High-precision deformation monitoring and intelligent early warning for wellbore based on BDS/GNSS. by Jiang Li, Lei Dai, Keke Xu, Xinyu Mei, Yifu Liu, Jianlin Shi, Hebing Zhang

“…To address the complex deformation of wellbores influenced by surrounding coal mining operations, this study employed an improved modified least-squares ambiguity decorrelation (MLAMBDA) algorithm based on the double-difference model for high-frequency dynamic computation of Bei Dou System and Global Navigation Satellite System (BDS/GNSS) observation data. …”
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Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state by Aydin Larestani, Sara Sahebalzamani, Abdolhossein Hemmati-Sarapardeh, Ali Naseri

“…Among the tested models, the extra trees (ET) algorithm demonstrated superior performance, achieving an average absolute percent relative error (AAPRE) of approximately 3%, indicating its high reliability for Rs prediction. …”
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AI-Driven Particulate Matter Estimation Using Urban CCTV: A Comparative Analysis Under Various Experimental Conditions by Woochul Choi, Hongki Sung, Kyusoo Chong

“…All four experiments that excluded the nighttime, added the solar incidence angle variable, applied the distribution of PM per class, and removed the outlier removal algorithm showed high predictive power. In particular, the final experiment that satisfied all conditions, including the exclusion of nighttime, addition of solar incidence angle variable, and application of outlier removal algorithm, derived predictive values that are expected to be commercialized.…”
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Demand-Adapting Charging Strategy for Battery-Swapping Stations by Benjamín Pla, Pau Bares, Andre Aronis, Augusto Perin

“…An optimized charging policy is derived using dynamic programming (DP), assuming average battery demand and accounting for both the costs and emissions associated with electricity consumption. The proposed algorithm uses a prediction of the expected traffic in the area as well as the expected cost of electricity on the net. …”
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Estimating Economic Insights: A Machine Learning Method for Estimating the Shanghai Stock Exchange by Reza Seifi Majdar, Seyed Hadi Seyed Hatami

“…This work aims to create an accurate hybrid model for predicting stock prices which includes Adaptive Boosting, Slime mould algorithm, and Empirical mode decomposition (EMD) to forecast the stock market values. …”
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Cyber threat intelligence for smart grids using knowledge graphs, digital twins, and hybrid machine learning in SCADA networks by Nabeel Al-Qirim, Munir Majdalawieh, Anoud Bani-hani, Hussam Al Hamadi

“…This study presents a novel “Digital-twin and Machine Learning-based SCADA Cyber Threat Intelligence (DT-ML-SCADA-CTI)” approach, which utilizes an innovative algorithm to visualize and predict the effects of cyber-attacks, including FDIA, RTCI, and SRA, on SCADA systems. …”
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Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis. by Leonardo V Castorina, Filippo Grazioli, Pierre Machart, Anja Mösch, Federico Errica

“…In this work, we introduce a novel approach for assessing the generalization capabilities of TCR binding predictors: the Distance Split (DS) algorithm. The DS algorithm controls the distance between training and testing peptides based on both sequence and structure, allowing for a more nuanced evaluation of model performance. …”
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A 5G network based conceptual framework for real-time malaria parasite detection from thick and thin blood smear slides using modified YOLOv5 model by Swati Lipsa, Ranjan Kumar Dash, Korhan Cengiz, Nikola Ivković, Adnan Akhunzada

“…Methods The proposed system leverages a deep learning-based YOLOv5x algorithm to detect malaria parasites in thick and thin blood smear samples. …”
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Implementation of Clustering and Association for Early Warning of Disasters in Bojonegoro Regency by Denny Nurdiansyah, Erna Hayati, Ika Purnamasari, Anna Apriana Hidayanti, Yuliana Fuji Rahayu

