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Health assessment and health trend prediction of wind turbine bearing based on BO-BiLSTM model
The role of generative AI tools in shaping mechanical engineering education from an undergraduate perspective
Abstract This study evaluates the effectiveness of three leading generative AI tools-ChatGPT, Gemini, and Copilot-in undergraduate mechanical engineering education using a mixed-methods approach. The performance of these tools was assessed on 800 questions spanning seven core subjects, covering multiple-choice, numerical, and theory-based formats. While all three AI tools demonstrated strong performance in theory-based questions, they struggled with numerical problem-solving, particularly in areas requiring deep conceptual understanding and complex calculations. Among them, Copilot achieved the highest accuracy (60.38%), followed by Gemini (57.13%) and ChatGPT (46.63%). To complement these findings, a survey of 172 students and interviews with 20 participants provided insights into user experiences, challenges, and perceptions of AI in academic settings. Thematic analysis revealed concerns regarding AI’s reliability in numerical tasks and its potential impact on students’ problem-solving abilities. Based on these results, this study offers strategic recommendations for integrating AI into mechanical engineering curricula, ensuring its responsible use to enhance learning without fostering dependency. Additionally, we propose instructional strategies to help educators adapt assessment methods in the era of AI-assisted learning. These findings contribute to the broader discussion on AI’s role in engineering education and its implications for future learning methodologies.
INS2 lineage cell tracking and insulin expression in the related organs of mice
Overcoming the limitations of motion sensor models by considering dendritic computations
Abstract The estimation of motion is an essential process for any sighted animal. Computational models of motion sensors have a long and successful history but they still suffer from basic shortcomings, as they disagree with physiological evidence and each model is dedicated to a specific type of motion, which is controversial from a biological standpoint. In this work, we propose a new approach to modeling motion sensors that considers dendritic computations, a key aspect for predicting single-neuron responses that had previously been absent from motion models. We show how, by taking into account the dynamic and input-dependent nature of dendritic nonlinearities, our motion sensor model is able to overcome the fundamental limitations of standard approaches.
Development and validation of an immune signature-based risk model for prognostic assessment in melanoma
An improved method for predicting the permeability of Chang 7 shale oil reservoirs based on lithology and hydraulic flow units
The iterated score regression estimation algorithm for PCA-based missing data with high correlation
Performance analysis of integrated solar and natural gas combined cycle power plants in high solar potential regions
The first-in-Africa ex vivo drug sensitivity testing platform identifies novel drug combinations for South African leukaemia patient cohort
The gain-of-function UBE3AQ588E variant causes Angelman-like neurodevelopmental phenotypes in mice
Convolutional Fine-Tuned Threshold Adaboost approach for effectual content-based image retrieval
Abstract Applications for content-based image retrieval (CBIR) are found in a wide range of industries, including e-commerce, multimedia, and healthcare. CBIR is essential for organising and obtaining visual data from massive databases. Traditional techniques frequently fail to extract high-level, relevant information from images, producing retrieval results that are not ideal. This research introduces a novel Convolutional Fine-Tuned Threshold Adaboost (CFTAB) approach that integrates deep learning and machine learning techniques to enhance CBIR performance. This dataset comprises image-based data collected from multiple sources. This image data were pre-processed using Adaptive Histogram Equalization (AHE). The features of localized image data were extracted using VGG16. For an efficient CBIR process, a novel CFTAB approach was introduced. It combines both deep and machine learning (ML) methods in the proposed architecture to improve the excellence of image search. To further improve performance, CFTAB incorporates an improved AB algorithm. This algorithm adjusts the threshold levels dynamically within a robust classifier to optimize training outcomes.
Acute effect of core stability exercises on static and dynamic postural balance in soccer players with groin pain
Control of contour evolution, burn rate variation, and reaction channel formation in coal gasification
Author Correction: Study of repulsive permanent magnetic levitation mechanism and its dynamic characteristics
Intercontinental movement of exotic fungi on decorative wood used in aquatic and terrestrial aquariums
A framework for mitigating malicious RLHF feedback in LLM training using consensus based reward
Adsorption of organic contaminants of emerging concern using microalgae-derived hydrochars
Abstract This study explored the adsorption capacity of hydrochars derived from a strain of microalgae biomass native to northern Sweden for contaminants of emerging concern (CECs) such as caffeine, chloramphenicol, trimethoprim, carbamazepine, bisphenol A, diclofenac, and triclosan. The findings indicate that the surface functionality of the microalgae-derived hydrochars – a blend of alkane/alkene and aromatic structures, coupled with different oxygen-containing functional groups (hydroxyl, carboxyl, and lactone) – significantly influenced the adsorption of the contaminants. The alkane/alkene and aromatic structures increased with increasing hydrothermal treatment temperature, while the oxygen- and nitrogen-containing groups diminished. Bisphenol A and triclosan, which were the compounds with the highest distribution coefficients, displayed improved adsorption on the hydrochars. The study measured peak adsorption values for the hydrochars processed at 180 °C, which achieved adsorption levels of 25.8 mg g− 1 for bisphenol A and 58.8 mg g− 1 for triclosan. The hydrochars produced using lower carbonisation temperatures (180 and 220 °C) exhibited enhanced adsorption of positively charged molecules such as trimethoprim, which was attributed to the increased presence of negatively charged oxygen-containing functional groups. Contrastingly, negatively charged molecules such as diclofenac and chloramphenicol demonstrated either low adsorption (2.5 mg g− 1 for chloramphenicol on hydrochar prepared at 180 °C) or no adsorption (diclofenac) due to repulsion by the negatively charged functional groups on the surface of the hydrochars.