Comparing Satellite-Derived and Model-Based Surface Soil Moisture for Spring Barley Yield Prediction in Central Europe by Felix Reuß, Mariette Vreugdenhil, Emanuel Bueechi, Wolfgang Wagner

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Fusing satellite imagery and ground-based observations for PM2.5 air pollution modeling in Iran using a deep learning approach by Zohreh Sohrabi, Jamshid Maleki

“…In this research, PM2.5 pollutant concentration modeling for monthly continuous distribution estimation has been implemented and evaluated based on deep learning models. …”
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Recursive feature elimination for summer wheat leaf area index using ensemble algorithm-based modeling: The case of central Highland of Ethiopia by Dereje Biru, Berhan Gessesse, Gebeyehu Abebe

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Resource Scheduling Method for Integration of TT＆C and Observation Based on Multi-Agent Deep Reinforcement Learning by Siyue CHENG, Haoran LI, Weigang BAI, Di ZHOU, Yan ZHU

“…With the development of satellite communication technology and the continuous expansion of the constellation scale, the integration of TT＆amp;C and observation technology has become the mainstream trend.The large constellation scale, many scheduling objects and complex operation joint control bring great challenges to the integrated resource scheduling of satellite network TT＆amp;C and observation.Subject to the low solution effi ciency and complex constraints of scheduling algorithms, the traditional TT＆amp;C resource scheduling technology adopts the advance injection TT＆amp;C instructions to perform tasks according to the fi xed deployment, which is diffi cult to meet the scheduling needs of emergencies and emergency tasks.Therefore, a kind of resource scheduling method based on multi-agent actor-Agent Actor-Critic Deterministic Policy Gradient Algorithms (MADDPG) was presented.With centralized training and distributed execution, the multi-agent model of integrated task of TT＆amp;C and observation was established.By analyzed the scheduling strategy of neighbor agent, the response speed of local information was improved.According to the model and constraints in the integrated resource scheduling problem of TT＆amp;C and observation, selected signifi cant and interpretable constraints, then established the multi-agent resource scheduling reinforcement learning model, and carried on the simulation test.The simulation results showed that the task benefi t of this method was 22% higher than the traditional method.…”
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Machine-learning-based spatial analysis of the spring states in the southernmost Eurasian permafrost, Hangai Mountains, central Mongolia by Mamoru Ishikawa, Azumi Okazaki, Avirmed Dashtseren, Khurelbaatar Temuujin, Tetsuya Hiyama

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Interacting Large Language Model Agents. Bayesian Social Learning Based Interpretable Models by Adit Jain, Vikram Krishnamurthy

“…Second, we utilize Bayesian social learning to construct interpretable models for LLMAs that interact sequentially with each other and the environment while performing Bayesian inference. …”
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Modeling the spatial relationship between bike-sharing stations and urban centrality using geographical weight variables by Jianyu Li, Mingxing Hu, Xinyu Zhang, Bing Han, Junheng Qi, Jiemin Zheng, Hui Wang

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PM2.5 prediction using population-based centrality weight by Hee Joon Choi, Won Kyung Lee, So Young Sohn

“…We propose to apply a population-based centrality weight to the cost function of the forecasting model, reflecting both of residential and changes in active populations. …”
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Application of problem based learning (PBL) learning model based on culturally responsive teaching (CRT) to the science learning outcomes of students in grade VII junior high schoo... by Lusi Rahmawati, Lina Agustina

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Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand by Oraya Sahat, Supot Kamsa-ard, Apiradee Lim, Siriporn Kamsa-ard, Matias Garcia-Constantino, Idongesit Ekerete

“…Regional variations in model performance were observed, with Random Forest performing best in the northern, northeastern regions, while XGBoost excelled in the central and southern regions. …”
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Development of Hypnoteaching-based Learning Videos to Improve Learning Outcomes of IPAS Materials: "Let's Get Acquainted with Our Earth" Grade V Students by Warto, Achmad Buchori, Ngatmini

