Multidimensional Evaluation Framework and Classification Strategy for Low-Carbon Technologies in Office Buildings by Hongjiang Liu, Yuan Song, Yawei Du, Tao Feng, Zhihou Yang

“…The method includes four core components: (1) establishing three archetypal models—low-rise (H ≤ 24 m), mid-rise (24 m < H ≤ 50 m), and high-rise (50 m < H ≤ 100 m) office buildings—based on 99 office buildings in Beijing; (2) classifying 19 key technologies into three clusters—Envelope Structure Optimization, Equipment Efficiency Enhancement, and Renewable Energy Utilization—using bibliometric analysis and policy norm screening; (3) developing a four-dimensional evaluation framework encompassing Carbon Reduction Degree (CRD), Economic Viability Degree (EVD), Technical Applicability Degree (TAD), and Carbon Intensity Degree (CID); and (4) conducting a comprehensive quantitative evaluation using the AHP-entropy-TOPSIS algorithm. …”
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RATIONALE FOR PREVENTION OF CARDIO-CEREBRAL COMPLICATIONS IN THE METABOLIC SYNDROME BASED ON MATHEMATICAL FORECASTING TECHNIQUES by S. V. Chernavskii, A. N. Fursov, N. P. Potekhin, V. N. Yakovlev

“…The diagnostic and treatment algorithms developed based on the stratification of cardiovascular risk index, allow us to give sound recommendations for targeted prevention of cardio-cerebral complications.…”
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MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models by Devlin C. Moyer, Justin Reimertz, Daniel Segrè, Juan I. Fuxman Bass

“…Abstract Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. …”
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Machine learning approaches for modelling of molecular polarizability in gold nanoclusters by Abhishek Ojha, Satya S. Bulusu, Arup Banerjee

“…Our results demonstrate the efficacy of machine-learning in accurately predicting the polarizability of gold nanoclusters. …”
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Laser-Induced Breakdown Spectroscopy vs. Fluorescence Spectroscopy for Olive Oil Authentication by Marios Bekogianni, Theodoros Stamatoukos, Eleni Nanou, Stelios Couris

“…The acquired spectroscopic data were analyzed by several machine learning algorithms, and the constructed predictive models are evaluated thoroughly for their reliability and robustness. …”
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Deep Ocean Learning of Small Scale Turbulence by Ali Mashayek, Nick Reynard, Fangming Zhai, Kaushik Srinivasan, Adam Jelley, Alberto Naveira Garabato, Colm‐cille P. Caulfield

“…Here, we show that supervised machine learning algorithms can be trained on the existing turbulence data to develop skillful predictions of the key properties of turbulence from T, S, Z, and topographic data. …”
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L-Shaped-Sensor-Array-Based Localization and Tracking Method for 3D Maneuvering Target by Xing Zhang, Xue Wang

“…Thirdly an autoregressive (AR) particle filter (PF) algorithm is realized to predict the locations in the next moment. …”
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Modeling flood zones on the basis of time series forecasting and GIS-technologies by the example of the Republic of Bashkortostan by E.V. Palchevsky, V.V. Antonov, L.E. Rodionova, L.A. Kromina, A.R. Fakhrullina

“…At the same time, the possibility of early modeling and visualization of river floods is realized only on the basis of the obtained predictive values of the water level. This will enable specialized organizations and services, as well as management bodies to make decisions related to flood control measures in advance and as soon as possible.…”
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Error-Mask-Adaptive Dynamic Filtering for Image Inpainting by Keunsoo Ko, Seunggyun Woo, Chang-Su Kim

“…Experimental results on diverse datasets show that the proposed EMDF algorithm outperforms existing inpainting algorithms significantly. …”
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Forecasting COVID-19 cases based on mobility by Mehmet Şahin

“…In this context, various machine learning algorithms are implemented to obtain accurate predictions. …”
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ARTIFICIAL INTELLIGENCE IN UNIVERSITY MARKETING: SOME REALITIES AND POSSIBLE APPLICATIONS by Eudochia JOMIR, Grigore BELOSTECINIC

“…In the present research, it is proposed to use the EyeQuant platform, based on predictive artificial intelligence and algorithms able to simulate the reactions of real users to various visual elements of university websites. …”
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A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights by Xie L, Gao M, Tan S, Zhou Y, Liu J, Wang L, Li X

“…Subsequently, based on 482 prognostic moDEGs, we developed and validated an optimal model, termed the Monocyte-related Gene Prognostic Signature (MGPS), by integrating 101 predictive models generated from 10 machine learning algorithms across multiple transcriptome sequencing datasets. …”
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Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks by Yuan Wang, Yuan Wang, Yutong Zhang, Yongxin Li, Tianyu She, Meiqing He, Hailing He, Dong Zhang, Dong Zhang, Jue Jiang

“…DeLong’s test was employed to compare the differences between the groups.ResultsAmong the five models, the one based on the ResNet18 algorithm demonstrated the highest performance. It exhibited statistically significant advantages in predictive accuracy (p &lt; 0.05) compared to the models based on ResNet152, ResNet101, ResNet50, and ResNet34 algorithms. …”
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A method for measuring carbon emissions from power plants using a CNN-LSTM-Attention model with Bayesian optimization by Jiacheng Chen, Li Zheng, Wenyan Che, Li Liu, Hui Huang, Jun Liu, Chang Xing, Penghua Qiu

“…The results indicate that the proposed model achieved the highest performance across every evaluation index for predicting CO2 emissions from coalfired power plants, and the prediction accuracy of the CO2 emission in the future months is also guaranteed.…”
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Stochastic modeling of structural fatigue damage in High Strength Steel structures by Jiri Brozovsky, Martin Krejsa, Petr Lehner, Premysl Parenica, Stanislav Seitl

“…The proposed parallel Direct Optimized Probability Computation method is developed and studied on the problem of fatigue damage prediction. Description of the parallel algorithm is provided, and the functionality of the method is shown in an example case. …”
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INFLUENCE OF MICROBIAL WOUND DISSEMINATION AND MICROCIRCULATION ON THE RESULTS OF SKIN ENGRAFTMENT by Yu. V. Yurova, I. V. Shlyk

“…The obtained data can allow the development of an algorithm of treatment and the preparation of the patients to surgery, determination of the terms of operation, the development the strategy of postoperative management of the patients, which can reduce unfavorable results of operations.…”
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Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality by Mu Yue, Jingxin Xi

“…Instead of adopting the traditional regularization approaches, we offer a novel multi-step sparse boosting algorithm to conduct model-based prediction and variable selection. …”
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Echo state networks for modeling turbulent convection by Mohammad Sharifi Ghazijahani, Christian Cierpka

“…Meanwhile, Echo State Networks (ESN) are among the most fundamental machine learning algorithms suited for modeling sequential data. The current study conducts reduced order modeling of experimental RBC. …”
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Post-Pandemic Urban Future: Identification of the main trends of change in Latin America, through the application of the Delphi method by Yeimis Milton Palomino Pichihua

“…Additionally, it employs machine learning algorithms to transform them into predictions. The sample is made up of 26 panelists from different Latin American countries, who participated in successive questionnaires until stable results were reached. …”
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Adaptive Ensemble Framework With Synthetic Sampling for Tackling Class Imbalance Problem by R. Sasirekha, B. Kanisha

“…ABSTRACT Class imbalance is a critical challenge in heart disease prediction datasets, often leading to biased models with poor performance on the minority class. …”
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