Hyperdimensional computing in biomedical sciences: a brief review by Fabio Cumbo, Davide Chicco

“…Hyperdimensional computing (HDC, also known as vector-symbolic architectures—VSA) is an emerging computational paradigm that relies on dealing with vectors in a high-dimensional space to represent and combine every kind of information. …”
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Incorporation of Histogram Intersection and Semantic Information into Non-Negative Local Laplacian Sparse Coding for Image Classification by Ying Shi, Yuan Wan, Xinjian Wang, Huanhuan Li

“…Finally, a multi-class linear Support Vector Machine (SVM) is employed for image classification. …”
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A Method for Landslide Deformation Detection Based on Projection Surface Element Matching of 3D Models by Mengxi Sun, Hui Cao, Yansong Duan

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Microbiome of <i>Hyalomma dromedarii</i> (Ixodida: Ixodidae) Ticks: Variation in Community Structure with Regard to Sex and Host Habitat by Nighat Perveen, Alejandro Cabezas-Cruz, Daniil Iliashevich, Lianet Abuin-Denis, Olivier Andre Sparagano, Arve Lee Willingham

“…A total of 40 ticks (male (15), female (15), and nymph (10)) were selected from tick samples collected from camels and processed for genomic DNA and next-generation sequencing using the Illumina MiSeq platform. …”
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The β-triketone, nitisinone, kills insecticide-resistant mosquitoes through cuticular uptake by Zachary Thomas Stavrou-Dowd, George Parsons, Clair Rose, Faye Brown, Rosemary Susan Lees, Álvaro Acosta-Serrano, Lee Rafuse Haines

“…The compound demonstrated consistent efficacy across all three mosquito species tested, indicating broad-spectrum activity against major disease vectors. Conclusions This study demonstrates that nitisinone exhibits a novel mode of action distinct from current Insecticide Resistance Action Committee (IRAC) classifications by specifically targeting blood digestion processes. …”
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Advanced signal processing algorithm for fault classification and localization in VSC-HVDC based Offshore wind farm by Rehana Perveen

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Document Relevance Filtering by Natural Language Processing and Machine Learning: A Multidisciplinary Case Study of Patents by Raj Bridgelall

“…These models include extreme gradient boosting, random forest, and support vector machines; a deep artificial neural network; and three natural language processing methods: latent Dirichlet allocation, non-negative matrix factorization, and k-means clustering of a manifold-learned reduced feature dimension. …”
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The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning. by Bo Yu, Zheng Wang, Shangke Liu, Xiaomin Liu, Ruixin Gou

“…To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this study introduces machine learning methods to explore the detection of FDIAs. …”
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Information-Theoretic Sequential Framework to Elicit Dynamic High-Order Interactions in High-Dimensional Network Processes by Helder Pinto, Yuri Antonacci, Gorana Mijatovic, Laura Sparacino, Sebastiano Stramaglia, Luca Faes, Ana Paula Rocha

“…The framework is validated through simulations of networks composed of cascade, common drive, and common target mechanisms, modelled using vector autoregressive (VAR) processes. The feasibility of the proposed approach is demonstrated through its application in climatology, specifically by analyzing the relationships between climate variables that govern El Niño and the Southern Oscillation (ENSO) using historical climate data.…”
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Modeling of digital spatial processing under conditions of troposphere propagation of centimeter radio waves for wireless telecommunication by Ilia W. Peshkov, Dmitry N. Borisov

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Bayesian-Optimized Multi-Task Gaussian Process Regression With Composite Kernels for Soybean Oil Futures Forecasting by Hui-Dong Yin, Yi-Yang Li

“…This study addresses the forecasting of soybean oil prices in China&#x2019;s wholesale markets by developing a Bayesian-optimized Multi-Task Gaussian Process Regression (MTGPR) framework. Traditional econometric models and machine learning approaches often struggle with non-stationary data and uncertainty quantification, while existing Gaussian process applications in agricultural markets remain underexplored. …”
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Integration of Gaussian process regression and K means clustering for enhanced short term rainfall runoff modeling by Ozgur Kisi, Salim Heddam, Kulwinder Singh Parmar, Andrea Petroselli, Christoph Külls, Mohammad Zounemat-Kermani

“…This study introduces a hybrid Gaussian process regression (GPR) model integrated with K-means clustering (GPR-K-means) for short-term rainfall-runoff forecasting. …”
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Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning by Bo Wang, Jun Wei, Le Zhang, Hui Jiang, Cheng Jin, Shaowen Huang

