artificial intelligence

Advances in dissolved gas analysis for power transformer diagnostics: From traditional methods to artificial intelligence driven prognostics

Utilisation and perceptions of generative artificial intelligence among physiotherapy students: a cross-sectional study

Smart hybrid water treatment systems: Synergizing electrocoagulation, advanced oxidation, and artificial intelligence for next-generation pollution control

Physiotherapy students perceptions and use of Artificial Intelligence in physiotherapy education

Harnessing fusion-based deep learning model and synergistic application of artificial intelligence and internet of things for computer vision-based precision spraying in tomato crop under covered cultivation

Artificial intelligence across the food packaging chain: from design to safety monitoring.

This review systematically examines the transformative potential of machine learning across the entire food packaging industry chain, including material design and selection, structural optimization, sensory-preference analysis, smart packaging, automated packaging machinery, microplastic detection, and waste recycling.

Offshore Cable Ploughing in Sands: Comparison of Literature Models and Machine Learning–Based Models with Field Data

In the global energy transition process, offshore wind is rapidly emerging as a crucial energy source. This shift has led to an increasing demand for submarine cables to connect offshore wind farms to onshore substations, with cable installation costs representing 9% of the total installation expenses. Cables are buried in the seabed, primarily through cable ploughing, adopting a special plough to create narrow trenches up to 3 m deep in the seabed. Accurate prediction of the vessel tow force is essential for efficient cable installation design, with the tow force being influenced by soil type, burial depth, and target velocity. Currently, available analytical approaches for sands rely on empirical correction factors, leading to inaccuracy in application across different scenarios. Advanced numerical methods could be in principle adopted to study the complex hydromechanical system response, but at the cost of significant computational resources and with the limitations related to field soil characterization. To address these limitations, artificial intelligence (AI)–based modeling has emerged as a promising alternative in many engineering fields, where wide data sets are available, as in cable ploughing applications due to the abundance of operational data. This study provides a comparison of the predictive capabilities of three literature analytical models against new field data from three different cable ploughing projects in sands, highlighting their limitations and proving the potential of the support vector machine (SVM) regression models in enhancing the prediction of the tow force. Remarkably, the developed SVM model only requires input parameters that are available from standard offshore geotechnical investigations, like cone penetration test results and soil granulometry. The study is based on 113 km of cable ploughing data, emphasizing the applicability of adaptable predictive models in the field of offshore cable installation.

Federated explainable artificial intelligence framework for real time cardiovascular risk prediction using multimodal data

Artificial intelligence-based soybean disease detection using transfer learning and deep learning models

Application of biochar in bioelectrochemical systems: performance enhancement mechanisms and application of artificial intelligence in system optimization

A smartphone-integrated colorimetric platform with AI assistance for sensitive rutin detection based on spherical Cu2O/MXene quantum dots nanozymes.

Artificial nanozymes integrate low cost and high sensitivity, and their rational design has become a research hotspot and core focus in the field. In this work, a novel high-sensitivity colorimetric detection platform was developed using MXene quantum dots (MQDs)-modified spherical Cu2O nanozymes (Cu2O/MQDs) for rutin detection. Combined experimental characterization and theoretical simulation were performed to study the catalytic mechanism of Cu2O/MQDs. Density functional theory (DFT) calculations theoretically revealed that the Cu-Ti bimetallic interfacial active sites endow the composite material with enhanced catalytic activity by accelerating the activation of H2O2 and the formation of hydroxyl radicals (•OH). Under optimized experimental conditions, the colorimetric sensing platform constructed with Cu2O/MQDs exhibited a linear detection range of 0-150 μM, with a calculated limit of detection (LOD) of 0.214 μM. Moreover, based on this detection mechanism, an artificial intelligence (AI)-assisted smartphone detection platform was developed to achieve more convenient, efficient, sensitive and reliable rutin detection, with an LOD of 0.054 μM. Based on the findings of this study, successful quantitative detection of rutin in environmental and pharmaceutical samples was achieved.

A global-local-edge collaborative artificial intelligence method for tunnel lining crack segmentation using grouped Kolmogorov-Arnold networks