Research on the Assessment of Architectural Colors in Cultural Heritage Blocks Based on Computer Vision: A Case Study of Tianjin by Xiaoli Cao, Yingxia Yun, Lijian Ren

“…However, further exploration is needed regarding how to integrate architectural color quantification metrics and evaluation techniques into the urban characteristics management framework. In this paper, taking Tianjin’s historic cultural heritage districts as a case study, street view data were utilized, and deep learning along with clustering analysis methods were employed to extract architectural colors. …”
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Proposing a framework for body mass prediction with point clouds: A study applied in typical swine pen environments by Gabriel Pagin, Luciane Silva Martello, Rubens André Tabile, Rafael Vieira de Sousa

“…Using a Python script, the Convex Hull (CH) and Alpha Shape (AS) algorithms were applied to extract dimensional characteristics from the dorsal region of the pigs, including perimeter, surface area (3D), projected surface area (2D), and volume, generating two datasets (CH and AS). …”
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3-D Model Extraction Network Based on RFM-Constrained Deformation Inference and Self-Similar Convolution for Satellite Stereo Images by Wen Chen, Hao Chen, Shuting Yang

“…The deformation result of each point in the point cloud is inferred by a graph convolution network to iteratively optimize the 3-D reconstruction effect of the visible surface. We construct the self-similar convolution module by utilizing the self-similarity characteristics existing in the target itself. …”
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Frequency-Based Density Estimation and Identification of Partial Discharges Signal in High-Voltage Generators via Gaussian Mixture Models by Krissana Romphuchaiyapruek, Sarawut Wattanawongpitak

“…This paper aims to identify PD types and estimate the density distribution of frequency characteristics for three PD types, internal PD, surface PD, and corona PD, using verified PD data. …”
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Tailoring neuromuscular dynamics: A modeling framework for realistic sEMG simulation. by Alvaro Costa-Garcia, Shingo Shimoda, Akihiko Murai

“…This study introduces an advanced computational model for simulating surface electromyography (sEMG) signals during muscle contractions. …”
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Underwater Sound Speed Profile Inversion Based on Res-SACNN from Different Spatiotemporal Dimensions by Jiru Wang, Fangze Xu, Yuyao Liu, Yu Chen, Shu Liu

“…It combines the spatiotemporal characteristics of sea level anomaly (SLA) and sea surface temperature anomaly (SSTA) data and establishes a nonlinear relationship between satellite remote sensing data and sound speed field by deep learning. …”
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TGF-Net: Transformer and gist CNN fusion network for multi-modal remote sensing image classification. by Huiqing Wang, Huajun Wang, Linfen Wu

“…In the field of earth sciences and remote exploration, the classification and identification of surface materials on earth have been a significant research area that poses considerable challenges in recent times. …”
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Practice and Enlightenment of Developing Librarians' Digital Scholarship Service Skills at the University of Florida Library by YANG Xing

“…[Results/Conclusions] Finally, it is suggested that domestic university libraries should focus on improving librarians' digital scholarship service ability from four aspects: 1) The library leadership should recognize the importance of digital scholarship services and incorporate them into the library's long-term development strategy, and advocate the concept of digital scholarship services from top to bottom. 2) The librarian competency training program should be designed from surface to depth. 3) The evaluation of librarian's training projects should be carried out from the surface to the essence, putting more emphasis on their learning process rather than the result.4) The library can first establish a digital scholarship interest group within the library, and then actively seek communication opportunities with external stakeholders such as campus departments, publishers, database vendors, and off-campus research institutions, thus building a digital scholarship practice community from the inside out. …”
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Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP Network by Xia Li, Feng Guo

“…It integrates four operational parameters—applied load, sliding speed, fluid viscosity and contact surface inclination. These enable the simultaneous prediction of two critical lubrication characteristics: film thickness and load-carrying capacity. …”
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Protected grounds of discrimination and the risk of workplace bullying by Michael Rosander, Helge Hoel, Stefan Blomberg

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Risk Assessment of Heavy Rain Disasters Using an Interpretable Random Forest Algorithm Enhanced by MAML by Yanru Fan, Yi Wang, Wenfang Xie, Bin He

“…The results indicate that (1) the annual characteristics of heavy rain days and rainfall amounts show a significant upward trend over the past 17 years; (2) the MAML-RF model improved the accuracy and precision of heavy rain disaster risk simulation by 4.44% and 3.71%, respectively, and reduced training time by 27.95% compared to the SCV-RF model; and (3) the SHAP interpretability algorithm results show that the top five influential factors are the number of heavy rain days, rainfall amount, slope, drainage pipe density, and impervious surface ratio.…”
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Adaptive Dynamic Programming-Based Intelligent Finite-Time Flexible SMC for Stabilizing Fractional-Order Four-Wing Chaotic Systems by Mai The Vu, Seong Han Kim, Duc Hung Pham, Ha Le Nhu Ngoc Thanh, Van Huy Pham, Majid Roohi

