In the present work, a chemical polymerization route was employed to synthesize polyaniline (PANI) with tailored properties through strategic variations in dopants and surfactants. The structural evolution induced by these modifications was systematically tracked via powder X-ray diffraction (pXRD), and the crystallite size estimated from XRD analysis was found to vary in the range of 1.4–9.9 nm, confirming the nanocrystalline nature of the synthesized PANI samples. Morphological analyses using scanning electron microscopy (SEM) and revealed particle sizes in the range of 30–85 nm, indicating the formation of agglomerated nanostructures composed of smaller crystallites, while energy-dispersive x-ray spectroscopy (EDS) confirmed phase purity. Transmission electron microscopy (TEM) confirmed distinctive particle size, surface features, and polycrystalline nature and SEM analysis Fourier transform infrared (FTIR) and Raman spectroscopy were used to identify functional groups and probe the chemical structure, respectively. UV-Visible spectroscopy indicated direct and indirect bandgap energies in the ranges of 2.4–2.8 eV and 1.41–1.47 eV, respectively reflecting tunable optoelectronic characteristics. Thermal stability was analyzed by thermogravimetric analysis (TGA/DSC). The key finding of this work lies in establishing a clear correlation between specific dopant–surfactant combinations and enhanced electrical performance. Impedance analysis demonstrated that PANI doped with HCl, and PANI incorporating H₂SO₄ as a dopant with DBSA as a surfactant exhibited the highest electrical conductivity, depicting values of 1.58 × 10⁻⁴ S/cm and 4.09 × 10⁻⁴ S/cm, respectively. This systematic approach provides new insights into the design of highly conductive PANI-based materials for applications in sensors, flexible electronics, and energy storage devices.
Social media platforms have become central to modern communication; however, they often serve as breeding grounds for biased content that can subtly or overtly reinforce harmful societal stereotypes. Detecting such bias, including political, gender-based, racial, or ideological bias, remains a challenging task due to the contextual, implicit, and dynamic nature of language. To address this challenge, this study presents a multi-phase deep learning framework that integrates statistical and contextual representations for bias detection in online content. The process begins with preprocessing to clean and normalize raw text, enabling consistent downstream analysis. The architecture employs a dual-path feature extraction strategy: the first path derives statistical features using an Enhanced Term Frequency–Inverse Document Frequency (TF–IDF) approach, while the second path captures semantic and contextual representations using a Transformer model with self-attention to learn deep linguistic dependencies. The features from both paths are concatenated and passed to a feature selection stage, where the Adaptive Lyrebird Optimization Algorithm (A_LyOA) is used to identify the most relevant and non-redundant features. Finally, classification is performed using a multi-attention-based AlexNet integrated with a Multi-Layer Perceptron (MLP), where attention modules are incorporated within convolutional layers to improve contextual feature representation. The proposed framework was evaluated using comprehensive performance metrics, achieving high accuracy (0.98), precision (0.97), a low false negative rate (FNR = 0.13), and a low false positive rate (FPR = 0.08), demonstrating strong performance compared with existing bias detection approaches.
Underwater Friction Stir Welding has arisen as proficient solid-state joining method for aluminium alloys necessitating stringent heat regulation. This work experimentally tested and systematically optimized the mechanical performance of 5 mm thick AA5754 butt joints produced by Underwater Friction Stir Welding. The influence of tool rotational speed, axial force and welding speed, on microhardness and tensile strength was assessed by an experimental design based on Taguchi orthogonal array. ANOVA were utilized to assess significance and impact of each process parameter. A multi-criteria decision-making (MCDM) MEREC and CoCoSo approaches was employed to determine best parameter combination, thereby addressing constraints of traditional single-response optimization. The findings indicate that tool rotational speed is the predominant contributor, succeeded by axial force and welding speed. Ideal process parameters for enhanced ki was determined at a a tool rotation speed of 1100 rpm, an axial force of 6 kN, and welding speed of 20 mm/min, corresponding to (X2 Y2 Z2). The enhanced UFSW condition led to a significant enhancement in joint efficiency and tensile strength, achieving approximately 90
Lung abnormalities are a frequent medical imaging case, which must be detected effectively and promptly to be treated. This paper introduces a convolutional neural network (CNN) of ResNet-18, which is introduced to detect lung abnormalities in the X-rays of the chest. The suggested model is meant to detect various forms of lung abnormalities with well-selected datasets in collaboration with the best prepreprocessing and training approaches. Particularly, lung segmentation, outlier elimination, region-of-interest (ROI) extraction, and image normalization were combined to improve the quality of the data and minimize noise before classification. The system was able to attain an accuracy of 93.10
We present a comprehensive analysis of inflationary models in light of projected sensitivities from forthcoming CMB and gravitational wave experiments, incorporating data from recent ACT DR6, DESI DR2, CMB-S4, LiteBIRD, and SPHEREx. Focusing on precise predictions in the (ns, as, fis) parameter space, we evaluate a broad class of inflationary scenarios-including canonical single-field models, non-minimally coupled theories, and string-inspired constructions such as Starobinsky, Higgs, Hilltop, a-attractors, and D-brane models. Our results show that next-generation observations will sharply constrain the scale dependence of the scalar power spectrum, elevating as and fisas key discriminants between large-field and small-field dynamics. Strikingly, several widely studied models-such as quartic Hilltop inflation and specific DBI variants-are forecast to be excluded at high significance. We further demonstrate that the combined measurement of fis and the field excursion Delta cent offers a novel diagnostic of kinetic structure and UV sensitivity. These findings underscore the power of upcoming precision cosmology to probe the microphysical origin of inflation and decisively test broad classes of theoretical models.
