Contribution of dust emissions from farmland to particulate matter concentrations in North China Plain: Integration of WRF-Chem and WEPS model by Haopeng Zhang, Feng Wang, Shenghui Zhou, Tianning Zhang, Minghui Qi, Hongquan Song

“…This study integrated the Wind Erosion Prediction System (WEPS) model with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to investigate the effect of agricultural dust emissions on PM10 concentrations in the NCP. …”
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Spectrum-effect relationship of immunologic activity of Ganoderma lucidum by UPLC-MS/MS and component knock-out method by Changqin Li, Yiping Cui, Jie Lu, Lijun Meng, Changyang Ma, Zhenhua Liu, Yan Zhang, Wenyi Kang

“…HPLC fingerprint and spectrum-effect relationship of G. lucidum were established to predict the active compounds and BP neural network model was established to predict the efficacy. …”
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Comprehensive Assessment of the Risk of Symptomatic Embolism in Patients With Infective Endocarditis by Sebastian D. Santos‐Patarroyo, Juan A. Quintero‐Martinez, Brian D. Lahr, Supavit Chesdachai, Daniel C. DeSimone, Hector R. Villarraga, Hector I. Michelena, Larry M. Baddour

“…A nomogram that incorporated these factors was constructed to facilitate the prediction of embolic risk. Conclusions Highly mobile, larger vegetations are associated with embolic events. …”
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Aerodynamic energy consumption analysis of divided evacuated tube transportation system by Ziwei Zhang, Yingxue Wang, Guanqing Wang, Feilong Li, Dengke Wang, Jianjun Luo

“…By figuring out the spatiotemporal variation of tube gas, the theoretical energy estimation scheme for aerodynamic energy consumption with various cases was put forward, then the rationality and accuracy of which was verified by numerical simulation with prediction error less than 8%. The saving ratio of aerodynamic drag energy consumption of a 10 km tube could be 39.9% compared to traditional tunnels, which will be higher under ultra-long mileage, thus realising the overall aerodynamic energy saving effect. …”
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The causal relationship of cigarette smoking to metabolic disease risk and the possible mediating role of gut microbiota by Jingda Zhang, Lin Hou, Shanxiang Lei, Yan Li, Guogang Xu

“…Furthermore, to mitigate the impact of confounding factors, adjusted models were conducted via the multivariate Mendelian randomization (MVMR) method, aiming to improve the accuracy of prediction. Ultimately, the study evaluated the effect of the intermediary factor, gut microbiome, on the relationship between cigarette smoke and metabolic diseases. …”
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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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Combined Treatment With Leuprolide Acetate and Burosumab in X-linked Hypophosphatemia and Precocious Puberty: A Therapeutic Response by Gustavo Tempone Cardoso Penna, MS, Carolina Costa Figueiredo, MD, MSc, Nara Michelle de Araújo Evangelista, MD, MSc, Vânia de Fátima Tonetto Fernandes, MD, PhD, Patricia Salmona, MD, Guido de Paula Colares Neto, MD, PhD

“…The combination of these treatments led to stabilized bone age, normalized growth velocity, and improved final height prediction without side effects or negative impacts on bone health during treatment. …”
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Characteristics and Associated Factors of Insomnia Among the General Population in the Post-Pandemic Era of COVID-19 in Zhejiang, China: A Cross-Sectional Study by Da M, Mou S, Hou G, Shen Z

“…Six machine learning models were employed to develop a predictive model for insomnia, namely logistic regression, random forest, neural network, support vector machine, CatBoost, and gradient boosting decision tree.Results: The study obtained 2769 and 1161 valid responses in T1 and T2, respectively. …”
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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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Protective effect and mechanism of Sufentanil on acute lung injury in septic mice by Hongqiao Hou, Bowen Jiang, Aiqing Zhu, Junjun Hou, Zhe Qu, Ruping Liu, Aiqun Li

“…To this end, a protein-protein interaction (PPI) network for common targets was first constructed with Swiss Target Prediction Database, GeneCards Database, Draw Venn Diagram Software, STRING 11.5 Database, Cytoscape 3.10.0 Software and Metascape Database, and then key targets were subject to enrichment analysis by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to obtain the key targets of Sufentanil for the treatment of pulmonary sepsis, and then verified by animal experiments. …”
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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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