Performance evaluation and correction of Al2O3 and YSZ-doped In2O3/In2O3 multilayer heterogeneous thin-film thermocouples up to 1850 °C by Meng Wang, Zhongkai Zhang, Jiaming Lei, Le Li, Bo Li, Zhaojun Liu, Yong Xia, Dan Liu, Bian Tian, Weixuan Jing

“…In experiments with different doping ratios at 1800 °C, the thermocouple achieves a maximum temperature electromotive force (TEMF) of 258.5 mV and a maximum Seebeck coefficient of 180.9 μV/°C, with an In2O3 : YSZ92(ZrO2 (92 wt%) : Y2O3 (8 wt%)) ratio of 9 : 1 in wt%. …”
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Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile by Lien Rodríguez-López, Lisandra Bravo Alvarez, Iongel Duran-Llacer, David E. Ruíz-Guirola, Samuel Montejo-Sánchez, Rebeca Martínez-Retureta, Ernesto López-Morales, Luc Bourrel, Frédéric Frappart, Roberto Urrutia

“…The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2), using in situ, and meteorological and satellite data from Landsat and Sentinel missions (Case 3). …”
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Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review by Umile Giuseppe Longo, Alberto Lalli, Guido Nicodemi, Matteo Giuseppe Pisani, Alessandro De Sire, Pieter D'Hooghe, Ara Nazarian, Jacob F. Oeding, Balint Zsidai, Kristian Samuelsson

“…Among the studies included in the final synthesis, Convolutional Neural Networks (CNN) emerged as the most frequently applied category of ML models, present in 17 studies (32%). …”
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ViTAU: Facial paralysis recognition and analysis based on vision transformer and facial action units by Jia GAO, Wenhao CAI, Junli ZHAO, Fuqing DUAN

“…These maps are then processed through a pyramid convolutional neural network interpreter to generate heatmaps. …”
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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…Temporal attention mechanism addresses the unequal importance of historical data by assigning weights to different time moments according to their relevance to current predictions. …”
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A Picking Point Localization Method for Table Grapes Based on PGSS-YOLOv11s and Morphological Strategies by Jin Lu, Zhongji Cao, Jin Wang, Zhao Wang, Jia Zhao, Minjie Zhang

“…In future work, we will enrich the grape dataset by collecting images under different lighting conditions, from various shooting angles, and including more grape varieties to improve the method’s generalization performance.…”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…Dear Editor Diabetes remains a significant public health challenge, and the integration of artificial intelligence (AI) presents remarkable opportunities to enhance early diagnosis, personalized treatment, and effective prevention strategies.1 AI algorithms, including supervised learning and convolutional neural networks, can efficiently analyze large datasets to identify patterns and risk factors associated with diabetes, surpassing the capabilities of traditional methods.2 This advanced analysis enables healthcare providers to predict the likelihood of diabetes in individuals and populations, facilitating timely interventions and customized prevention strategies. …”
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A Diagnosis Method for Noise and Intermittent Faults in Analog Circuits Based on the Fusion of Multiscale Fuzzy Entropy Features and Amplitude Features by Junyou Shi, Yilei Hou, Zili Wang, Zhilin Yang, Zhenyang Lv

“…Finally, the two features are fed into a convolutional neural network for diagnosis. The method is applied to two typical analog circuits. …”
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A Computational Model of Attention-Guided Visual Learning in a High-Performance Computing Software System by Alice Ahmed, Md. Tanim Hossain

“…The study discovered that supervised error backpropagation and the attention-modulated Hebbian rule outperformed the weight gain rule on MNIST; however, concentration differed. …”
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Real time weed identification with enhanced mobilevit model for mobile devices by Xiaoyan Liu, Qingru Sui, Zhihui Chen

“…Also, existing methods generally use large, slow multi-layer convolutional networks (CNNs), which are impractical for use on mobile embedded devices. …”
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MFSE-TransUNet: A Thyroid Nodule Ultrasound Image Segmentation Network Integrated With Dynamic Feature Calibration and Edge Enhancement by Ye Lu, Jiaojiao Jing, Wenbo Zhang, Yali Kong

