Development of data-driven algal bloom alert models with low temporal resolution data and application to Hong Kong rivers by Shujie Xu, Zhongnan Ye, Shu-Chien Hsu, Xiaoyi Liu, Chunmiao Zheng

“…This study offers a potential solution for prediction and forecasting without extensive datasets and emphasizes the importance of integrating domain knowledge in both prior and posterior analyses to enhance the interpretability of data-driven models in water science.…”
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Single‐Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite‐Gene Correlation Network by Xiying Mao, Dandan Xia, Miao Xu, Yan Gao, Le Tong, Chen Lu, Weiqi Li, Runmin Xie, Qinghuai Liu, Dechen Jiang, Songtao Yuan

“…However, the current single cell metabolome (SCM) study using mass spectroscopy has difficulty giving a complete view of the metabolic activity in the cell, and the prediction of the metabolism‐phenotype relationship is limited by the potential inconsistency between transcriptomic and metabolic levels. …”
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Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models by Imran Said, Vasit Sagan, Kyle T. Peterson, Haireti Alifu, Abuduwanli Maiwulanjiang, Abby Stylianou, Omar Al Akkad, Supria Sarkar, Noor Al Shakarji

“…This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions. …”
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Optimasi Pertanian Padi: Peramalan Curah Hujan Berbasis Arima Untuk Penentuan Waktu Tanam Yang Tepat by Sofi Defiyanti, Betha Nurina Sari, Tesa Nur Padilah

“…Forecasting using the ARIMA method can be used to predict future rainfall, helping to identify the best time to start planting. …”
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A deep learning-based method for the intelligent identification of the quantity of coals flushed out during borehole hydraulic flushing by Xiaojun LI, Mingyang ZHAO, Miao LI

“…The CIoU loss function was replaced with the SIoU loss function to accelerate the matching between prediction and ground truth boxes. Finally, the improved YOLOv8n algorithm was validated using a self-built dataset. …”
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Modelación con elementos finitos del agrietamiento en el hormigón por corrosión localizada en la armadura Finite element modeling of cracking in concrete due to localized corrosion... by J Castorena, J.L. Pérez, A Borunda, C Gaona, A Torres-Acosta, I Velázquez, A Martínez, F Almeraya

“…The results show the great influence that has the localized corrosion (small-size anode vs large-size cathode) on the prediction of rcrit, and that effect is only possible to analyze it in a three-dimensional way. …”
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Steering conservation biocontrol at the frontlines: A fuzzy logic approach unleashing potentials of climate-smart intercropping as a component within the integrated management of f... by Komi Mensah Agboka, Henri E.Z. Tonnang, Emily Kimathi, Elfatih M. Abdel-Rahman, John Odindi, Onisimo Mutanga, Saliou Niassy

“…Farms in the eastern and southern regions are predicted to be highly suitable, while the suitability of farms in West Africa is expected to improve over time due to the perennial nature and agronomic benefits of companion plants. …”
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ECG-LM: Understanding Electrocardiogram with a Large Language Model by Kai Yang, Massimo Hong, Jiahuan Zhang, Yizhen Luo, Suyuan Zhao, Ou Zhang, Xiaomao Yu, Jiawen Zhou, Liuqing Yang, Ping Zhang, Mu Qiao, Zaiqing Nie

“…Conclusions: The results across various tasks demonstrate that ECG-LM effectively captures the intricate features of ECGs, showcasing its versatility in applications such as disease prediction and advanced question answering.…”
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Prediksi Interaksi Drug Target pada Gen Kanker Menggunakan Metode Lasso-XGBoost by Muh Fadhil Al-Haaq Ginoga, Wisnu Ananta Kusuma, Mushthofa Mushthofa

“…In this study, a DTI prediction model is built by selecting features on the data set using Least Absolute Shrinkage and Selection Operator (LASSO) then data balancing performed with Synthetic Minority Oversampling Technique (SMOTE) and Extreme Gradient Boosting (XGBoost) performed to predict the interaction. …”
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Model Klasifikasi Dengan Logistic Regression Dan Recursive Feature Elimination Pada Data Tidak Seimbang by Sutarman, Rimbun Siringoringo, Dedy Arisandi, Edi Kurniawan, Erna Budhiarti Nababan

“…Logistic regression can yield good results in classification and prediction problems. The extensive features of the dataset can lead to computational burdens and reduced classification performance. …”
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Unveiling the inhibitory effect of hydrogen-decorated voids and dislocation loops on the glide of edge dislocation in tungsten by Qing-Yuan Ren, Yu-Hao Li, Yu-Chen Du, Tian-Ren Yang, Dmitry Terentyev, Wei-Zhong Han, Hong-Bo Zhou, Guang-Hong Lu

