Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System by Hongseok Jang, Yeonho Lee, Hongseok Lee, Youngtaek Cha, Sungjoon Choi, Jongkyu Park

“…The ERMS, mounted on a mobile crane, continuously monitors the ground conditions in real time and predicts the likelihood of ground subsidence to prevent accidents. …”
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Computational modelling and optimization of porous plates for mandibular fracture fixation accounting for bone healing by Ali Mehboob, Imad Barsoum, Hassan Mehboob, Abdelhak Ouldyerou, Rashid K. Abu Al-Rub

“…The healing process was predicted using a mechano-regulation algorithm. Conventional plate showed excessive interfragmentary strain (IFS) of approximately 1.4 and 0.6 in the 0.5 mm and 2.0 mm fracture gaps, respectively, which resulted in delayed healing. 2 MPP bone plates exhibited improved biomechanical performance with less stress in the bone plates and improved micromovement at the fracture site, promoting early bone union. …”
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Mammography in 2022, from Computer-Aided Detection to Artificial Intelligence Applications by Filippo Pesapane, Chiara Trentin, Marta Montesano, Federica Ferrari, Luca Nicosia, Anna Rotili, Silvia Penco, Mariagiorgia Farina, Irene Marinucci, Francesca Abbate, Lorenza Meneghetti, Anna Bozzini, Antuono Latronico, Alessandro Liguori, Giuseppe Carrafiello, Enrico Cassano

“…Breast imaging must face with the exponential growth in imaging requests (and consequently higher costs) and a predicted reduced number of trained radiologists to read imaging and provide reports. …”
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Approximation-Aware Training for Efficient Neural Network Inference on MRAM Based CiM Architecture by Hemkant Nehete, Sandeep Soni, Tharun Kumar Reddy Bollu, Balasubramanian Raman, Brajesh Kumar Kaushik

“…This architecture includes a mapping algorithm that modulates inputs and map AFC to crossbar arrays directly, eliminating the need to predict approximated weights for evaluating output. …”
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Crop choice advisory for the West African Sudan Savanna based on soil type and presowing rainfall forecasts: A machine learning residual model approach by Toshichika Iizumi, Kohtaro Iseki, Kenta Ikazaki, Toru Sakai, Shintaro Kobayashi, Benoit Joseph Batieno

“…Here, we present a modification of a process model simulation performed using a machine learning residual model trained to predict the error in the process model-simulated yields, relative to field experimental data, from growing conditions. …”
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The adaptive dynamic programming signal control system for person in a connected vehicle environment by Zongyuan Wu, Shiming LI, Gen LI, Ben Waterson, Luyao Zhu, Decai Wang

“…The generalized vehicle trajectory and car-following model is adopted for predicting the platoon discharge times considering different cases and fleet trajectories to enhance the responsiveness to CV data. …”
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Fusion of Medium- and High-Resolution Remote Images for the Detection of Stress Levels Associated with Citrus Sooty Mould by Enrique Moltó, Marcela Pereira-Sandoval, Héctor Izquierdo-Sanz, Sergio Morell-Monzó

“…The data were used to evaluate the performance of a Random Forest classifier in predicting intensity levels through cross-validation. …”
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The Immune‐Related Gene CD48 Is a Prognostic Biomarker Associated With the Breast Cancer Tumor Microenvironment by Yi Zhao, Hengheng Zhang, Wenwen Wang, Guoshuang Shen, Miaozhou Wang, Zhen Liu, Jiuda Zhao, Jinming Li

“…Conclusions CD48 may serve as an effective biomarker for predicting BCa patient prognosis and a potential immune‐related therapeutic target.…”
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Assessing the Spatial Benefits of Green Roofs to Mitigate Urban Heat Island Effects in a Semi-Arid City: A Case Study in Granada, Spain by Francisco Sánchez-Cordero, Leonardo Nanía, David Hidalgo-García, Sergio Ricardo López-Chacón

“…Green roofs (GRs) may minimize land surface temperature (LST) by modifying albedo. This research predicts, assesses, and measures the impact of reducing the LST by applying green roofs in buildings by using a Random Forest algorithm and different remote sensing methods. …”
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The Water Table Model (WTM) (v2.0.1): coupled groundwater and dynamic lake modelling by K. L. Callaghan, K. L. Callaghan, K. L. Callaghan, A. D. Wickert, A. D. Wickert, A. D. Wickert, R. Barnes, J. Austermann

