Signature of pre-pregnancy microbiome in infertile women undergoing frozen embryo transfer with gestational diabetes mellitus by Wenzheng Guan, Tian Zhou, Jiao Jiao, Liwen Xiao, Zhen Wang, Siyuan Liu, Fujie Yan, Fangqing Zhao, Xiuxia Wang

“…Algorithms on the basis of marker species and biochemical parameters can be used as effective tools for GDM risk evaluation before pregnancy.…”
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Shipyard Manpower Digital Recruitment: A Data-Driven Approach for Norwegian Stakeholders by Bogdan Florian Socoliuc, Andrei Alexandru Suciu, Mădălina Ecaterina Popescu, Doru Alexandru Plesea, Florin Nicolae

“…The application of machine learning algorithms provides predictive insights that support real-time adjustments to job postings, optimizing recruitment strategies. …”
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Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review by Hesameddin Akbarein, Mohammad Hussein Taaghi, Mahyar Mohebbi, Parham Soufizadeh

“…It also examines the primary machine learning algorithms used in relevant studies, highlighting emerging trends in the field. …”
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Finite-element analysis of contact characteristics and friction modes of the "valve-guide" of the internal combustion engine by K.E. Holenko, A.A. Vychavka, M.O. Dykha, V.O. Dytyniuk

“…Modeling the performance of the "valve-guide" engine pair using modern software is an effective tool both for identifying weak points in the design and for predicting the behavior of the friction unit in operation. …”
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An advanced structural health monitoring IoT platform for offshore wind turbine blades by Zhou Xingguo, Tian Yankang, Qin Yi, Charitidis Costas A., Milickovic Tanja K., Termine Stefania

“…This research focuses on developing a comprehensive, real-time monitoring system that utilises advanced sensor networks and edge computing, empowering advanced predictive algorithms to strengthen in-time maintenance of turbine blades, improving operational efficiency and reducing maintenance cost.…”
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Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Dataset from the Festival of Lights in Lyon by Oscar Dufour, Huu-Tu Dang, Jakob Cordes, Raphael Korbmacher, Mohcine Chraibi, Benoit Gaudou, Alexandre Nicolas, Antoine Tordeux

“…Abstract The dynamics of dense crowds have received considerable attention from researchers seeking fundamental understanding or aiming to develop data-driven algorithms to predict pedestrian trajectories. However, current research mainly relies on data collected in controlled settings. …”
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Experimentally validated inverse design of FeNiCrCoCu MPEAs and unlocking key insights with explainable AI by Fangxi Wang, Allana G. Iwanicki, Abhishek T. Sose, Lucas A. Pressley, Tyrel M. McQueen, Sanket A. Deshmukh

“…Abstract A computational workflow integrating a stacked ensemble machine learning (SEML) model and a convolutional neural network (CNN) model with evolutionary algorithms has been developed to identify new compositions of FeNiCrCoCu MPEAs with high bulk modulus and unstable stacking fault energies. …”
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A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides by Xiaomeng Geng, Xiaomeng Geng, Xiaomeng Geng, Huiru Li, Huiru Li, Le Wang, Weidong Sun, Yize Li

“…Additionally, it identifies the limitations and unresolved challenges in current approaches, such as constraints on data resolution, algorithmic biases, and environmental variability. The potential for integrating multi-source remote sensing data with marine environmental parameters and deep learning techniques is discussed, emphasizing their roles in improving the accuracy and reliability of monitoring and predicting Ulva prolifera green tides. …”
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Towards a greener future: The role of sustainable methodologies in metabolomics research by Chiara Spaggiari, Kgalaletso Othibeng, Fidele Tugizimana, Gabriele Rocchetti, Laura Righetti

“…Computational advancements, including AI-driven models, machine learning-based semi-quantification, and predictive algorithms for solvent selection, further enhance sustainability by reducing resource consumption. …”
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Self-organizing maps to evaluate optimal strategies for balancing binary class distributions: a methodological approach by Alberto Nogales, Diego Guadalupe, Álvaro J. García-Tejedor

“…Abstract Since machine learning algorithms rely on data, the way datasets are collected significantly impacts their performance. …”
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Impact Analysis of BIM on Power Substation Project Costs: Techno-Economic Data Evidence from China by Ding Liu, Lizhong Qi, Yi Sun, Jingguo Rong, Su Zhang, Guangze Yu

“…To this end, this study collected total project cost data from 164 power substation projects and techno-economic statements from 34 power substation projects from SGCC to capture data evidence of the impact of BIM on project costs and explore its patterns. Algorithms such as hierarchical clustering based on improved DTW and feature selection based on QDA were designed for data mining. …”
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Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision by Janni Yuval, Paul A. O'Gorman, Chris N. Hill

“…Abstract A promising approach to improve climate‐model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data‐driven. However, neural networks (NNs) often lead to instabilities and climate drift when coupled to an atmospheric model. …”
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Analyzing Digital Political Campaigning Through Machine Learning: An Exploratory Study for the Italian Campaign for European Union Parliament Election in 2024 by Paolo Sernani, Angela Cossiri, Giovanni Di Cosimo, Emanuele Frontoni

“…Our findings highlight the significance of micro-targeting practices, the role of algorithmic biases, and the risks associated with disinformation in shaping public opinion. …”
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Communicating the use of artificial intelligence in agricultural and environmental research by Aaron Lee M. Daigh, Samira H. Daroub, Peter M. Kyveryga, Mark E. Sorrells, Nithya Rajan, James A. Ippolito, Endy Kailer, Christine S. Booth, Umesh Acharya, Deepak Ghimire, Saurav Das, Bijesh Maharjan, Yufeng Ge

“…Clear communication is perhaps more necessary with AI than previous technologies due to its broad and flexible spectrum of uses, the “black‐box” nature of deep‐learning algorithms, and ongoing debates regarding AI's predictive power versus knowledge of first‐principles mechanistic and process‐based theories and models. …”
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Potentialities and development of groundwater resources applying machine learning models in the extended section of Manbhum-Singhbhum Plateau, India by Arijit Ghosh, Biswajit Bera

“…In this study, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) algorithms have been applied. The accuracy of each model has been estimated using the receiver operating characteristics (ROC) curve. …”
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AI-Driven Future Farming: Achieving Climate-Smart and Sustainable Agriculture by Karishma Kumari, Ali Mirzakhani Nafchi, Salman Mirzaee, Ahmed Abdalla

“…Using sophisticated algorithms to predict soil conditions, improving agricultural yield projections, diagnosing water stress from sensor data, and identifying plant diseases and weeds through image recognition, crop mapping, and AI-guided crop selection are some of the main applications investigated. …”
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Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning by Abimbola Efunogbon, Enjie Liu, Renxie Qiu, Taiwo Efunogbon

“…The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. …”
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The Use of BIM Models and Drone Flyover Data in Building Energy Efficiency Analysis by Agata Muchla, Małgorzata Kurcjusz, Maja Sutkowska, Raquel Burgos-Bayo, Eugeniusz Koda, Anna Stefańska

“…The paper examines methodologies for combining thermal imaging with BIM, including image analysis algorithms and artificial intelligence applications. …”
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Embedding With Preservation of Semantics of the Original Data by M. E. Vatkin, D. A. Vorobey, M. V. Yakovlev, M. G. Krivova

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Assessing Similarity Between Datasets Using Vector Representations by А. А. Usatoff, A. M. Nedzved, Guo Jiran

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