Perspectives of Second-Order Blind Identification for Operational Modal Analysis of Civil Structures by C. Rainieri

“…These represent key issues in view of the automation of the algorithm and its integration within vibration-based monitoring systems. …”
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An Intelligent Hybrid Framework for Threat Pre-Identification and Secure Key Distribution in Zigbee-Enabled IoT Networks Using RBF and Blockchain by Bhukya Padma, Mahipal Bukya, Ujjwal Ujjwal

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Geographical Peer Matching for P2P Energy Sharing by Romaric Duvignau, Vincenzo Gulisano, Marina Papatriantafilou, Ralf Klasing

“…Significant cost reductions attract ever more households to invest in small-scale renewable electricity generation and storage. …”
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Complex Therapy for Intraoperative Blood Loss during Pelvic Bone Repair by Z. G. Marutyan, Yu. V Nikiforov, A. B. Kazantsev, A. A. Ter-Grigoryan

“…Objective: to introduce blood saving technologies and to develop algorithms for management of patients with pelvic fractures. …”
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Student initiative in international projects on preventive cardiology. by T. A. Khomazyuk, V. Yu. Krotova, Н. A. Kosova, M. G. Kirichko

“…Professional socialization is the ultimate link in the professional way of the individual. The algorithm of professional socialization of students in higher education consists of the following stages: professional orientation → professional development → professional adaptation → professional becoming → professional socialization. …”
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A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition by Sinian Hu, Han Xiao, Cancan Yi

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The impact of presentation modes on mental rotation processing: a comparative analysis of eye movements and performance by Philipp Stark, Efe Bozkir, Weronika Sójka, Markus Huff, Enkelejda Kasneci, Richard Göllner

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Accurate Virtual Trial Assembly Method of Prefabricated Steel Components Using Terrestrial Laser Scanning by Yin Zhou, Daguang Han, Kaixin Hu, Guocheng Qin, Zhongfu Xiang, Chunli Ying, Lidu Zhao, Xingyi Hu

“…Experimental results show that the geometric prediction deviation of VTA is less than 1/1800 of the experimental bridge span, and the mean stress predicted via VTA is 90% of the measured mean stress. …”
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StratLearn-z: Improved photo-$z$ estimation from spectroscopic data subject to selection effects by Chiara Moretti, Maximilian Autenrieth, Riccardo Serra, Roberto Trotta, David A. van Dyk, Andrei Mesinger

“…We benchmark our results against the GPz algorithm, quantifying the performance of the two algorithms with a set of metrics. …”
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Integration of single‐cell and bulk RNA‐sequencing data reveals the prognostic potential of epithelial gene markers for prostate cancer by Zhuofan Mou, Lorna W. Harries

“…Current clinicopathological factors inadequately predict biochemical recurrence, a critical indicator guiding post‐treatment strategies following radical prostatectomy. …”
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ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool by D. Di Santo, C. He, F. Chen, L. Giovannini

“…These regression algorithms are used to construct computationally inexpensive surrogate models to effectively predict the impact of input parameter variations on model output, thereby significantly reducing the computational burden of running high-fidelity models for sensitivity analysis. …”
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Optimization of clustering parameters for single-cell RNA analysis using intrinsic goodness metrics by Nicolina Sciaraffa, Antonino Gagliano, Luigi Augugliaro, Claudia Coronnello, Claudia Coronnello

“…This procedure has enabled the effective prediction of clustering accuracy through the utilization of intrinsic metrics. …”
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Application of artificial neural networks in the drilling processes: Can equivalent circulation density be estimated prior to drilling? by Husam H. Alkinani, Abo Taleb T. Al-Hameedi, Shari Dunn-Norman, David Lian

“…The goal of this work was to predict ECD prior to drilling by using artificial neural network (ANN). …”
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Structural and population-based evaluations of TBC1D1 p.Arg125Trp. by Tom G Richardson, Elaine C Thomas, Richard B Sessions, Debbie A Lawlor, Jeremy M Tavaré, Ian N M Day

“…We investigated these findings in the Avon Longitudinal Study of Parents and Children (ALSPAC), a large European birth cohort of mothers and offspring, and by generating a predicted model of the structure of this domain. Structural prediction involved the use of three separate algorithms; Robetta, HHpred/MODELLER and I-TASSER. …”
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A Comparative Study of Data-Driven Prognostic Approaches under Training Data Deficiency by Jinwoo Song, Seong Hee Cho, Seokgoo Kim, Jongwhoa Na, Joo-Ho Choi

“…Data Augmentation Prognostics (DAPROG) also exhibits lower variance in its predictions, suggesting a more consistent performance. …”
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Estimating canopy height in tropical forests: Integrating airborne LiDAR and multi-spectral optical data with machine learning by Brianna J. Pickstone, Hugh A. Graham, Andrew M. Cunliffe

“…This study aims to compare the performance of three machine learning algorithms (Multiple Linear Regression (MLR), Random Forest (RF), and Convolutional Neural Networks (CNN)) when using PlanetScope and Sentinel-2 imagery to improve the accuracy of height predictions. …”
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Short-Term Electricity Price Forecasting Using the Empirical Mode Decomposed Hilbert-LSTM and Wavelet-LSTM Models by Kunal Shejul, R. Harikrishnan, Amit Kukker

“…The proposed techniques show better performance in terms of rank correlation, mean square error, and root mean square error compared to the existing algorithms of LSTM and CNN-LSTM. The prediction results achieved with wavelet-LSTM and Hilbert-LSTM (1-month dataset of 8 years) are rank correlation 0.9746 and 0.9749, MSE 0.2962 and 0.1363, and RMSE 0.5443 and 0.3692, respectively. …”
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Implementation of a neural network model in the Statistica 12 for mudflow frequency forecasting by B. A. Ashabokov, A. A. Tashilova, L. A. Kesheva, N. V. Teunova

“…It follows from the linear trend equation that, on average, over the entire period, including the predicted one, the number of mudflows tends to grow slightly by 0.3/10 years. …”
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Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan. by Ana Torres, Brima Musa Younis, Samuel Tesema, Jose Carlos Solana, Javier Moreno, Antonio J Martín-Galiano, Ahmed Mudawi Musa, Fabiana Alves, Eugenia Carrillo

“…Today, basic knowledge of this neglected disease and how to predict its progression remain largely unknown.<h4>Methods and findings</h4>This study addresses the use of several biochemical, haematological and immunological variables, independently or through unsupervised machine learning (ML), to predict PKDL progression risk. …”
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Leveraging machine learning techniques to analyze nutritional content in processed foods by K. A. Muthukumar, Soumya Gupta, Doli Saikia

“…The findings reveal that the SVR model is particularly effective in predicting nutrient retention, outperforming the RF model. …”
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