Techno India

In this study, thermal conductivity is analyzed as a relative quantity that depends on the inertial frame from which it is observed. That is, the measured value of thermal conductivity changes when the velocity of the observer differs. The analysis reveals that if the heat flow velocity is relatively higher in the moving frame compared to the rest frame, then the observed conductivity also increases. Conversely, if the heat flow velocity is higher in the rest frame, then the apparent conductivity becomes comparatively lower. This varying behavior depends on both the relative velocity \(\:v\) and the heat flow velocities \(\:u\) and \(\:{u}^{{\prime\:}}.\) Furthermore, by comparing the heat flow velocities in two different frames, the concept of relative velocity addition can also be explained. As a result, it becomes evident that thermal conductivity is not merely a material property, but rather a transformable quantity dependent on relative motion.This theory may provide significant insights into high-speed heat conduction processes and can contribute to modern astrophysical and physical research.

In recent days, medical science is considering the disease cancer as the most challenging one having significant mortality rate. It is observed that some gene expression levels vary significantly when normal cells turn into cancer cells. The identification of cancer-mediating genes is crucial for understanding cancer progression, diagnosis, and the development of targeted therapies. Traditional statistical methods and machine learning models often struggle with the inherent uncertainty and noise in biological data. The Random Forest (RF) technique is nowadays being used in the field of bioinformatics which ensembles decision trees and incorporates feature selection. This paper presents a novel approach which combines statistical method Z-test followed by Fuzzy Random Forest (FRF) algorithm to identify cancer-mediating genes. By integrating fuzzy logic with the Random Forest model, FRF effectively handles the uncertainty in gene expression data, leading to more robust and interpretable results. We apply this method to a dataset of gene expression profiles from cancerous and normal tissues, demonstrating its superiority over traditional methods in identifying key cancerrelated genes. The accuracy of the model has been established with the F1 score, accuracy score.

This research extends the classical concept of thermal conductivity into the framework of special relativity, presenting a new relativistic formulation. In the classical case, thermal conductivity is treated as a fixed material property, but under high-speed relativistic conditions its value may vary between frames. To establish this, we begin with the classical one-dimensional heat conduction law and reformulate it in terms of mass and velocity. Using the relativistic mass relation, we then derive a general transformation between the thermal conductivity in the rest frame (𝑘) and that in the moving frame (𝑘 ′ ). The analysis shows that thermal conductivity is not invariant in the classical sense, but becomes framedependent. When the heat flow is parallel to the motion of the frame, it is always found that 𝑘 ′ > 𝑘, indicating an enhancement of conductivity in the moving frame. In contrast, when the heat flow is opposite to the motion, both possibilities— 𝑘 ′ > 𝑘 or 𝑘 > 𝑘′—can arise, depending on the heat flow velocity and the relative velocity of the frames, or equivalently, from the Lorentz factor. Thus, relativistic motion imparts a dual character to thermal conductivity that has no analogue in the classical framework. These findings suggest the necessity of a relativistic extension of Fourier’s law, which would be essential for accurate modeling of heat transfer in high-speed engineering systems, plasma physics, particle accelerators, and astrophysical processes. The study establishes that the relation between 𝑘 and 𝑘′ is not merely material-dependent, but instead emerges as a fundamental physical property governed by relativistic motion and velocity transformations.

This paper presents a novel decision-making method for face recognition where the features were extracted from the original image fused with its corresponding true and partial diagonal images. To extract features, we adopted the generalized two-dimensional FLD (G2DFLD) feature extraction technique. The feature vectors from a test image are given as input to neural network-based classifier. It is trained with the feature vectors of original image and diagonally fused images and thereby the merit weights with respect to different classes were generated. To address the factors that affect the face recognition accuracy and uncertainty related to raw biometric data, a fuzzy score for each of the classes is generated by treating a type-2 fuzzy set. This type-2 fuzzy set is formed by the feature vectors of both the diagonally fused training samples and the test image of the respective classes. A concluding score for each of the classes under consideration is computed by fusing complemented merit weight with the complemented fuzzy score. These class-wise concluding scores are considered in the face recognition process. In this study, the well-known face databases (AT&T, UMIST and CMU-PIE) are used to evaluate the performance of the proposed method. The experimental results illustrate the fact that the proposed method has exhibited superior classification precision as compared with other state-of-art methods. Our T2FMFI(mg)F method achieves highest face recognition accuracies of 99.41%, 98.36% and 89.80% in case of AT&T, UMIST and CMU-PIE (with expression), respectively while for CMU-PIE (with Light) the highest recognition accuracy is 97.957%. In addition to it, the presented method is quite successful in fusing and classifying textural information from the original and partial diagonal images by integrating them with type-2 fuzzy set-based treatment.

Chromatography is a method for the isolation of a mixture into separate sections. It is usually based on the principle of partition of solute between two phases. It usually consists of a mobile phase and a stationary phase. The advancement of the mobile process in the planar stationary stage is evoked by different external or internal forces. Capillarity and gravity are the intrinsic force whereas external forces are electric field, pump, and centrifugal forces. Thin layer chromatography and high-pressure thin layer chromatography are based on the adsorption theory. Forced flow thin layer chromatography is also based on the adsorption principle. FFTLC is an authoritative method of separation that uses a solvent pump to extract the sample mixture into a pressurized ultra-micro cavity containing an adsorbent analytical or preparative planer bed. FFTLC needs no or slight sample preparation as in TLC. One relation between FFTLC and high-pressure liquid chromatography is that as in HPLC, the maximum layer length is used for analytical and preparatory separation. FFTLC is a process in which continuous production and continual evaporation of the mobile phase from the end of the chromatography plate produces forced-flow. Here, we focus on FFTLC, an advanced and more sophisticated chromatographic technique.