“…Our findings advocate that the presence of interstitial impurities can dramatically modify the mechanical properties of materials underirradiation, and provide an important reference for the prediction of W performance and the development of advanced nuclear materials.…”
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Pengaruh Prediksi Missing Value pada Klasifikasi Decision Tree C4.5 by Aji Seto Arifianto, Kursita Dewi Safitri, Khafidurrohman Agustianto, I Gede Wiryawan

“…Then in the second treatment, the prediction of missing value was applied using the mean and mode formula before the formation of the rule tree, obtained 24 rules. …”
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Development of improved deep learning models for multi-step ahead forecasting of daily river water temperature by Mehdi Gheisari, Jana Shafi, Saeed Kosari, Samaneh Amanabadi, Saeid Mehdizadeh, Christian Fernandez Campusano, Hemn Barzan Abdalla

“…This study addresses the limited use of signal decomposition in hybrid WT prediction models by proposing three methods: namely ensemble empirical mode decomposition (EEMD) on AdaBoost, long short-term memory (LSTM), and gated recurrent unit (GRU). …”
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SDUST2023BCO: a global seafloor model determined from a multi-layer perceptron neural network using multi-source differential marine geodetic data by S. Zhou, S. Zhou, J. Guo, H. Zhang, Y. Jia, H. Sun, X. Liu, D. An

“…Second, the input data at interesting points are fed into the MLP model to obtain prediction bathymetry. Finally, a high-precision bathymetric model with a resolution of <span class="inline-formula">1^′</span> <span class="inline-formula">×</span> <span class="inline-formula">1^′</span> has been constructed for the global marine area. …”
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Maternal health risk factors dataset: Clinical parameters and insights from rural BangladeshMendeley Data by Mayen Uddin Mojumdar, Dhiman Sarker, Md Assaduzzaman, Hasin Arman Shifa, Md. Anisul Haque Sajeeb, Oahidul Islam, Md Shadikul Bari, Mohammad Jahangir Alam, Narayan Ranjan Chakraborty

“…It will aid in generating high-risk pregnancy evaluation and prediction models to support clinical management. This dataset is valuable for its potential to serve as a benchmark for comparing maternal health responses across different clinical conditions of patients, thereby contributing to a broader understanding of pregnancy-related complications. …”
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HyQ2:&#x2009;A&#x2009;Hybrid&#x2009;Quantum&#x2009;Neural&#x2009;Network for&#x2009;NextG&#x2009;Vulnerability&#x2009;Detection by Yifeng Peng, Xinyi Li, Zhiding Liang, Ying Wang

“…The results show that the prediction accuracy and receiver operating characteristic AUC value fluctuate around 0.2&#x0025;, indicating HyQ2&#x2019;s robustness in noisy quantum environments. …”
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A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography by Kyu-Jin Jung, Thierry G. Meerbothe, Chuanjiang Cui, Mina Park, Cornelis A.T. van den Berg, Stefano Mandija, Dong-Hyun Kim

“…Within this framework, deep learning is used to discern the optimal polynomial fitting weights for a physics based polynomial fitting reconstruction on the complex B1+ data. For the prediction of optimal fitting coefficients, three neural networks were separately trained on simulated heterogeneous brain models to predict optimal polynomial weighting parameters in three orthogonal planes. …”
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Analysis of Inflammatory Mediator Profiles in Sepsis Patients Reveals That Extracellular Histones Are Strongly Elevated in Nonsurvivors by Tanja Eichhorn, Ingrid Linsberger, Lucia Lauková, Carla Tripisciano, Birgit Fendl, René Weiss, Franz König, Gerhard Valicek, Georg Miestinger, Christoph Hörmann, Viktoria Weber

“…The timely recognition of sepsis and the prediction of its clinical course are challenging due to the complex molecular mechanisms leading to organ failure and to the heterogeneity of sepsis patients. …”
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Exploring the role of circulating proteins in multiple myeloma risk: a Mendelian randomization study by Matthew A. Lee, Kate L. Burley, Emma L. Hazelwood, Sally Moore, Sarah J. Lewis, Lucy J. Goudswaard

“…Future work should explore the utility of these proteins in disease prediction or prevention using proteomic data from patients with MM or precursor conditions.…”
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Imaging Diagnostics, Biomarkers, and Emerging Trends in Orthopedic Research and Treatment by Gabriela Oliwia Trestka, Wiktoria Domino, Urszula Zelik, Maria Przygoda, Joanna Śnieżna, Kamila Stępień, Sabina Adamczyk, Wojciech Florczak, Jagienka Włodyka, Jakub Dziewic, Karol Dzwonnik

“…Biomarkers, such as those used in osteoarthritis, osteoporosis, joint inflammation, and bone regeneration, complement imaging techniques by enabling early diagnosis, monitoring disease progression, and predicting treatment outcomes. The integration of biomarkers with imaging provides a more comprehensive approach to patient care. …”
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