“…However, we currently lack the capacity to simulate and predict these terrestrial water changes across the full range of relevant spatial (watershed to global) and temporal (monthly to millennial) scales. …”
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AIpollen: An Analytic Website for Pollen Identification Through Convolutional Neural Networks by Xingchen Yu, Jiawen Zhao, Zhenxiu Xu, Junrong Wei, Qi Wang, Feng Shen, Xiaozeng Yang, Zhonglong Guo

“…For the optimization algorithm, we opted for the Adam optimizer and utilized the cross-entropy loss function. …”
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Bayesian inference of radial impurity transport in the pedestal of ASDEX Upgrade discharges using charge-exchange spectroscopy by T. Gleiter, R. Dux, F. Sciortino, T. Odstrčil, D. Fajardo, C. Angioni, J. Buchner, R.M. McDermott, T. Hayward-Schneider, G.F. Harrer, M. Faitsch, M. Griener, R. Fischer, E. Wolfrum, U. Stroth, the ASDEX Upgrade Team

“…This supports the hypothesis of additional transport associated with the predicted high-n ballooning-unstable region and the observed quasi- coherent mode.…”
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Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors by Zhangli Lu, Huiying Zhou, Honghao Lyu, Haiteng Wu, Shaohua Tian, Geng Yang

“…The key limitations included: a limited generalizability to severely impaired patients who were unable to walk independently, and the inability to predict the score of individual tasks.…”
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Chemical Composition, Chemometric Analysis, and Sensory Profile of <i>Santolina chamaecyparissus L.</i> (<i>Asteraceae</i>) Essential Oil: Insights from a Case Study in Serbia and... by Biljana Lončar, Mirjana Cvetković, Milica Rat, Jovana Stanković Jeremić, Jelena Filipović, Lato Pezo, Milica Aćimović

“…Chemometric analysis proved effective in predicting the oil’s composition, and sensory evaluation revealed a herbal aroma with earthy, woody, and camphoraceous notes. …”
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Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods by Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani

“…A well-known outlier detection algorithm is applied on the gathered data to assess the data reliability before model development. …”
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Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach by Dan Ben Ami, Kobi Cohen, Qing Zhao

“…In this paper, we present a novel multi-armed bandit (MAB)-based approach for client selection to minimize the training latency without harming the ability of the model to generalize, that is, to provide reliable predictions for new observations. We develop a novel algorithm to achieve this goal, dubbed Bandit Scheduling for FL (BSFL). …”
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Deep Learning-Based Secrecy Performance of UAV-IRS NOMA Systems With Friendly Jamming by Kajal Yadav, Prabhat K. Upadhyay, Jules M. Moualeu, Amani A. F. Osman, Pedro H. J. Nardelli

“…Finally, a fully optimized deep neural network model is developed to predict the SOP under dynamic conditions. Numerical results demonstrate the efficacy of the proposed jamming-enabled system over its non-jamming counterpart. …”
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Research on power system transformation based on edge node technology under the background of carbon neutrality by LI Jin, GAO Hongliang, LIU Kemeng, XIE Hu

“…A stable time series is established according to the historical data of the power system, and then, the medium and long-term power demand is predicted through the exponential smoothing method as the basis of system transformation design. …”
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Machine Learning-Driven Optimization of Transport Layers in MAPbI&#x2083; Perovskite Solar Cells for Enhanced Performance by Velpuri Leela Devi, Piyush Kuchhal, Debasis de, Abhinav Sharma, Neeraj Kumar Shukla, Mona Aggarwal

“…In this research work, among those eight ML models, the XGBoost algorithm shows high accuracy for predicting the power conversion efficiency (PCE) of the cell, achieving root mean square error (RMSE) of 0.052 and a coefficient of determination (R2) of 0.999. …”
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Data-driven machine learning with lattice distortion and thermodynamic parameters guided strength optimization of refractory high-entropy alloys by Shujian Ding, Yifan Zhang, Siyang Lei, Xiang Weng, Wenhui Li, Wei Ren, Jian Chen, Weili Wang

“…To address this difficulty, a data-driven machine learning (ML) model was established to predict the compressive yield strength (σ0.2) of RHEAs, which provides a design strategy with two pivotal descriptors concerning lattice distortion and thermodynamic parameters. …”
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