Optimization Method for Crop Growth Characteristics Based on Improved Locality Preserving Projection by Jia Dongyao, Hu Po, Zou Shengxiong

“…An improved locality preserving projection algorithm is proposed to optimize the extraction of growth characteristics. …”
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Non-Destructive Detection of Pomegranate Blackheart Disease via Near-Infrared Spectroscopy and Soft X-ray Imaging Systems by Rongke Nie, Xingyi Huang, Xiaoyu Tian, Shanshan Yu, Chunxia Dai, Xiaorui Zhang, Qin Fang

“…The results showed that the optimal NIR-based discriminative model, constructed with a Random Forest (RF) algorithm based on spectra preprocessed by the second-derivative (D2) denoising and a Competitive Adaptive Reweighted Sampling (CARS) algorithm, achieved a prediction set accuracy of 86.00%; the optimal soft X-ray imaging-based discriminative model, built with an RF algorithm using textural features extracted from images preprocessed by median filtering and a Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm combined with gray-level co-occurrence matrix (GLCM) and gray-gradient co-occurrence matrix (GGCM) algorithms, reached a prediction set accuracy of 93.10%. …”
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Digital twin-driven method for determining wind force coefficients of a bridge deck section by Hao-Yang Li, You-Lin Xu, Hai-Li Liao, Lin Huang, Qi Wang

“…The results show that the digital twin-driven method can provide more accurate prediction than wind tunnel tests or CFD simulations.…”
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Specification and Verification Method of Parallel Hierarchical Timed Automata by Predicate Abstraction and Refinement by Satoshi Yamane

“…Based on the results of the analysis, the algorithm performs refinement corresponding to each abstraction. …”
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Synthesizing Sum and Difference Patterns with Low Complexity Feeding Network by Sharing Element Excitations by Jafar Ramadhan Mohammed

“…Unlike the standard optimization approaches such as genetic algorithm (GA), the described algorithm performs repeatedly deterministic transformations on the initial field until the prescribed requirements are satisfied. …”
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Convolutional neural network-based low light image enhancement method by J. Guo

“…However, noise and contrast reduction frequently have an impact on image quality in low-light situations. …”
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AlphaFold-based AI docking reveals AMPK/SIRT1-TFEB pathway modulation by traditional Chinese medicine in metabolic-associated fatty liver disease by Lulu Zhang, Yi Zheng, Mingyan Shao, Aiping Chen, Meiyi Liu, Wenlong Sun, Tianxing Li, Yini Fang, Yang Dong, Shipeng Zhao, Hui Luo, Juan Feng, Qi Wang, Lingru Li, Yanfei Zheng

“…This study investigated QGJZF’s effects in high-fat diet-induced zebrafish and golden hamsters, and in palmitate (PA) and oleic acid (OA) - induced HepG2 cells, using the SymMap database to identify potential targets and pathways of QGJZF in MAFLD and AlphaFold algorithms to predict protein structures. In vivo, QGJZF significantly alleviated hepatic lipid deposition. …”
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Anomaly Detection Method for Hydropower Units Based on KSQDC-ADEAD Under Complex Operating Conditions by Tongqiang Yi, Xiaowu Zhao, Yongjie Shi, Xiangnan Jing, Wenyang Lei, Jiang Guo, Yang Meng, Zhengyu Zhang

“…This paper proposes a hydropower unit anomaly detection method based on K-means seeded quadratic discriminant clustering and an adaptive density-aware ensemble anomaly detection algorithm (KSQDC-ADEAD). The method first employs the KSQDC algorithm to identify different operating conditions of hydropower units. …”
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A Multi-Modal Attentive Framework That Can Interpret Text (MMAT) by Vijay Kumari, Sarthak Gupta, Yashvardhan Sharma, Lavika Goel

“…Deep learning algorithms have demonstrated exceptional performance on various computer vision and natural language processing tasks. …”
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Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions by Giovanna Nicora, Samuele Pe, Gabriele Santangelo, Lucia Billeci, Irene Giovanna Aprile, Marco Germanotta, Riccardo Bellazzi, Enea Parimbelli, Silvana Quaglini

