The solar-powered DC microgrid using adaptive feedback linearisation-based backstepping neural network controller (FLBNNC) is able to get the desired bus voltage with enhanced transient and steady-state responses. The...
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作者：
Baonan ZhouBinglong ZhaoChangning WuJunguo LiKe LiuDepartment of Chemistry
Southern University of Science and TechnologyShenzhen518055China Department of Mechanical EngineeringThe Hong Kong Polytechnic UniversityHong Kong999077China Beijing Petrochemical Engineering Co.Ltd.Beijing100107China School of Innovation and EntrepreneurshipSouthern University of Science and TechnologyShenzhen518055China
In this research, precise motion control and synchronized high-speed microscopic dual-wavelength interferometry were employed to investigate the impact of surface-active components on the rupture behavior of wetting f...
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In this research, precise motion control and synchronized high-speed microscopic dual-wavelength interferometry were employed to investigate the impact of surface-active components on the rupture behavior of wetting films. The findings unveiled a novel mechanism for wetting film rupture at hydrophobic interfaces, propelled by gas migration towards the solid-liquid interface, resulting in the nucleation and growth of surface nanobubble. Salt ions accelerate film rupture by reducing electrostatic interactions and enhancing gas transfer, whereas surfactant adsorption immobilizes the gas-liquid interface through the Marangoni effect, thereby postponing rupture by impeding gas migration and surface nanobubble formation. Furthermore, surfactants influence the kinetics of three-phase contact line formation, where variations in molecular structure, solubility, and ionic properties contributing to differing levels of friction, and thereby affecting the overall dynamics of wetting films.
This paper introduces an innovative approach for addressing the Poisson equation in simply and doubly connected 3D domains with irregular surfaces, which has significant implications in various scientific and engineer...
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Path planning for a mobile robot means devising a feasible, collision-free route between any two points while operating in tough, busy environments. The design of intelligent and efficient path planning algorithm...
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With the ‘smart irrigation system’, you can automate your whole irrigation system in addition to its other features. This Internet of Things-based irrigation system is being built with the help of an ESP8266 Node MC...
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Breast cancer, marked by uncontrolled cell growth in breast tissue, is the most common cancer among women and a second-leading cause of cancer-related deaths. Among its types, ductal and lobular carcinomas are the mos...
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Breast cancer, marked by uncontrolled cell growth in breast tissue, is the most common cancer among women and a second-leading cause of cancer-related deaths. Among its types, ductal and lobular carcinomas are the most prevalent, with invasive ductal carcinoma accounting for about 70–80% of cases and invasive lobular carcinoma for about 10–15%. Accurate identification is crucial for effective treatment but can be time-consuming and prone to interobserver variability. AI can rapidly analyze pathological images, providing precise, cost-effective identification, thus reducing the pathologists’ workload. This study utilizes a deep learning framework for advanced, automatic breast cancer detection and subtype identification. The framework comprises three key components: detecting cancerous patches, identifying cancer subtypes (ductal and lobular carcinoma), and predicting patient-level outcomes from whole slide images (WSI). The validation process includes visualization using Score-CAM to highlight cancer-affected areas prominently. Datasets include 111 WSIs (85 malignant from the Warwick HER2 dataset and 26 benign from pathologists). For subtype detection, there are 57 ductal and 8 lobular carcinoma cases. A total of 28,428 annotated patches were reviewed by two expert pathologists. Four pre-trained models—DenseNet-201, MobileNetV2, an ensemble of these two, and a Vision Transformer-based model—were fine-tuned and tested on the patches. Patient-level results were predicted using a majority voting technique based on the percentage of each patch type in the WSI. The Vision Transformer-based model outperformed other models in patch classification, achieving an accuracy of 96.74% for cancerous patch detection and 89.78% for cancer subtype classification. For WSI-based cancer classification, the majority voting method attained an F1-score of 99.06 and 96.13% for WSI-based cancer subtype classification. The proposed deep learning-based framework for advanced breast cancer det
This paper presents a novel machine learning-driven approach for designing and optimizing multi-band patch antennas tailored for next-generation Internet of Things applications in 5G and 6G wireless communication syst...
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The growth in Internet audio data has highlighted the need for accurate and efficient search methodologies. In this context, query-by-example spoken term detection (QbE-STD) plays a pivotal role, mainly when dealing w...
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Image de-hazing aims to improve quality and restore clarity of hazy images. When airborne particles like dust and smoke absorb light, it can result in a haze, a typical meteorological phenomenon that degrades color ac...
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A recent trend in the design and manufacturing industries is the development of miniaturised components or parts whose sizes are measured in microns. Micro electro discharge machining (μ-EDM) is one of the most promi...
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