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Background: Head and neck cancer (HNC) is amongst the 10 most common cancers worldwide and has a major effect on patients' quality of life. Given the complexity of this unique group of patients, a multidisciplinary team approach is preferable. Amongst the debilitating sequels of HNC and/or its treatment, swallowing, speech and voice impairments are prevalent and require the involvement of speech-language pathologists (SLPs).

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Hydrothermal biochar has demonstrated potential in enhancing crop growth by improving soil properties and microbial activity; however, its effectiveness varies with application rate, with excessive amounts potentially inhibiting plant growth. This study employed a pot experiment approach to compare varying application rates of hydrothermal biochar (ranging from 0 to 50 t/ha) and to analyze its effects on alfalfa biomass, photosynthetic efficiency, soil nutrient content, and microbial community composition. Biochar application increased alfalfa dry weight by 12.

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MXene-based composite photocatalysts for efficient degradation of antibiotics in wastewater.

Sci Rep

December 2024

Department of Nano-Chemical Engineering, Faculty of Advanced Technologies, Shiraz University, Shiraz, Iran.

MXene-based (nano)materials have recently emerged as promising solutions for antibiotic photodegradation from aquatic environments, yet they are limited by scalability, stability, and selectivity challenges in practical settings. We formulated FeO-SiO/MXene ternary nano-photocomposites via coupled wet impregnation and sonochemistry approach for optimised tetracycline (TC) removal (the second most used antibiotic worldwide) from water using response surface methodology-central composite design (RSM-CCD). The photocatalysts containing various loading of FeO/SiO (5-45 wt%) on the MXene with a range of calcination temperatures (300-600 °C) via RSM optimisation were synthesised, characterised regarding crystallinity properties, surface morphology, binding energy, and light absorption capability, and analysed for TC degradation efficiency.

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EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.

Sci Rep

December 2024

School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou, 434100, Hubei, China.

Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effectiveness. In this paper, we propose a novel end-to-end emotion recognition method from EEG signals, called MSDCGTNet, which is based on the Multi-Scale Dynamic 1D CNN and the Gated Transformer.

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The Quantum Computing for Drug Discovery Challenge, held at the 42nd International Conference on Computer-Aided Design (ICCAD) in 2023, was a multi-month, research-intensive competition. Over 70 teams from more than 65 organizations from 12 different countries registered, focusing on the use of quantum computing for drug discovery. The challenge centered on designing algorithms to accurately estimate the ground state energy of molecules, specifically OH+, using quantum computing techniques.

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