What sustain Chinese adult second language (L2) learners' engagement in online classes? A sequential mix-methods study on the roles of L2 motivation and enjoyment. by Yanyan Li, Wentao Liu, Shuai Ren

“…Enjoyment was found to directly and positively predict engagement. However, contrary to existing literature, ideal L2 self did not directly predict either enjoyment or engagement. …”
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Covid-19 pandemic data analysis using tensor methods by Dipak Dulal, Ramin Goudarzi Karim, Carmeliza Navasca

“…Our method can predict weekly, and quarterly Covid-19 spreads with high accuracy. …”
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The Relationship Between Mental Health Literacy and Social Well-Being: A Longitudinal Study in China by Jiali Pan, Tianyu Xu, Dan Li

“…Furthermore, the across-lagged model was employed to determine the relationship between mental health literacy and social well-being over time, revealing that mental health literacy and social well-being merely have a unidirectional predictive relationship; social well-being at T1 can significantly and positively predict mental health literacy at T2, but mental health literacy at T1 cannot predict social well-being at T2. …”
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Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning by Won Joong Kim, Gunho Jung, Sun-Yong Choi

“…All the models were less predictable in highly volatile data periods than in less volatile periods. …”
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Association between the expression of specific microRNAs and prostate cancer progression- a systematic review and meta-analysis by Jihad Awadallah Alrehaili

“…For two studies investigating MiRNAs as biomarkers for predicting metastasis, the pooled OR was 0.60 (95% CI: 0.47–0.76) and the RR was 0.72 (95% CI: 0.62–0.84), both demonstrating significant predictive value with low heterogeneity (I² = 0%).ConclusionThe results emphasize the potential of miRNAs as biomarkers for predicting PrCa metastasis and demonstrate that miRNAs have a discernible effect on PrCa proliferation. …”
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Vibrations of Flat-End Cutter Entering Workpiece Process: Modeling, Simulations, and Experiments by Ming Luo, Qi Yao

“…Aiming at in-depth understanding the mechanism of this phenomenon, this paper presents a novel time-domain simulation model to predict the dynamic response of the cutter during the entering process. …”
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Life Cycle of the Lesser Cornstalk Borer in Sugarcane by Hardev S. Sandhu, Leslie E. Baucum, Gregg S. Nuessly

“…But since the populations of this pest vary greatly depending on temperature, UF/IFAS scientists are developing predictive models to predict lesser cornstalk borer population in a field. …”
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Life Cycle of the Lesser Cornstalk Borer in Sugarcane by Hardev S. Sandhu, Leslie E. Baucum, Gregg S. Nuessly

“…But since the populations of this pest vary greatly depending on temperature, UF/IFAS scientists are developing predictive models to predict lesser cornstalk borer population in a field. …”
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Immunostimulatory/Immunodynamic model of mRNA‐1273 to guide pediatric vaccine dose selection by Vijay Ivaturi, Husain Attarwala, Weiping Deng, Baoyu Ding, Sabine Schnyder Ghamloush, Bethany Girard, Javid Iqbal, Saugandhika Minnikanti, Honghong Zhou, Jacqueline Miller, Rituparna Das

“…The developed model was used to predict the optimal vaccine dose for future pediatric trials. …”
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Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses by Ying Xiong, Linpeng Yao, Jinglai Lin, Jiaxi Yao, Qi Bai, Yuan Huang, Xue Zhang, Risheng Huang, Run Wang, Kang Wang, Yu Qi, Pingyi Zhu, Haoran Wang, Li Liu, Jianjun Zhou, Jianming Guo, Feng Chen, Chenchen Dai, Shuo Wang

“…Two multi-phase convolutional neural networks are developed to predict the malignancy and aggressiveness of renal masses. …”
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Forecasting GICs and Geoelectric Fields From Solar Wind Data Using LSTMs: Application in Austria by R. L. Bailey, R. Leonhardt, C. Möstl, C. Beggan, M. A. Reiss, A. Bhaskar, A. J. Weiss

