Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration by Pin Chen, Xiyue Wang, Zexia Yang, Changfeng Shi

“…Urban agglomerations with a high economic activity and population density are key areas for carbon emissions and pioneers in achieving carbon peaking and the Sustainable Development Goals (SDGs). This study combines machine learning with an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model to uncover the mechanisms driving carbon peaking disparities within these regions. …”
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Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma by Feiyu Qi, Guiming Zha, Yanfang Zhang, Sihua Liu, Yuhang Yang, Wanliang Sun, Dongdong Wang, Zhong Liu, Zheng Lu, Dengyong Zhang

“…Genes associated with lipid metabolism in liver cells were identified, and a machine-learning model was developed using the bulk transcriptomic data randomly partitioned into training and validation sets. …”
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Exploiting Data Distribution: A Multi-Ranking Approach by Beata Zielosko, Kamil Jabloński, Anton Dmytrenko

“…So-called local rankings were constructed for local data sources using an approach based on machine learning models, i.e., the greedy algorithm for the induction of decision rules. …”
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Enhanced mastitis severity classification in dairy cows using DNN and RF: A study on PCA and correlation-based feature selection by Manar Lashin, Ayman Samir Farid, Abdullah T. Elgammal

“…These findings highlight the broader applicability of machine learning advancements beyond veterinary sciences, demonstrating the potential for interdisciplinary research across veterinary medicine, computer science, and engineering to drive innovation in automated diagnostics and resource management in dairy farming. …”
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Electricity nowcasting and forecasting efficiency in blockchain-integrated energy trading platforms by Ameni Boumaiza

“…Our methodology integrates real-time nowcasting algorithms and machine learning-based forecasting models to optimize electricity distribution, battery storage utilization, and cost efficiency within the marketplace. …”
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Real-Time Dolphin Whistle Detection on Raspberry Pi Zero 2 W with a TFLite Convolutional Neural Network by Rocco De Marco, Francesco Di Nardo, Alessandro Rongoni, Laura Screpanti, David Scaradozzi

“…These advancements enable low-cost devices for real-time cetacean presence detection, offering transformative potential for bycatch reduction and adaptive deterrence systems. This study bridges artificial intelligence innovation with ecological stewardship, providing a scalable framework for deploying machine learning in resource-constrained settings while addressing urgent conservation challenges.…”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…The influence of environmental noise is generally excluded during research on machine fault diagnosis using acoustic signals. This study proposes a fault diagnosis method using a variational autoencoder (VAE) and domain adaptation neural network (DANN), both of which are based on unsupervised learning, to address this problem. …”
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Fast outlier detection for high-dimensional data of wireless sensor networks by Yan Qiao, Xinhong Cui, Peng Jin, Wu Zhang

“…Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. …”
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History Matching of a Channelized Reservoir Using a Serial Denoising Autoencoder Integrated with ES-MDA by Sungil Kim, Baehyun Min, Seoyoon Kwon, Min-gon Chu

“…The SDAE learns how to eliminate the noise and restore the clean reservoir models. …”
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Early Detection and Dynamic Grading of Sweet Potato Scab Based on Hyperspectral Imaging by Xiaosong Ning, Qiyao Xia, Fajiang Tang, Ziyu Ding, Xiawei Ding, Fanguo Zeng, Zhangying Wang, Hongda Zou, Xuejun Yue, Lifei Huang

“…This study investigates the early detection of sweet potato scab by using hyperspectral imaging and machine learning techniques. The research focuses on developing an accurate, economical, and non-destructive approach for disease detection and grading. …”
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Seismic Attribute Extraction and Application Based on the Gabor Wavelet Transform by Ran Xiong, Xuri Huang, Liang Guo, Xuan Zou, Haonan Tian

“…This can be captured by machine learning and deep learning methods to stratigraphic segmentation as many people investigated in industry. …”
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Multidimensional analysis reveals gene expression, cell interactions, and signaling networks in glioma and Alzheimer’s disease by Meiyi Wang, Jie Yu, Dongfang Ge, Zhiwen Geng, Chengqun Wei, Dan Xu

“…This research also involved building machine learning models to classify glioma and assessing their performances, as well as a model that can best classify each type.. …”
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Red Fox Optimization-Based Estimation Algorithms for Splitting Tensile Strength of Basalt Fiber Reinforced Concrete by Xiao Wu, Daoyong Zhu, Qin Yan

“…This study aimed to evaluate the predictive ability of multiple methods based on machine learning (ML) for the STS of BFRC and to clarify their fundamental concepts. …”
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Classification and spectrum optimization method of grease based on infrared spectrum by Xin Feng, Yanqiu Xia, Peiyuan Xie, Xiaohe Li

“…The results show that this machine learning method can effectively eliminate the interference fringes in the IR spectrum, and complete the feature selection and dimensionality reduction of the high-dimensional spectral data. …”
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Framework for Groove Rating in Exercise-Enhancing Music Based on a CNN–TCN Architecture with Integrated Entropy Regularization and Pooling by Jiangang Chen, Junbo Han, Pei Su, Gaoquan Zhou

“…The integration of entropy pooling and regularization is critical, with their omission leading to notable reductions in R² values. Our method also surpasses the performance of CNN and other machine-learning models, including long short-term memory (LSTM) networks and support vector machine (SVM) variants. …”
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Synergy pathway innovation of carbon emission trading, tradable green certificate and green power trading policies: achieving China’s dual carbon goals by Lili Liu, Tiantian Feng, Cheng Zhong, Mingli Cui, Haoran Wang

“…A dual machine learning model is innovatively introduced to test the robustness of the results. …”
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A Methodology for Turbine-Level Possible Power Prediction and Uncertainty Estimations Using Farm-Wide Autoregressive Information on High-Frequency Data by Francisco Javier Jara Ávila, Timothy Verstraeten, Pieter Jan Daems, Ann Nowé, Jan Helsen

“…Wind farm performance monitoring has traditionally relied on deterministic models, such as power curves or machine learning approaches, which often fail to account for farm-wide behavior and the uncertainty quantification necessary for the reliable detection of underperformance. …”
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A conceptual digital twin framework for supply chain recovery and resilience by Oluwagbenga Victor Ogunsoto, Jessica Olivares-Aguila, Waguih ElMaraghy

“…This aids supply chain (SC) managers in making informed decisions. In the first phase, machine learning algorithms, including logistic regression and Long Short-Term Memory (LSTM), were trained on Kerala India's precipitation data to predict floods. …”
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Optimizing mRNA Vaccine Degradation Prediction via Penalized Dropout Approaches by Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Latifah Munirah Kamarudin

“…By the 100th epoch, training and validation losses reached 0.0023 and 0.0029, respectively, indicating a substantial improvement over baseline models. These methodologies extend beyond mRNA vaccine research, demonstrating versatility across diverse machine learning domains. …”
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Characteristics and mechanisms of soil salinization in humid climate areas by Chunrui Wu, Xinqiang Du, Bo Meng, Hui Guo

“…Study region: The Sanjiang Plain, China Study focus: Using 509 field-collected soil samples and multi-source remote sensing data (Landsat and Sentinel-2) as model inputs, among several machine learning methods tested, the XGBoost model was identified as the most effective and was used to construct a soil salinization inversion model, revealing the spatiotemporal evolution of soil salinization from 1992 to 2022. …”
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