient thermistor.