“… This study aimed to develop hypnoteaching-based learning media to enhance fifth-grade students' learning outcomes in Natural and Social Sciences (IPAS – a subject integrating natural and social science contents in the Indonesian elementary curriculum) using the ADDIE model. …”
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Deep‐Learning Model for Central Nervous System Infection Diagnosis and Prognosis Using Label‐Free 3D Immune‐Cell Morphology in the Cerebrospinal Fluid by Bo Kyu Choi, Ho Heon Yang, Jong Hyun Kim, JaeSeong Hong, Kyung Min Kim, Yu Rang Park

“…A deep‐learning model is constructed to predict the etiology and prognosis of CNS infections using the immune‐cell morphology. …”
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The Application of Islamic Critical Thinking in Inquiry-Based Learning in Traditional Islamic Educational Institutions by Syafruddin Syafruddin, Muhyiddin Muhyiddin, Anjar Mahmudin Nst, Wingkolatin Wingkolatin, Ratna Rintaningrum, Yunita Abdullah Aji

“… This study aims to examine the application of Islamic Critical Thinking as an approach in the inquiry-based learning model within the traditional Islamic educational environment (dayah), with a case study at Dayah Jamiah Al-Aziziyah Bireuen, Aceh. …”
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Machine learning-based prediction model for lung ischemia-reperfusion injury: insights from disulfidptosis-related genes by Yanpeng Zhang, Yanpeng Zhang, Yanpeng Zhang, Jingyang Sun, Jingyang Sun, Jingyang Sun, Yihan Lin, Yihan Lin, Yihan Lin, Rongxuan Jiang, Rongxuan Jiang, Rongxuan Jiang, Niuniu Dong, Niuniu Dong, Niuniu Dong, Huanhuan Dong, Huanhuan Dong, Huanhuan Dong, Peng Li, Peng Li, Peng Li, Jinteng Feng, Jinteng Feng, Jinteng Feng, Zijiang Zhu, Guangjian Zhang, Guangjian Zhang, Guangjian Zhang

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Systematic Comparison of CRISPR and shRNA Screens to Identify Essential Genes Using a Graph-Based Unsupervised Learning Model by Yulian Ding, Connor Denomy, Andrew Freywald, Yi Pan, Franco J. Vizeacoumar, Frederick S. Vizeacoumar, Fang-Xiang Wu

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Prediction of Gross Primary Productivity Change in Central Asia Under Climate Change Using Deep Learning by Wei Shao, Chenchen Wu, Tianxiang Yue, Na Zhao, Yunfeng Hu

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QMIX-GNN: A Graph Neural Network-Based Heterogeneous Multi-Agent Reinforcement Learning Model for Improved Collaboration and Decision-Making by Taiyin Zhao, Tian Chen, Bing Zhang

“…Therefore, this paper proposes a heterogeneous multi-agent reinforcement learning model based on graph neural networks, which we call QMIX-GNN. …”
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Machine-learning unravels spatial shifting in homogeneous rainfall subregions in Central Africa under global warming by Alain T Tamoffo, Fernand L Mouassom, Torsten Weber

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Constraining Reionization with Lyα Damping-wing Absorption in Galaxy Spectra: A Machine Learning Model Based on Reionization Simulations by Hyunbae Park, Intae Jung, Hidenobu Yajima, Jenny G. Sorce, Paul R. Shapiro, Kyungjin Ahn, Pierre Ocvirk, Romain Teyssier, Gustavo Yepes, Ilian T. Iliev, Joseph S. W. Lewis

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Spectrum sensing based on adversarial transfer learning by Jiawu Miao, Yuebo Li, Xiaojun Jing, Fangpei Zhang, Junsheng Mu

“…Motivated by this, adversarial transfer learning is applied to SS here, where the model is pre‐trained at the central node firstly and fine‐tuned at the local nodes. …”
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