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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A Reinforcement-Learning Based Approach for Designing High-Voltage SiC MOSFET Guard Rings by Tejender Singh Rawat, Chia-Lung Hung, Yi-Kai Hsiao, Wei-Chen Yu, Surya Elangovan, Wei-Ting Lin, Yi-Rong Lin, Kai-Lin Yang, Nien-Yi Jan, Yung-Hui Li, Hao-Chung Kuo

“…In this work, the reinforcement learning method has been successfully implemented on the 1.7 kV SiC guard ring device TCAD simulated data for the prediction of parameters. Our work has predicted the parameters successfully for the 2.5 kV guard ring design. …”
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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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The mediating effect of clinical belongingness on the relationship between anxiety and professional identity in nursing interns: a cross-sectional study by Junhao Zhang, Lijia Wang, Xue Yang, Yuwei Yang, Xuehua Wu, Huaping Huang, Guirong Li

“…The clinical belongingness of nursing interns had a mediating effect on the relationship between anxiety and PI (β = −0.072, 95% confidence interval = −0.133 to −0.013, p &lt; 0.001), accounting for 40% of the total effect.ConclusionThe anxiety level of nursing interns can have a direct impact on the prediction of PI and an indirect influence on PI mediated by clinical belongingness. …”
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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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A scalable multi-modal learning fruit detection algorithm for dynamic environments by Liang Mao, Liang Mao, Zihao Guo, Mingzhe Liu, Yue Li, Linlin Wang, Jie Li

“…A sliding slice method is then employed to predict image targets, thereby reducing the miss rate of small targets.ResultsExperimental results demonstrate that the proposed model improves accuracy, recall, and mean average precision (mAP) by 9.5, 0.9, and 12.3 percentage points, respectively, compared to the original YOLOv5s model. …”
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ALKBH5 alleviates lower extremity arteriosclerosis by regulating ITGB1 demethylation and influencing macrophage polarization by Zeyu Guan, Xiaogao Wang, Chao Xu, Ran Lu

“…Additionally, Starbase2.0 was used for downstream target gene prediction of ALKBH5. The half-life of ITGB1 was evaluated using RT-qPCR. …”
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A new strategy for pharmacodynamic substance screening and research on gut microbiota pathway mechanisms based on UPLC-Q-orbitrap-MS and 16S rRNA by Zhiying Yu, Tong Li, Jie Yang, Jianghua He, Weijiang Zhang, Siyuan Li, Yunpeng Qi, Yihui Yin, Ling Dong, Wenjuan Xu

“…The abundance of gut microbiota involved in the metabolism of the prototype components was influenced by the corresponding components. The function prediction results showed that PUE was the most comparable to GQD, with 24 consistent pathways. …”
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The influencing factors of the erythropoietin resistance index and its association with all-cause mortality in maintenance hemodialysis patients by Xinju Zhao, Liangying Gan, Fan Fan Hou, Xinling Liang, Xiaonong Chen, Yuqing Chen, Zhaohui Ni, Li Zuo

“…ERI was associated with increased all-cause mortality in MHD patients, indicating the possibility of death prediction by ERI. Patients with high ERI warrant more attention.…”
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Investigation of a Novel Noninvasive Risk Analytics Algorithm With Laboratory Central Venous Oxygen Saturation Measurements in Critically Ill Pediatric Patients by Sarah A. Teele, MD, MSHPEd, Avihu Z. Gazit, MD, Craig Futterman, MD, William G. La Cava, PhD, David S. Cooper, MD, MPH, MBA, Steven M. Schwartz, MD, MS, Joshua W. Salvin, MD, MPH

“…Collected data included vital signs, ventilator data, laboratory data, and demographics. PREDICTION MODEL:. The ability of the IDo2 index to predict Svo2 below a preselected threshold (30%, 40%, or 50%) was evaluated for discriminatory power, range utilization, and robustness. …”
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Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis by Zhongli Wang, Zhongli Wang, Gaopei Zhu, Shixue Li, Shixue Li

“…Citation analysis highlighted two major breakthroughs: AlphaFold’s protein structure prediction (6,811 citations) and deep learning approaches to antibiotic discovery (4,784 citations). …”
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Discovery of a heparan sulfate binding domain in monkeypox virus H3 as an anti-poxviral drug target combining AI and MD simulations by Bin Zheng, Meimei Duan, Yifen Huang, Shangchen Wang, Jun Qiu, Zhuojian Lu, Lichao Liu, Guojin Tang, Lin Cheng, Peng Zheng

“…Using AI-based structural prediction tools and molecular dynamics (MD) simulations, we identified a novel, positively charged α-helical domain in H3 that is essential for HS binding. …”
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The significance of risk stratification through nomogram-based assessment in determining postmastectomy radiotherapy for patients diagnosed with pT1 − 2N1M0 breast cancer by Chao Wei, Jie Kong, Huina Han, Xue Wang, Zimeng Gao, Danyang Wang, Andu Zhang, Jun Zhang, Zhikun Liu

“…Abstract Objective To explore the high-risk factors affecting the prognosis of pT1 − 2N1M0 patients after mastectomy, establish a nomogram prediction model, and screen the radiotherapy benefit population. …”
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Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach by Dongmei Pei, Chengpu Zhang, Yu Quan, Qiyong Guo

“…With a strict data filtration, 10,436 records from the eligible participants were utilized to develop a prediction model using the J48 decision tree algorithm. …”
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Penerapan Decision Tree J48 dan Reptree dalam Menentukan Prediksi Produksi Minyak Kelapa Sawit menggunakan Metode Fuzzy Tsukamoto by Tundo Tundo, Shofwatul 'Uyun

“…From the data used the accuracy of the J48 decision tree is 95.2381%, while the REPTree accuracy is 90.4762%, but in this case the REPTree decision tree is more appropriate to be used in the prediction process of palm oil production, because it is tested with actual data in March 2019 uses REPTree obtained 16355835 liters, while using J48 obtained 11844763 liters, where the actual production data is 179,20000 liters. …”
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