A multi-scene deep learning model for automated segmentation of acute vertebral compression fractures from radiographs: a multicenter cohort study by Hao Zhang, Genji Yuan, Ziyue Zhang, Xiang Guo, Ruixiang Xu, Tongshuai Xu, Xin Zhong, Meng Kong, Kai Zhu, Xuexiao Ma

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Use of Machine Learning to Predict the Incidence of Type 2 Diabetes Among Relatively Healthy Adults: A 10-Year Longitudinal Study in Taiwan by Ying-Qiang Liu, Tzu-Wei Chang, Lung-Chun Lee, Chia-Yu Chen, Pi-Shan Hsu, Yu-Tse Tsan, Chao-Tung Yang, Wei-Min Chu

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Deep‐learning approaches to identify critically Ill patients at emergency department triage using limited information by Joshua W. Joseph, Evan L. Leventhal, Anne V. Grossestreuer, Matthew L. Wong, Loren J. Joseph, Larry A. Nathanson, Michael W. Donnino, Noémie Elhadad, Leon D. Sanchez

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Machine learning enhanced ultra-high vacuum system for predicting field emission performance in graphene reinforced aluminium based metal matrix composites by Sunil Kumar Pradhan, Subhayu Kabiraj, Shivin Kumar Gupta, Abhishek Singh, Padmakar G. Chavan, Shubham S. Patil, Trilok Nath Pandey

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Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy by Kimji N. Pellano, Inga Strumke, Daniel Groos, Lars Adde, Espen F. Alexander Ihlen

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Analysis of Antifungal Drug Resistance Among Candida Spp. and Other Pathogenic Yeasts Isolated from Patients in Eastern Poland: Diagnostic Problems by Olender A, Bogut A, Dąbrowski W, Pietrzak DJ, Szukała M, Wójtowicz-Bobin M, Kubera D, Dróżdż A, Stepulak A, Gagoś M

“…The wide spectrum of isolated species may complicate the establishment of a targeted antifungal therapy due to the lack of reference MIC ranges for the interpretation of antibiograms. Gradient strips are an accurate, reproducible, and convenient method for MIC determination.Keywords: antifungal resistance, Candida, rare Candida species, antifungal susceptibility testing…”
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Refining source-specific lung cancer risk assessment from PM2.5-bound PAHs: Integrating component-based potency factors and machine learning in Ningbo, China by Lord Famiyeh, Ke Chen, Fiseha Berhanu Tesema, Celeb Kelly, Dongsheng Ji, Hang Xiao, Lei Tong, Zongshuang Wang, Jun He

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A new risk assessment model of venous thromboembolism by considering fuzzy population by Xin Wang, Yu-Qing Yang, Xin-Yu Hong, Si-Hua Liu, Jian-Chu Li, Ting Chen, Ju-Hong Shi

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Machine Learning Aided Tapered Four-Port MIMO Antenna for V2X Communications With Enhanced Gain and Isolation by Nagesh Kallollu Narayanaswamy, Yazeed Alzahrani, Krishna Kanth Varma Penmatsa, Ashish Pandey, Ajay Kumar Dwivedi, Vivek Singh, Manoj Tolani

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Explainable Machine Learning for Radio Environment Mapping: An Intelligent System for Electric Field Strength Monitoring by Yiannis Kiouvrekis, Theodor Panagiotakopoulos, Efthymia Nousi, Ioannis Filippopoulos, Agapi Ploussi, Ellas Spyratou, Efstathios P. Efstathopoulos

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A numerical study of the effect of graphene nanoparticle size on brownian displacement, thermophoresis, and thermal performance of graphene/water nanofluid by molecular dynamics si... by Heider H.J. Almutter, Waqed H. Hassan, Shaymaa Abed Hussein, Dheyaa J. Jasim, Soheil Salahshour, Nafiseh Emami