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EXPERIMENTAL STUDY OF LOCAL HYDRODYNAMICS AND MASS EXCHANGE PROCESSES OF COOLANT IN FUEL ASSEMBLIES OF PRESSURIZED WATER REACTORS by S. M. Dmitriev, A. A. Barinov, A. N. Pronin, V. D. Sorokin, A. E. Khrobostov

“…In order to measure local hydrodynamic characteristics of coolant flow five-channel Pitot probes were used that enable to measure the velocity vector in a point by its three components. The tracerpropane method was used for studying mass transfer processes. …”
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Gaussian Process Regression Total Nitrogen Prediction Based on Data Decomposition Technology and Several Intelligent Algorithms by WANG Yongshun, CUI Dongwen

“…Total nitrogen (TN) is one of the important indicators to reflect the degree of water pollution and measure the eutrophication status of lakes and reservoirs.To improve the accuracy of TN prediction,based on the empirical wavelet transform (EWT) and wavelet packet transform (WPT) decomposition technology,this paper proposes a Gaussian process regression (GPR) prediction model optimized by osprey optimization algorithm (OOA),rime optimization algorithm (ROA),bald eagle search (BES) and black widow optimization algorithm (BWOA) respectively.Firstly,the TN time series is decomposed into several more regular subsequence components by EWT and WPT respectively.Then,the paper briefly introduces the principles of OOA,ROA,BES,and BWOA algorithms and applies OOA,ROA,BES,and BWOA to optimize GPR hyperparameters.Finally,EWT-OOA-GPR,EWT-ROA-GPR,EWT-BES-GPR,EWT-BWOA-GPR,WPT-OOA-GPR,WPT-ROA-GPR,WPT-BES-GPR,WPT-BWOA-GPR models (EWT-OOA-GPR and other eight models for short) are established to predict the components of TN by the optimized super-parameters.The final prediction results are obtained after reconstruction,and WT-OOA-GPR,WT-ROA-GPR,WT-BES-GPR and WT-BWOA-GPR models based on wavelet transform (WT) are built.Eight models,including EWT-OOA-SVM based on support vector machine (SVM),the paper compares the unoptimized EWT-GPR,WPT-GPR models,and the uncomposed OOA-GPR,ROA-GPR,BES-GPR,and BWOA-GPR models.The models were verified by the monitoring TN concentration time series data of Mudihe Reservoir,an important drinking water source in China,from 2008 to 2022.The results are as follows.① The average absolute percentage error of eight models such as EWT-OOA-GPR for TN prediction is between 0.161% and 0.219%,and the coefficient of determination is 0.999 9,which is superior to other comparison models,with higher prediction accuracy and better generalization ability.② EWT takes into account the advantages of WT and EMD.WPT can decompose low-frequency and high-frequency signals at the same time.Both of them can decompose TN time series data into more regular modal components,significantly improving the accuracy of model prediction,and the decomposition effect is better than that of the WT method.③ OOA,ROA,BES,and BWOA can effectively optimize GPR hyperparameters and improve GPR prediction performance.…”
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Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images by Dominic Chee Yong Ong, Iksan Bukhori, Kok Swee Sim, Kok Beng Gan

“…It is shown that LSR method to perform better than the rest. Then, Support Vector Machines (SVM) and Gaussian Process Regression (GPR) are tested by pairing it with LSR. …”
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Size-exclusion chromatography as a multi-attribute method for process and product characterization of adeno-associated virus by Sri Hari Raju Mulagapati, Arun Parupudi, Tomasz Witkos, Nick Bond, Xiaoyu Chen, Thomas Linke, Guoling Xi, Albert Ethan Schmelzer, Wei Xu

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A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization by Dongjie Pang, Cristina Moliner, Tao Wang, Jin Sun, Xinyan Zhang, Yingping Pang, Xiqiang Zhao, Zhanlong Song, Ziliang Wang, Yanpeng Mao, Wenlong Wang

“…This review examines the deployment of AI-optimized control algorithms in processes including pyrolysis, incineration, and gasification. …”
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A deep q-networks model for optimising decision-making process in the context of energy transition modelling by Ana Tănăsescu, Cristian Bucur, Jean Vasile Andrei, Bogdan-George Tudorică, Dorel Mihai Paraschiv, Dorel Dușmănescu

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Some comments on the assessment of students’ knowledge at the University by Darya I. Yareshchenko

“…This article is devoted to the study and modeling of the process of acquiring knowledge by students at the University. …”
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