Coupling-Induced Dynamic Off-Centering of Cu Drives High Thermoelectric Performance in TlCuS.

Seeking new and efficient thermoelectric materials requires a detailed comprehension of chemical bonding and structure in solids at microscopic levels, which dictates their intriguing physical and chemical properties. Herein, we investigate the influence of local structural distortion on the thermoelectric properties of TlCuS, a layered metal sulfide featuring edge-shared Cu-S tetrahedra within CuS layers. While powder X-ray diffraction suggests average crystallographic symmetry with no distortion in CuS tetrahedra, the synchrotron X-ray pair distribution function experiment exposes concealed local symmetry breaking, with dynamic off-centering distortions of the CuS tetrahedra.

Synthesis and mechanistic studies of 4-aminoquinoline-Isatin molecular hybrids and Schiff's bases as promising antimicrobial agents.

In this investigation, to determine their potential as specific antibacterial agents, Schiff's bases (LT-SB1-23 and SB1-SB12) and novel quinoline-isatin hybrids were subjected to microbiological testing. The in-vitro screening against bacterial strains (Escherichia coli, Enterococcus faecalis, Bacillus subtilis, Staphylococcus aureus, Pseudomonas aeruginosa, and Salmonella typhi) exhibited their antibacterial potential with many of the compounds showing inhibition range of 90-100 % at 200 μg/mL, against most of the tested strains. The MIC values of some of the compounds showed good antibacterial efficacy with values ranging from 32 to 128 μg/mL.

Ensemble Techniques for Robust Fake News Detection: Integrating Transformers, Natural Language Processing, and Machine Learning.

The proliferation of fake news across multiple modalities has emerged as a critical challenge in the modern information landscape, necessitating advanced detection methods. This study proposes a comprehensive framework for fake news detection integrating text, images, and videos using machine learning and deep learning techniques. The research employs a dual-phased methodology, first analyzing textual data using various classifiers, then developing a multimodal approach combining BERT for text analysis and a modified CNN for visual data.

CBNAAT: An Emerging Xpert in Diagnosis of Head and Neck Tuberculosis.

Aims And Obctives: To compare FNAC, microscopy (ZN staining), histopathology and CBNAAT for diagnosis of head and neck tuberculosis, and to evaluate efficacy of CBNAAT in early diagnosis of tuberculosis in head and neck region.

Materials And Methods: This prospective study was carried out in the department of otorhinolaryngology, JLN Medical College and attached hospital, Ajmer from August 2020 to September 2022. Thorough history and clinical examination of all patients presented with neck swelling was done.