A Numerical Laboratory for Simulation of Flanged Reinforced Concrete Shear Walls by Alireza Khaloo, Mohammad Tabiee, Hatef Abdoos

“…In order to validate FlashLab, this paper numerically simulates T-, H-, and L-shaped RC shear walls and compares the results to the experimental data, indicating good agreement between the numerical and experimental results to an extent that the proposed numerical laboratory is capable of predicting the backbone curve with an accuracy more than 90 percent. …”
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Reconsidering CPR protocols: Exploring hypernatremia's role in ventricular tachycardia from a case report by Arthur Gavotto, Jean-Yves Le Guennec, Cyril Amouroux, Pascal Champeroux, Angelo Polito, Jérôme Thireau, Alain Lacampagne, Gilles Cambonie

“…Conclusion: In silico modelling can help to predict arrhythmia risk and to select the best strategies. …”
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Neural Networks in Forecasting Disease Dynamics by A. G. Hasanov, D. G. Shaybakov, S. V. Zhernakov, A. M. Men’shikov, F. F. Badretdinova, I. F. Sufiyarov, J. R. Sagadatova

“…The best convergence of the network learning process is provided by the quasi-Newton and conjugated gradient algorithms. In order to assess the effectiveness of the proposed neural network in predicting the dynamics of inflammation, a comparative analysis was carried out using a number of conventional methods, such as exponential smoothing, moving average, least squares and group data handling.Conclusion.The proposed neural network based on approximation and extrapolation of variations in the patient’s medi‑ cal history over fixed time window segments (within the ‘sliding time window’) can be successfully used for forecasting the development and outcome of erysipelas.…”
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Healthcare Economics-Vital Threat Detection System Efficiency: A Business Process Analysis Methodology by Salma Elhag, Amal Alghamdi, Amal Alahmari, Abeer Alharbi

“…The data is sent to the cloud, where it is stored and analyzed using artificial intelligence (AI) algorithms to predict critical health conditions. When any abnormalities are detected, immediate alerts are sent to family members or caregivers via mobile applications, enabling a quick response. …”
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Advancing agriculture with machine learning: a new frontier in weed management by Mohammad MEHDIZADEH, Duraid K. A. AL-TAEY, Anahita OMIDI, Aljanabi Hadi Yasir ABBOOD, Shavan ASKAR, Soxibjon TOPILDIYEV, Harikumar PALLATHADKA, Renas Rajab ASAAD

“…Validation and refinement of these algorithms are needed for practical application.…”
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Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability by Amir Reza Nikzad, Amr Adel Mohamed, Bala Venkatesh, John Penaranda

“…The method is tested using a North American utility feeder data, achieving an average accuracy of 95.56% for forecasting aggregate load power, 93.70% for feeder flow predictions, and 97.53% for estimating the aggregate capacity of DERs.…”
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Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis by Renhai Feng, Khan Wajid, Muhammad Faheem, Jiang Wang, Fazal E. Subhan, Muhammad Shoaib Bhutta

“…Moreover, its emphasis the importance of computed and estimated indices obtained through UPINN for predicting voltage collapse occurrences within the system.…”
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Analysis of State-of-Charge Estimation Methods for Li-Ion Batteries Considering Wide Temperature Range by Yu Miao, Yang Gao, Xinyue Liu, Yuan Liang, Lin Liu

“…Accurate state-of-charge (SOC) estimation is critical for optimizing battery performance, ensuring safety, and predicting battery lifetime. However, SOC estimation faces significant challenges under extreme temperatures and complex operating conditions. …”
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Comprehensive improvement of energy efficiency and indoor environmental quality for university library atrium—A multi-objective fast optimization framework by Shen Xu, Yongzhong Chen, Jianlin Liu, Jian Kang, JinFeng Gao, Yuchen Qin, Wenjun Tan, Gaomei Li

“…The machine learning models predicted performance 400 times faster than traditional performance simulations. …”
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Toward Street‐Level Nowcasting of Flash Floods Impacts Based on HPC Hydrodynamic Modeling at the Watershed Scale and High‐Resolution Weather Radar Data by Pierfranco Costabile, Carmelina Costanzo, John Kalogiros, Vasilis Bellos

“…This approach paves the way for the development of new operational systems focused on impact‐based flash‐floods nowcasting, wherein hydrodynamic simulations directly model the spatial and temporal variability of measured or predicted rainfall on impacts even at a street scale. …”
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Real-time congestion control using cascaded LSTM deep neural networks for deregulated power markets by G. Madhu Mohan, T. Anil Kumar, A. Srujana, Yasser Fouad, Alexey Mikhaylov, Nora Baranyai, Kitmo, Ch. Rami Reddy

