LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping by Jonas Schmidinger, Sebastian Vogel, Viacheslav Barkov, Anh-Duy Pham, Robin Gebbers, Hamed Tavakoli, Jose Correa, Tiago R. Tavares, Patrick Filippi, Edward J. Jones, Vojtech Lukas, Eric Boenecke, Joerg Ruehlmann, Ingmar Schroeter, Eckart Kramer, Stefan Paetzold, Masakazu Kodaira, Alexandre M.J.-C. Wadoux, Luca Bragazza, Konrad Metzger, Jingyi Huang, Domingos S.M. Valente, Jose L. Safanelli, Eduardo L. Bottega, Ricardo S.D. Dalmolin, Csilla Farkas, Alexander Steiger, Taciara Z. Horst, Leonardo Ramirez-Lopez, Thomas Scholten, Felix Stumpf, Pablo Rosso, Marcelo M. Costa, Rodrigo S. Zandonadi, Johanna Wetterlind, Martin Atzmueller

“…We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. …”
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Robust Tube-Based MPC with Piecewise Affine Control Laws by Meng Zhao, Xiaoming Tang

“…This paper presents a tube-based model predictive control (MPC) algorithm with piecewise affine control laws for discrete-time linear systems in the presence of bounded disturbances. …”
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Designing a Neural Observer to Estimate the State Variables of the Dynamical System of a Specific Class of Leukaemia by Yousef Farshidi, Reza Ghasemi, Aminin Sharafian Ardekani

“…In order to adjust the neural network weights, the error back propagation learning algorithm was implemented. First of all, in this algorithm, the system outputs are generated according to random weights. …”
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Deep learning based energy-efficient transmission control for STAR-RIS aided cell-free massive MIMO networks by Chihyun Song, Donghyun Lee, Yunseong Lee, Wonjong Noh, Sungrae Cho

“…From the simulations, it is revealed the proposed algorithm provides better energy performance than benchmarks, highlighting the benefits of STAR-RIS in the CF network.…”
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Few-shot crop disease recognition using sequence- weighted ensemble model-agnostic meta-learning by Junlong Li, Quan Feng, Junqi Yang, Jianhua Zhang, Jianhua Zhang, Sen Yang

“…Diseases pose significant threats to crop production, leading to substantial yield reductions and jeopardizing global food security. Timely and accurate detection of crop diseases is essential for ensuring sustainable agricultural development and effective crop management. …”
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Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets by Catarina Lopes, Andreia Brandão, Manuel R. Teixeira, Mário Dinis-Ribeiro, Carina Pereira

“…Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. …”
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Personalized treatment decision-making using a machine learning-derived lactylation signature for breast cancer prognosis by Simin Min, Xiaonan Zhang, Yuling Liu, Weiqiang Wang, Jingwen Guan, Yuyan Chen, Meng Sun, Ziheng Wang, Tao Wang

“…The model demonstrated robust predictive power across multiple cohorts. Immune infiltration analysis revealed that the low-risk group exhibited higher levels of immune checkpoints (e.g., PD-1, PD-L1) and greater infiltration of B cells, CD4+ T cells, and CD8+ T cells, suggesting better responsiveness to immunotherapy. …”
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Unmanned Aerial Vehicle Remote Sensing for Monitoring Fractional Vegetation Cover in Creeping Plants: A Case Study of <i>Thymus mongolicus</i> Ronniger by Hao Zheng, Wentao Mi, Kaiyan Cao, Weibo Ren, Yuan Chi, Feng Yuan, Yaling Liu

“…FVC estimation models were developed using four algorithms: multiple linear regression (MLR), random forest (RF), support vector regression (SVR), and artificial neural network (ANN). …”
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Detection and prevalence of avian influenza epidemic in the southwest of Poyang Lake and analysis of the influence of meteorological factors by Kang Fang, Xiansheng Ni, Xi Wang, Wentao Song, Zhiqiang Deng, Zeyu Zhao, Wei Hua, Zhizhong Zeng, Wei Wang, Qianqian Si, Jiang Wu, Bo Zhang, Ping Zhang, Hui Li, Tianmu Chen

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Online Estimation of the State of Health for the Lithium-Ion Battery Based on Open Circuit Model by Pan Yajia, Junda Li, Wang Tian'An, Yang Shengxun, Zhang Yunxuan, Li Zigang, Hu Yanwen