“…The GICs are generally predicted better by an LSTM trained on values from a specific substation, but only a fraction of the largest GICs are correctly predicted. …”
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Real world federated learning with a knowledge distilled transformer for cardiac CT imaging by Malte Tölle, Philipp Garthe, Clemens Scherer, Jan Moritz Seliger, Andreas Leha, Nina Krüger, Stefan Simm, Simon Martin, Sebastian Eble, Halvar Kelm, Moritz Bednorz, Florian André, Peter Bannas, Gerhard Diller, Norbert Frey, Stefan Groß, Anja Hennemuth, Lars Kaderali, Alexander Meyer, Eike Nagel, Stefan Orwat, Moritz Seiffert, Tim Friede, Tim Seidler, Sandy Engelhardt

“…Our two-step semi-supervised strategy distills knowledge from task-specific CNNs into a transformer. First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads. …”
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The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models by Qingzeng Song, Junting Xu, Lei Ma, Ping Yang, Guanghao Jin

“…On a testing sample, the outputs of all trained models are used to predict the domain of this sample. Then, this sample is classified by the output of models that belong to the predicted domain. …”
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Analisis Perbandingan Simulasi Injeksi Plastik Terhadap Hasil Produk Dalam Software Autodesk Moldflow Pada Produk Air Filter by Zacky Fahd Mustafa, Oleh, Iwan Nugraha Gusniar

“…In the production results, defects were found that had been predicted by previous simulations. The simulation results can be considered to be able to predict production defects that will occur in the production process.…”
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Boosting skin cancer diagnosis accuracy with ensemble approach by Priya Natha, Sivarama Prasad Tera, Ravikumar Chinthaginjala, Safia Obaidur Rab, C. Venkata Narasimhulu, Tae Hoon Kim

“…We demonstrate that the Max Voting method greatly improves predictive performance, reaching an accuracy of 94.70% and producing the best results for F1-measure, recall, and precision. …”
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Enhancing Infection Control in ICUS Through AI: A Literature Review by Aditya Amit Godbole, Paras, Maanya Mehra, Sumitaksha Banerjee, Poulami Roy, Novonil Deb, Sarang Jagtap

“…Discussion The review highlights AI's prowess in predicting HAIs, surpassing conventional methods. Existing evidence demonstrates AI's efficacy in accurately predicting and mitigating HAIs. …”
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Investigation of sensory attenuation in the somatosensory domain using EEG in a novel virtual reality paradigm by Gianluigi Giannini, Till Nierhaus, Felix Blankenburg

“…Together, our results demonstrate that VR opens up new possibilities to study sensory attenuation in more ecological valid yet well-controlled paradigms, and that sensory attenuation is not significantly modulated by stimulus predictability, suggesting that sensory attenuation relies on motor-specific predictions about their sensory outcomes. …”
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Performance of digital early warning score (NEWS2) in a cardiac specialist setting: retrospective cohort study by Amitava Banerjee, Riyaz Patel, Baneen Alhmoud, Daniel Melley, Tim Bonnici

“…Standardisation and integrated National Early Warning Score 2 (NEWS2) in electronic health records (EHRs) are recommended yet have not been evaluated in specialist settings.Objective To investigate the performance of digital NEWS2 in predicting critical events: death, intensive care unit (ICU) admission, cardiac arrest and medical emergencies.Methods Retrospective cohort analysis.Study cohort Individuals admitted with CVD diagnoses in 2020; including patients with COVID-19 due to conducting the study during the COVID-19 pandemic.Measures We tested the ability of NEWS2 in predicting the three critical outcomes from admission and within 24 hours before the event. …”
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Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring by Fang Li, Shengguo Wang, Zhi Gao, Maofeng Qing, Shan Pan, Yingying Liu, Chengchen Hu

“…Beyond these advancements, AI enhances diagnostic accuracy, predicts long-term outcomes, and supports dynamic risk assessment in clinical settings. …”
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Leveraging AI to improve disease screening among American Indians: insights from the Strong Heart Study by Paul Rogers, Thomas McCall, Ying Zhang, Jessica Reese, Dong Wang, Weida Tong

“…A screening test’s positive predictive value (PPV) is susceptible to the prevalence of the outcome. …”
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