“…The goal was to enable better disaster prediction and preparedness in the future. The methods applied included mapping, clustering using the K-means algorithm, and association rule mining with the Apriori algorithm. …”
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Reducing Computational and Memory Cost of Substructuring Technique in Finite Element Models by M H. Kadkhodaee, H. Moslemi

“…A combined case is also considered which investigates balanced reduction of computational and memory cost for 2D problems. …”
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Design of Low-Power Structural FIR Filter Using Data-Driven Clock Gating and Multibit Flip-Flops by Lamjed Touil, Abdelaziz Hamdi, Ismail Gassoumi, Abdellatif Mtibaa

“…The combination of these methods into a single algorithm enables further power saving of the FIR filter. …”
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ACCURACY ANALYSIS OF UAV COORDINATES USING EGNOS AND SDCM AUGMENTATION SYSTEMS by Kamil KRASUSKI, Damian WIERZBICKI, Małgorzata KIRSCHENSTEIN, Marta ŻUKOWSKA, Paweł GOŁDA

“…Apart from that, another important result of the application of the proposed algorithm was the reduction of outlier positioning errors that reduced the accuracy of the positioning of UAV when a single SBAS solution (EGNOS or SDCM) was used. …”
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Smart Real-time Attendance System for Nigerian Universities by Mikailu Habila, Faseki N. Francisca, Luwani Ishaya, Muhammed Kabir Ahmed, Umar A. Muhammad, Haruna P. Charles

“…The system employs Convolutional Neural Networks (CNNs) and the ArcFace algorithm for facial feature extraction and identity verification. …”
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The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and Reservoirs by Xiao-zhe Bai, Hui-yan Zhang, Xiao-yi Wang, Li Wang, Ji-ping Xu, Jia-bin Yu

“…In this study, an adaptive-clustering algorithm is introduced to obtain some typical operating intervals. …”
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Persistence and Burn-in in Solar Coronal Magnetic Field Simulations by Eric J. Hall, Karen A. Meyer, Anthony R. Yeates

“…Simulations of solar phenomena play a vital role in space-weather prediction. A critical computational question for automating research workflows in the context of data-driven solar coronal magnetic field simulations is quantifying a simulation's burn-in time, after which a solar quantity has evolved away from an arbitrary initial condition to a physically more realistic state. …”
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LIFO-STACK SIZE DETERMINATION FOR GROWING OF THE IMAGE REGIONS by V. Yu. Tsviatkou

“…Using the proposed expression to calculate the required capacity of the LIFO-stack under conditions of its maximum load in the image segmentation algorithm based on growing regions provides a 2-fold reduction in the number of LIFO-stack memory cells.…”
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Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic by Muhammed Ballı, Asli Ercan Dogan, Sevin Hun Senol, Hale Yapici Eser

“…Mental disorders are closely linked to suicidal ideation, but predicting suicide remains complex due to the multifaceted nature of contributing factors. …”
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A critical assessment of ionic liquid-based aqueous biphasic systems for biomolecules extraction and CO2 absorption by Viggy Wee Gee Tan, Yazan Abuhasheesh, Doris Ying Ying Tang, Yongyu Zhang, Zengling Ma, Lin Chen, Rambabu Krishnamoorthy, Pau Loke Show

“…The incorporation of machine learning (ML) algorithms to predict the toxicity and CO2 capture properties, as well as the scaling up of IL-ABSs, is also explored.…”
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Synchrony Vision: Capturing Body Motion Synchrony Through Phase Difference Using the Kinect by Jinhwan Kwon

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From Tweets to Threats: A Survey of Cybersecurity Threat Detection Challenges, AI-Based Solutions and Potential Opportunities in X by Omar Alsodi, Xujuan Zhou, Raj Gururajan, Anup Shrestha, Eyad Btoush

“…The findings indicate that current studies often lack comprehensive evaluations of critical aspects such as prediction scope, types of cybersecurity threats, feature extraction techniques, algorithm complexity, information summarization levels, scalability over time, and performance measurements. …”
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