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Automatic detection and visualization of temporomandibular joint effusion with deep neural network by Yeon-Hee Lee, Seonggwang Jeon, Jong-Hyun Won, Q.-Schick Auh, Yung-Kyun Noh

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A first principle investigation to explore the effect of Zr-site Ti doping on structural, electronic, optical, and mechanical properties of BaZrO3 by Apon Kumar Datta, M. Khalid Hossain, M. S. Revathy, M. Sudhakara Reddy, Abhayveer Singh, S. Radhika, Satish Choudhury, Ankur Singh Bisht, Abdullah M. S. Alhuthali, Magda H. Abdellattif, R. Balachandran, Rajesh Haldhar

“…This investigation delves into the comprehensive assessment of structural, electronic, optical, mechanical, and thermodynamic properties in Ti-doped BaZrO3 (BaZr1-xTixO3 where x = 0,0.25, 0.5, 0.75) through the application of Density Functional Theory (DFT) employing the Generalized Gradient Approximation (GGA) and Perdew-Burke-Ernzerhof (PBE) exchange–correlation function. …”
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A robust tuned EfficientNet-B2 using dynamic learning for predicting different grades of brain cancer by A. A. Abd El-Aziz, Mohammed Elmogy, Sameh Abd El-Ghany

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Strain Fluctuations Unlock Ferroelectricity in Wurtzites by Steven M. Baksa, Simon Gelin, Seda Oturak, R. Jackson Spurling, Alireza Sepehrinezhad, Leonard Jacques, Susan E. Trolier‐McKinstry, Adri C. T. van Duin, Jon‐Paul Maria, Andrew M. Rappe, Ismaila Dabo

“…Concurrent experimental and computational evidence of these effects are provided by demonstrating polarization switching in ZnO/(Zn,Mg)O/ZnO heterostructures featuring built‐in interfacial strain gradients. …”
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Electron Beam Additive Manufacturing of SS316L with a Stochastic Scan Strategy: Microstructure, Texture Evolution, and Mechanical Properties by K. N. Chaithanya Kumar, Shashank Sharma, Madhavan Radhakrishnan, Rohit Randhavan, Krishna Kamlesh Verma, Shelden Dowden, Zane Weldon Hughes, Rajarshi Banerjee, Narendra B. Dahotre

“…Microstructural analysis indicated a generation of columnar grains from the bottom to the top of the build owing to high thermal gradients. A crystallographic texture investigation showed a generation of strong <110> fiber texture along the build direction of the material and reported that the stress distributions within the melt pool led to a strong crystallographic texture driven by the stress evolution observed from thermokinetic computational modelling of the electron beam-melting process. …”
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Gyrokinetic profile prediction and validation of a negative triangularity plasma in ASDEX Upgrade by R. Bielajew, B. Vanovac, N.T. Howard, P. Rodriguez-Fernandez, L. Aucone, G.D. Conway, T. Happel, C. Holland, P. Mantica, J. Schellpfeffer, C. Yoo, A.E. White, the EUROfusion WPTE Team, the ASDEX Upgrade Team

“…In this work, gyrokinetic simulations are performed with the CGYRO code (Candy et al 2016 J. Comput. Phys. 324 73–93) for a negative triangularity H-mode plasma in ASDEX Upgrade, and compared with experimental measurements. …”
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Risk Factors for Gout in Taiwan Biobank: A Machine Learning Approach by Liu YR, Nfor ON, Zhong JH, Lin CY, Liaw YP

“…Five machine learning models were used: Bayesian Network (BN), Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR), and Neural Network (NN). …”
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AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

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Intra-day dispatch method via deep reinforcement learning based on pre-training and expert knowledge by Yanbo Chen, Qintao Du, Huayu Dong, Tao Huang, Jiahao Ma, Zitao Xu, Zhihao Wang

“…In this paper, we construct a reinforcement agent for intra-day dispatch to optimize generator output, using a twin delayed deep deterministic policy gradient algorithm based on pre-training and expert knowledge (PEK-TD3). …”
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