In recent years, viral diseases have exhibited a significant incidence of infections and fatalities. The analysis of viral genomic sequences can be efficacious in evaluating the present and potentially forthcoming condition of viruses. Considering the importance of the internal structure of the cell and the nucleotide sequences within it, analyzing nucleotide sequences can provide a range of discussable features. On the other hand, it has been demonstrated that the use of graph algorithms and machine learning in the analysis and examination of virus samples and even viral variants can yield beneficial results. This study proposes a novel approach that utilizes complex networks and probabilistic graph modeling methods to analyze viral genomic sequences for feature extraction. The proposed approach, which relies on the PageRank centrality algorithm, operates on codons that are associated with the nucleotide sequences. Experiments with machine learning algorithms were conducted on multiple datasets of viruses and various variants of coronavirus and influenza viruses. The use of a decision tree classifier model on the extracted distinguishing features enabled the differentiation of coronavirus samples from other samples. The high discriminative capability of the graph node centrality feature played a significant role in these experiments, establishing a meaningful connection with genetic concepts as well. The decision tree classifier applied on 173,228 genomic sequence samples originating from 30 distinct virus types, showed a remarkable accuracy rate of 99.73%. The proposed algorithm was successfully tested on several types of viruses, and the interpretability of the extracted features also enabled its structural analysis. The use of a graph-based approach on genetic features containing information about the internal structure of nucleotides yielded results that could be significant for the identification of any type of virus or specific viral variant.

We present a dual-band, polarization-independent dielectric terahertz absorber exhibiting near-perfect absorption and minimal reflection at frequencies of f ₁ = 419 THz and f ₂ = 528 THz. Using electromagnetic theory, we modeled the structure to derive the surface electric admittance and magnetic impedance of the metasurface, elucidating the conditions required for perfect absorption in terms of inverse electric and magnetic polarizabilities. The absorber features a tunable symmetrical design, facilitating precise frequency adjustment by modifying structural parameters and ensuring polarization independence for perpendicularly incident electromagnetic waves. This scalable and versatile absorber, constructed from readily available materials, is optimally suited for applications in resource detection, imaging, sensing, and medical diagnostics, attributed to its simplicity, cost-effectiveness, and high performance.

Detecting multivariate interactions between the variables of a problem is a challenge in traditional genetic algorithms (GAs). This issue has been addressed in the literature as the linkage learning problem. It is widely acknowledged that the success

Optical sensors getting widespread usage in almost every field, especially industries. A high thermal optical sensor is proposed to predict the environmental temperature in power plants. A high sensitivity, accuracy, low cost, compact size, linear operation, and suitable transmission coefficient optical sensor in a wide thermal range is achieved that changing the surrounding temperature has a severe effect on the conductivity of graphene which changes the transmission power of the sensor. In this paper, the position of the graphene sheet affects the interaction of light and graphene, effectively. A maximum sensitivity of 17.47% is achieved in K temperature deviation. The Cylindrical diameter is 4.1 lambda, the smallest in size among the references checked and has the desired performance up to 2000 K, indicating the suitable efficiency of the sensor and the fibre optic sensor with graphene coating has great potential in the field of measurement, especially in the temperature of the surrounding atmosphere in the industry.