“…Although the existing TransUNet balances local and global features through a hybrid CNN-Transformer architecture, it still faces the following challenges in thyroid nodule segmentation: firstly, the fixed convolutional kernels struggle to adapt to nodule morphological diversity; Secondly, existing multi-scale feature fusion methods fail to consider hierarchical contribution differences; Furthermore, significant edge information is easily lost during upsampling. …”
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Improvement in positional accuracy of neural-network predicted hydration sites of proteins by incorporating atomic details of water-protein interactions and site-searching algorith... by Kochi Sato, Masayoshi Nakasako

“…Recently, we developed a convolutional neural network (CNN) to predict the probability distribution of hydration water molecules over protein surfaces and in protein cavities. …”
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Cell-TRACTR: A transformer-based model for end-to-end segmentation and tracking of cells. by Owen M O'Connor, Mary J Dunlop

“…Existing architectures developed for cell tracking based on convolutional neural networks (CNNs) have tended to fall short in managing the spatial and global contextual dependencies that are crucial for tracking cells. …”
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ML‐UrineQuant: A machine learning program for identifying and quantifying mouse urine on absorbent paper by Warren G. Hill, Bryce MacIver, Gary A. Churchill, Mariana G. DeOliveira, Mark L. Zeidel, Marcelo Cicconet

“…Abstract The void spot assay has gained popularity as a way of assessing functional bladder voiding parameters in mice, but analyzing the size and distribution of urine spot patterns on filter paper with software remains problematic due to inter‐laboratory differences in image contrast and resolution quality and non‐void artifacts. …”
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Depth-Enhanced Tumor Detection Framework for Breast Histopathology Images by Integrating Adaptive Multi-Scale Fusion, Semantic Depth Calibration, and Boundary-Guided Detection by A. Robert Singh, Suganya Athisayamani, Hariharasitaraman S, Faten Khalid Karim, Jose Varela-Aldas, Samih M. Mostafa

“…AMSF combines RGB histopathology image channels, binary masks, and multi-scale convolution outputs using attention mechanisms to generate a fused feature map that captures occlusion information and boundary relevance. …”
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A Multitask Network for the Diagnosis of Autoimmune Gastritis by Yuqi Cao, Yining Zhao, Xinao Jin, Jiayuan Zhang, Gangzhi Zhang, Pingjie Huang, Guangxin Zhang, Yuehua Han

“…The endoscopic manifestations of AIG differ from those of gastritis caused by <i>Helicobacter pylori</i> (<i>H. pylori</i>) infection in terms of the affected gastric anatomical regions and the pathological characteristics observed in biopsy samples. …”
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YOLOv9-GDV: A Power Pylon Detection Model for Remote Sensing Images by Ke Zhang, Ningxuan Zhang, Chaojun Shi, Qiaochu Lu, Xian Zheng, Yujie Cao, Xiaoyun Zhang, Jiyuan Yang

“…Secondly, a Diverse Branch Block (DBB) is embedded in the feature extraction–fusion module, which enriches the feature space by enhancing the representation capability of single-convolution operations, thereby improving model feature extraction performance without increasing inference time costs. …”
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Random Undersampled Digital Elevation Model Super-Resolution Based on Terrain Feature-Aware Deep Learning Network by Ziqiang Huo, Meng Xi, Jingyi He, Zhengjian Li, Jiabao Wen

“…However, DEM data and general images have unique characteristics and fundamental differences, making it inappropriate to directly apply image-based super-resolution methods to extract terrain features. …”
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Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids by Yanfei Li, Juejing Liu, Xiaodong Zhao, Wenjun Liu, Tong Geng, Ang Li, Xin Zhang

“…Notably, MTL models show superior accuracy compared to binary classification convolutional neural networks. Additionally, introducing a tailored cross‐entropy loss function improves MTL model performance. …”
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Non-intrusive load monitoring based on time-enhanced multidimensional feature visualization by Tie Chen, Yimin Yuan, Jiaqi Gao, Shinan Guo, Pingping Yang

“…Abstract In the research of non-intrusive load monitoring (NILM), the temporal characteristics of V–I trajectories are often overlooked, and using a single feature for identification may lead to insignificant differences between similar loads. Based on this, this paper proposes a non-intrusive load monitoring method based on time-enhanced multidimensional feature visualization. …”
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