“…ML is reported as highly effective in predicting movement intentions, assessing clinical outcomes, and detecting compensatory movements, providing insights into the future of personalized rehabilitation interventions. …”
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Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas by Safiah Almarri, Hur Al Safwan, Shahd Al Qisoom, Soufien Gdaim, Abdelkrim Zitouni

“…The system also integrates artificial intelligence (AI) algorithms (multiclass logistic regression) to process sensor data and predict fire risk levels with 99.97% accuracy, enabling proactive wildfire mitigation. …”
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Assessing CO2 separation performances of IL/ZIF-8 composites using molecular features of ILs by Hasan Can Gulbalkan, Alper Uzun, Seda Keskin

“…The accurate prediction power of these ML models was shown by comparing their estimates with the experimental and simulation data. …”
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A cross-stage features fusion network for building extraction from remote sensing images by Xiaolong Zuo, Zhenfeng Shao, Jiaming Wang, Xiao Huang, Yu Wang

“…CFF-Net outperformed other state-of-the-art algorithms on the two datasets in IoU and F1 metrics. …”
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An Enhanced Seamless Localization Framework Using Spatial-temporal Uncertainty Predictor Under Obscured Indoor and Outdoor Scenes by Y. Yu, S. Bai, J. Wang, K. Tang, S. Bao, L. Chen, R. Chen

“…An iPDR-based trajectory estimation structure is proposed, using the integration of INS/PDR mechanizations, magnetic observations, and deep-learning based speed estimation to enhance the performance of traditional PDR algorithm. A period of human motion features extracted from hybrid location sources are modelled instead of only one or adjacent location points to realize time-varying measured uncertainty errors prediction, and the predicted uncertainty errors of different indoor and outdoor location sources are integrated with iPDR to realize robust seamless positioning performance. …”
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Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation by Matthew J. McCarthy, Jesus D. Gomez-Velez, David Hughes, Shannon Meade

“…To address this data gap, we developed a protocol that combined satellite-based time-series measurements of river width at seven locations throughout Ukraine from 2013 to 2023 with reanalysis data, climate-model predictions, and hydrologic models to both provide a means of monitoring a proxy for near-real-time discharge and also predict near-term (i.e., 2023–2030) hydrologic patterns for the region. …”
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Differences in parameter estimates derived from various methods for the ORYZA (v3) Model by Jun-wei TAN, Qing-yun DUAN, Wei GONG, Zhen-hua DI

“…The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. …”
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A Machine Learning-Based Diagnostic Nomogram for Moyamoya Disease: The Validation of Hypoxia-Immune Gene Signatures by Cunxin Tan, Xilong Wang, Zhenyu Zhou, Yutong Liu, Shihao He, Yuanli Zhao

“…These genes were further refined through machine learning algorithms. The diagnostic value was confirmed using an external dataset, and a diagnostic nomogram was constructed. …”
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Establishment and validation of an immune-related nomogram for the prognosis of pancreatic adenocarcinoma by Kan Wang, Yunkun Lu, Yanfei Cao, Ping Feng, Qiu Wu, Peng Xiao, Yimin Ding

“…This study aims to improve prognosis prediction to guide therapeutic decision-making, and to identify novel targets for immunotherapy of PDAC. …”
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Multigoal-oriented a posteriori error control for heated material processing using a generalized Boussinesq model by Beuchler, Sven, Endtmayer, Bernhard, Lankeit, Johannes, Wick, Thomas

“…Therein, error reductions and effectivity indices are consulted to establish the robustness and efficiency of our framework.…”
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Development of Recurrent Neural Networks for Thermal/Electrical Analysis of Non-Residential Buildings Based on Energy Consumptions Data by Elisa Belloni, Flavia Forconi, Gabriele Maria Lozito, Martina Palermo, Michele Quercio, Francesco Riganti Fulginei

“…Simplifying input variables can enhance the applicability of artificial intelligence techniques in predicting energy and thermal performance. This study proposes a neural network-based approach to characterize the thermal–energy relationship in commercial buildings, aiming to provide an efficient and scalable solution for performance prediction. …”
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