“…The control strategy eliminates the need for explicit system models by exploiting the norm-bounded characteristics of chaotic system states. To optimize the parameters of the model-free FTF-SMC, a deep reinforcement learning framework based on the adaptive dynamic programming (ADP) algorithm is employed. …”
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PM2.5 concentration 7-day prediction in the Beijing–Tianjin–Hebei region using a novel stacking framework by Xintong Gao, Xiaohong Wang, Fuping Li, Wenhao Jiang, Meng Zhe, Jiaxing Sun, Ao Zhang, Linlin Jiao

“…It has been acknowledged that existing PM2.5 prediction models predominantly rely on variables influenced by near-surface factors. This inherent limitation could hinder the comprehensive exploration of the continuous spatio-temporal characteristics associated with PM2.5. …”
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FOV Expansion of Bioinspired Multiband Polarimetric Imagers With Convolutional Neural Networks by Yongqiang Zhao, Miaomiao Wang, Guang Yang, Jonathan Cheung-Wai Chan

“…Spectral and polarimetric contents of the light reflected from an object contain useful information on material type and surface characteristics of the object. Jointly exploiting spatial, spectral, and polarimetric information helps detect camouflage targets. …”
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When mimetics meets chitosan by Yilin Guo, Xiaoyan Yu

“…The concept of mimetics can be defined in terms of “learning from others” or “inspired by others”, and indeed its essence is “universal”. …”
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Clinical diagnosis of conjunctival microcirculation in hypertensive patients using shadowless imaging optical technology based on artificial intelligence by SONG Tianli, LI Haixia, LIU Li’an

“…Objective To utilize artificial intelligence (AI) -based shadowless imaging optical technology to achieve high-definition collection, feature extraction, and comprehensive analysis of conjunctival microcirculation in hypertensive patients, and explore the characteristics of the scleral vasculature in these patients. …”
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Progress in developments and applications of the HYDRUS model and associated coupling model packages by Taotao WANG, Bei ZHANG, Huihua CHEN, Jianwei HUI, Long HAO, Liang CHEN

“…Future research should focus on the following aspects: (1) Considering the effects of different planting years, different root types, and different root characteristics such as root length, root diameter, root volume, and root density in the simulation of plant root effect by HYDRUS. (2) Accumulating more transport parameters for new pollutants, including diffusion, adsorption, and degradation, to improve the simulation of pollutant transport. (3) Enhancing the description and simulation of the heterogeneity of unsaturated zone media. (4) Strengthening the acquisition and determination of parameters through the integration of Machine Learning and Artificial Intelligence. (5) Further developing and applying HYDRUS coupling models to enable comprehensive simulations of the entire process of surface water, soil water, and saturated groundwater during seepage.…”
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Vegetation growth monitoring based on ground-based visible light images from different views by Yanli Chen, Lu Huang, Cheng Chen, Ying Xie

“…In this study, with the underlying surface mixed with karst bare rock and vegetation as the research object, the far-view images and near-view images of 4 eco-meteorological stations were used to compare the segmentation effect of machine learning segmentation algorithm on images from far and near views, analyze the vegetation growth characteristics of visible images from far and near views, and investigate the differences between multi-view images and satellite remote sensing monitoring. …”
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Forecasting the eddying ocean with a deep neural network by Yingzhe Cui, Ruohan Wu, Xiang Zhang, Ziqi Zhu, Bo Liu, Jun Shi, Junshi Chen, Hailong Liu, Shenghui Zhou, Liang Su, Zhao Jing, Hong An, Lixin Wu

“…WenHai outperforms a state-of-the-art eddy-resolving numerical GOFS and AI-based GOFS for the temperature profile, salinity profile, sea surface temperature, sea level anomaly, and near-surface current forecasts led by 1 day to at least 10 days. …”
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Comparative analysis of force sensitive resistor circuitry for use in force myography systems for hand gesture recognition by Giancarlo K. Sagastume, Peyton R. Young, Marcus A. Battraw, Justin G. Kwong, Jonathon S. Schofield

“…One method used for hand gesture recognition is force myography (FMG), which utilizes non-invasive pressure sensors to measure radial muscle forces on the skin’s surface of the forearm during hand movements. These sensors, typically force-sensitive resistors (FSRs), require additional circuitry to generate analog output signals, which are then classified using machine learning to derive corresponding control signals for the device. …”
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