The aim of this research is to develop high carbon-yielding biochar from pinewood, coffee husk, sugarcane bagasse, and maize cob and to characterize the biochar/HDPE composites for electromagnetic (EM) shielding application. During the biochar/HDPE composites fabrication, slow pyrolysis and compression molding manufacturing were used. The enhanced properties characterizations were conducted by using thermogravimetric analysis (TGA), scanning electron microscopy (SEM), differential thermal analysis (DTA), Fourier transform spectrometry (FTIR), Brunauer-Emmet-Teller (BET) analysis, digital multi-meter, and proximity analysis. The results of biochar pyrolysis showed the maximum carbon yield of 74.6 %, 68.9 %, 68.4 %, and 40 % for pine wood, maize cob, sugarcane bagasse, and coffee husk respectively. The BET analysis showed the maximum specific surface area (734.5 m²/g), pore volume (0.2364 cm³/g), and pore radius (9.897 Å) from the pine wood biochar. The biochar loading analysis results showed that the 30 % and 40 % pine wood biochar significantly enhanced the electrical conductivity, thermal conductivity, thermal stability, crystallinity, and EM shielding effectiveness (SE) of the biochar/HDPE composites. In particular, the biochar/HDPE composite with 30 wt% pine wood biochar showed the highest thermal conductivity of 2.219 W/mK, and the 40 wt% pine wood biochar/HDPE composite achieved the highest electrical conductivity of 4.67 × 10⁻⁷ S/cm and EM SE of 44.03 dB at 2.1 GHz.

This paper investigates the mathematical modelling of cybercrime attacks on multiple devices connected to the server. This model is a very successful way for cybercrime, bio-mathematics, and artificial intelligence to investigate and comprehend the behaviour of mannerisms with harmful intentions in a computer system. In this computational model, we are studying the factors (i.e., computer viruses, disease infections, and cyberattacks) that affect connected devices. This compartmental model, SEIAR, represents the various hardware utilised during the cyberattack. The letters S, E, I, A, and R are used to represent different stages or groups of individuals in epidemiological models, helping to understand the spread and control of infectious diseases. The dynamics of the previous model are determined by a series of differential equations. The dynamics of the preceding model are determined by a system of differential equations. Numerical solutions of the model are calculated using backpropagated Levenberg-Marquardt algorithm (BLMA) and a specific optimization algorithm known as the Levenberg-Marquardt algorithm (LMA). Reference solutions were obtained by using the Runge-Kutta algorithm of order 4 (RK-4). The backpropagated Levenberg-Marquardt algorithm (BLMA), commonly known as the damped least-squares (DLS) method. Subsequently, we endeavor to analyze the surrogate solutions obtained for the system and determine the stability of our approach. Moreover, we aim to ascertain fitting curves to the target solutions with minimum errors and achieve a regression value of 1 for all the predicted solutions. The outcome of our simulations ensures that our approach is capable of making precise predictions concerning the behavior of real-world phenomena under varying circumstances. The testing, validation, and training of our technique concerning the reference solutions are then used to determine the accuracy of the surrogate solutions obtained by BLMA. Convergence analysis, error histograms, regression analysis, and curve fitting were used for each differential equation to examine the robustness and accuracy of the design strategy.

1. Hydropower is the most abundantly available renewable energy source that can be used for electricity generation for many nations. It is the leading source of renewable energy worldwide covering ...