Deep learning-based prediction of individualized Real-time FSH doses in GnRH agonist long protocols by Na Kong, Yu Xia, Zhilong Wang, Hui Zhang, Liyan Duan, Yingchun Zhu, Chenyang Huang, Guijun Yan, Jie Mei, Wujun Li, Haixiang Sun

“…Abstract Background Individualizing follicle-stimulating hormone (FSH) dosing during controlled ovarian stimulation (COS) is critical for optimizing outcomes in assisted reproduction but remains difficult due to patient heterogeneity. …”
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Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing by Shizhe Li, Chunzhi Fan, Ali Kargarandehkordi, Yinan Sun, Christopher Slade, Aditi Jaiswal, Roberto M. Benzo, Kristina T. Phillips, Peter Washington

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Online Prediction of Concrete Temperature During the Construction of an Arch Dam Based on a Sparrow Search Algorithm–Incremental Support Vector Regression Model by Yihong Zhou, Yu Deng, Fang Wang, Chunju Zhao, Huawei Zhou, Zhipeng Liang, Lei Lei

“…First, the SSA was employed to optimize the penalty and kernel coefficients of the ISVR algorithm, minimizing errors between predicted and measured temperatures to establish a pretrained initial temperature prediction model. …”
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Statistical learning-driven parameter tuning in injection molding using modified simplex method by Pongchanun Luangpaiboon, Walailak Atthirawong, Anucha Hirunwat, Pasura Aungkulanon

“…The optimization of product parameters, including length and standard deviation, is a persistent challenge, despite the fact that injection molding is a critical manufacturing technique. …”
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A Deep Learning-Based Approach for Predicting Michaelis Constants from Enzymatic Reactions by Yulong Li, Kai Wang

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Research on Change Point Detection during Periods of Sharp Fluctuations in Stock Prices–Based on Bayes Method <i>β</i>-ARCH Models by Fenglin Tian, Yong Wang, Qi Qin, Boping Tian

“…In periods of dramatic stock price volatility, the identification of change points in stock price time series is important for analyzing the structural changes in financial market data, as well as for risk prevention and control in the financial market. As their residuals follow a generalized error distribution, the problem of estimating the change point parameters of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-ARCH model is solved by combining the Kalman filtering method and the Bayes method innovatively, and we give a method for parameter estimation of the Bayes factors for the occurrences of change points, the expected values of the change point positions, and the variance of the change points. …”
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Improved food recognition using a refined ResNet50 architecture with improved fully connected layers by Pouya Bohlol, Soleiman Hosseinpour, Mahmoud Soltani Firouz

“…ResNet50 with a specific dense layer was the best development version of ResNet50. This model with Adam optimizer, 10−3 initial learning rate, batch size 4, and image size 340 × 640 could recognize various foods with 97.25% accuracy and 0.2 loss. …”
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Development and validation of a risk-adjustment model for mortality and hospital length of stay for trauma patients: a prospective registry-based study in Australia by Peter Cameron, Arul Earnest, Kate Curtis, Gerard O'Reilly, Sudhakar Rao, Cameron Palmer, Maxine Burrell, Emily McKie

“…Current risk adjustment models are not optimal in terms of the number and nature of predictor variables included in the model and the treatment of missing data. …”
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Personalized dose reduction strategies for biologic disease-modifying antirheumatic drugs for treating axial spondyloarthritis: a clinical and economic evaluation with predictive m... by Bui Hai Binh, Nguyen Thi Thu Phuong, Vu Thi Thanh Hang, Ngo Thi Thuc Nhan, Nguyen Thi Nhu Hoa, Hoang Van Dung

“…Conclusions In this cohort, bDMARD dose reduction was associated with preserved clinical outcomes and lower costs, suggesting it may be a viable strategy for selected patients under close clinical supervision. Predictive modeling provided actionable insights to optimize personalized treatment strategies, balancing efficacy and economic sustainability. …”
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A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach by Yuli Wang, Na Mei, Ziyi Zhou, Yuan Fang, Jiacheng Lin, Fanchen Zhao, Zhihong Fang, Yan Li