“…The proposed CM system consists of three cascaded long short-term memory (LSTM) DNNs that operate sequentially. These LSTM modules predict congestion status, violated power, and adjusted active power for rescheduling generation. …”
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A survey on exploring the challenges and applications of wireless body area networks (WBANs) by Arun Sekar Rajasekaran, L. Sowmiya, Azees Maria, R. Kannadasan

“…Due to the arrival of the Micro Electromechanical System (MEMS) and several intelligent sensors, collaboration with WBAN makes accurate predictions of parameters in the human body. WBAN has numerous applications in medical and non-medical fields. …”
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Does the hepatologist still need to rely on aminotransferases in clinical practice? A reappraisal of the role of a classic biomarker in the diagnosis and clinical management of chr... by Patrizia Burra, Calogero Cammà, Pietro Invernizzi, Fabio Marra, Maurizio Pompili

“…ALT, a sensitive and cost-effective marker of liver injury, remains pivotal in predicting clinical outcomes and guiding interventions in several chronic liver diseases including metabolic dysfunction-associated steatotic liver disease, and chronic viral hepatitis. …”
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Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability by Danish Raza, Hong Shu, Muhsan Ehsan, Hong Fan, Kamal Abdelrahman, Hasnat Aslam, Abdul Quddoos, Rana Waqar Aslam, Majid Nazeer, Mohammed S. Fnais, Azeem Sardar

“…First, by incorporating more than three decades of satellite data (1990–2022) and different Landsat missions with machine learning algorithms, high-confidence classes were defined for different land features, including cropland. …”
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The role of hematological parameters as predictors of mortality in elderly patients in the hospital period by L. V. Borisova, S. A. Rukavishnikova, A. S. Pushkin, T. A. Ahmedov, V. V. Yakovlev

“…The development of algorithms for predicting adverse outcomes, including death in elderly and senile patients, is an urgent issue. …”
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Biosynthesis, applications, and mathematical modeling dynamics of lactic acid bacteria exopolysaccharides: A review by Ankit Barot, Kalyan Das, Yogesh Patel, et al.

“…Global models, such as flux balance analysis (FBA), integrate genome-scale networks to predict cellular behavior and optimize production conditions. …”
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Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies by Rahul Kumar, Ethan Waisberg, Joshua Ong, Phani Paladugu, Dylan Amiri, Jeremy Saintyl, Jahnavi Yelamanchi, Robert Nahouraii, Ram Jagadeesan, Alireza Tavakkoli

“…When integrated with artificial intelligence (AI) algorithms, these techniques achieve unprecedented diagnostic precision, facilitating early detection of neurodegeneration and inflammation. …”
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Automatic maxillary sinus segmentation and age estimation model for the northwestern Chinese Han population by Yu-Xin Guo, Jun-Long Lan, Wen-Qing Bu, Yu Tang, Di Wu, Hui Yang, Jia-Chen Ren, Yu-Xuan Song, Hong-Ying Yue, Yu-Cheng Guo, Hao-Tian Meng

“…The regression model performed best, with mean absolute errors (MAE) of 1.45 years (under 18) and 3.51 years (aged 18 and above), providing relatively precise age predictions. Conclusion The maxillary sinus-based model is a promising tool for age estimation, particularly in adults, and could be enhanced by incorporating additional variables like dental dimensions.…”
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Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning by Ranran Kou, Cong Wang, Jinxia Liu, Ran Wan, Zhe Jin, Le Zhao, Youjie Liu, Junwei Guo, Feng Li, Hongbo Wang, Song Yang, Cong Nie

“…In recent years, near-infrared (NIR) spectroscopy combined with algorithmic models has emerged as a popular method for identifying the tobacco leaf position. …”
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A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods by Francisco Lopez-Jimenez, Heather M. Alger, Zachi I. Attia, Barbara Barry, Ranee Chatterjee, Rowena Dolor, Paul A. Friedman, Stephen J. Greene, Jason Greenwood, Vinay Gundurao, Sarah Hackett, Prerak Jain, Anja Kinaszczuk, Ketan Mehta, Jason O'Grady, Ambarish Pandey, Christopher Pullins, Arjun R. Puranik, Mohan Krishna Ranganathan, David Rushlow, Mark Stampehl, Vinayak Subramanian, Kitzner Vassor, Xuan Zhu, Samir Awasthi

“…Background: Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular conditions using electrocardiogram (ECG) data. …”
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