“…The accurate prediction of the state of health (SOH) for lithium-ion batteries is of significant importance in proactively avoiding unsafe behavior. …”
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Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology by Chen Bin, Lei Dong, Huang Liangjun, Liu Qingdong, Zhao Xuan, Shi Yuanping

“…Combining the deep learning model with the multi model hierarchical classification algorithm, the remote sensing prediction map based on deep learning is used as the classification base map, and the normalized difference vegetation index threshold is used to classify the uncovered area. …”
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Coordinated Interaction Strategy of User-Side EV Charging Piles for Distribution Network Power Stability by Juan Zhan, Mei Huang, Xiaojia Sun, Zuowei Chen, Zhihan Zhang, Yang Li, Yubo Zhang, Qian Ai

“…Secondly, by combining urban transportation big data and prediction networks, high-precision inference of the spatiotemporal distribution of charging loads can be achieved. …”
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A Deep Pedestrian Tracking SSD-Based Model in the Sudden Emergency or Violent Environment by Zhihong Li, Yang Dong, Yanjie Wen, Han Xu, Jiahao Wu

“…Under crowded scenarios and emergency places, it is a challenging problem to predict and warn owing to the complexity of crowd intersection. …”
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Optimization of Adaptive I²H &#x221E; Control Method Based on Multiple Input Sensors by Yu Gu, Hanyang Li, Zeting Mei, Hao Wen, Yuanxiong Jin, Wenxuan Dong

“…The core contributions of this study include: 1) Designing a multi-sensor current reference estimator to dynamically generate the optimal electromagnetic torque through state variables such as wheel speed, acceleration, slope, and human factor database (heart rate, subjective score, fatigue index) to achieve real-time prediction of rider demand; 2) Proposing an adaptive current reference value estimation algorithm that integrates feedforward compensation and error feedback to ensure smooth switching of assistance modes and suppress sensor noise; 3) Developing an intention-induced H<inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> robust current tracking controller that significantly enhances the system&#x2019;s robustness to parameter fluctuations and external disturbances by optimizing the H<inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> norm of the closed-loop transfer function, while supporting personalized riding assistance.…”
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Depth Integrated Multi-Task Prototypical Learning With Self Refinement for Unsupervised Domain Adaptation by Antonio Dauphin Fernando, Thumma Anirudh, Selvaraj Palanisamy, Karthika Prasad, Katia Alexander, Pandiyarasan Veluswamy, Rohini Palanisamy

“…Additionally, this pipeline integrates a Self-Refinement learning (SRL) algorithm that generates cross-domain pseudo-labels, which are leveraged to generate refined targets for further self-supervised training. …”
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A hybrid model based on the photovoltaic conversion model and artificial neural network model for short-term photovoltaic power forecasting by Ran Chen, Shaowei Gao, Yao Zhao, Dongdong Li, Shunfu Lin

“…The proposed model consists of an improved artificial neural network (ANN) algorithm and a PV power conversion model. First, the ANN model is designed to forecast the plane of array (POA) irradiance and ambient temperature. …”
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Enhancing Process Control in Agriculture: Leveraging Machine Learning for Soil Fertility Assessment by Ashutosh Sarangi, Sailesh Kumar Raula, Sohamdev Ghoshal, Swadhin Kumar, Chinta Sai Kumar, Neelamadhab Padhy

“…<b>Result</b>: The results demonstrated that the machine learning classifier significantly improves prediction accuracy. We used LR, KNN, NB, and DT classifiers to increase the accuracy, as well as to increase the efficiency of the soil fertility assessment. …”
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Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques by Pablo López-Muñoz, Luis Gimeno San Frutos, Christian Abarca, Francisco José Alegre, Jose Luis Calle, Jose F. Monserrat

“…Additionally, this module integrates a localization and tracking algorithm, allowing the tracking of the intruding UAV’s position. …”
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Incremental mining of periodic patterns of inadequately informed communication data based on fuzzy segmentation of time series by Miaomiao Li

“…Subsequently, the partial weekly incremental pattern mining algorithm with a moving window scans the segmented data, extracting new periodic patterns using the maximum sub-pattern pandering tree algorithm. …”
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MMG-Based Motion Segmentation and Recognition of Upper Limb Rehabilitation Using the YOLOv5s-SE by Gangsheng Cao, Shen Jia, Qing Wu, Chunming Xia

“…Additionally, the model demonstrated exceptional accuracy in predicting motion categories, achieving an accuracy of 98.9%. …”
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