“…Finally, critical variables in the optimal model were screened based on the interpretable algorithms to build a decision tree to facilitate clinical application. …”
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The Construction Level of Health Literate Health Organizations and Its Impact on Patients’ Health Literacy: Based on Self-Determination Theory and Structural Equation Modeling by Renjie Lu PhD, Jing Zhou MD, Jiaying Ge BD, Xiyang Xia MD, Chao Lei MD, Shenyu Zhao BD, Dan Shen BD, Xiaoyu Wang MD, Jiaqian Chang BD, Yang Chen MD, Lingmin Hu PhD

“…A total of 6411 questionnaires were collected. Following quality control standards, the remaining sample of 5206 valid responses met the minimum recommended size for structural equation modeling. …”
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In vivo evaluation of phage therapy against Klebsiella pneumoniae using the Galleria mellonella model and molecular characterization of a novel Drulisvirus phage species by Gustavo Quispe-Villegas, Gabriela I. Alcántara-Lozano, Diego Cuicapuza, Raúl Laureano, Brenda Ayzanoa, Pablo Tsukayama, Jesús Tamariz

“…The therapeutic efficacy of the phage was evaluated using MDR Klebsiella-infected G. mellonella larvae as an in vivo model. Phage titers and larva survival were compared in phage-treated and control groups. …”
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Historical overview, advancements, and future prospects of coalfield structure research in China by Daiyong CAO, Yiwen JU, Yucheng XIA, Bo JIANG, Shuzheng NING, Yingchun WEI, Anmin WANG, Xiaoming LI, Wenfeng ZHAN, Huantong LI, Lu WANG, Zhuocheng CAI

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Breaking barriers: targeted delivery of resveratrol in lung cancer using folate-integrated and pH-responsive hybrid nanocarriers by Sanjoy Das, Malay K. Das, Taison Jamatia, Nayan Ranjan Ghose Bishwas, Dhritiman Roy, Emdormi Rymbai

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Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study by Michael S Deiner, Russell Y Deiner, Cherie Fathy, Natalie A Deiner, Vagelis Hristidis, Stephen D McLeod, Thomas J Bukowski, Thuy Doan, Gerami D Seitzman, Thomas M Lietman, Travis C Porco

“…However, monitoring social media content for conjunctivitis outbreaks is costly and laborious. Large language models (LLMs) could overcome these barriers by assessing the likelihood that real-world outbreaks are being described. …”
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Rheological properties study of high-viscosity asphalt based on direct coal liquefaction residue by Liu Yong, Wang Yong, Ru Yanyan, Li Yandan, Li Yongxiang, Gao Zhong

“…The zero-shear viscosity (ZSV) values obtained by fitting the Carreau model indicated that within the temperature range of 46°C–64°C, the ZSV values of the DCLR modified asphalt were 1-2 orders of magnitude higher than those of the control samples, and the asphalt still maintained stable viscoelasticity at high temperatures (64°C), demonstrating its outstanding interfacial bonding performance. …”
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Dispatching Strategy of Park-Level Integrated Energy System Considering Carbon Trading Mechanism and Hydrogen Blending Natural Gas by Dongshun ZHANG, Hengli QUAN, Hua XIE, Zhihong XU, Yayun TAO, Huisheng WANG

“…With the objective of minimizing the operational cost of the system, an optimization and dispatching model for the park’s comprehensive energy system is established under a tiered carbon trading mechanism. …”
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A comprehensive study of recent maximum power point tracking techniques for photovoltaic systems by Mohammed Hamouda Ali, Mohammad Zakaria, Sally El-Tawab

“…Finally, the artificial neural network (ANN) and fuzzy logic control (FLC) techniques have been used as AI methods. …”
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The Use of Artificial Intelligence Technologies in Energy and Climate Security by Igbal A. Guliev, Agil Mammadov, Kanan Ibrahimli

“…The authors conclude that the truth of the hypothesis has been proven: the use of AI as a control feedback loop at a technical facility for purification and energy generation is a more cost-effective and technically optimal alternative to a “live” operator, which will eliminate the human error factor. …”
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Classification Based on the Support Vector Machine for Determining Operational Targets for Controlling Electricity Usage With Conventional Meters: A Case Study of Industrial and Bu... by Galih Arisona, Alief Pascal Taruna, Dwi Irwanto, Arif Bijak Bestari, Wildan Juniawan

“…Results show that the SVM model, particularly with the RBF kernel, achieves optimal performance, with balanced precision and recall, especially with 30 months of historical data. …”
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