Artificial intelligence and anti-cancer drugs' response by Xinrui Long, Kai Sun, Sicen Lai, Yuancheng Liu, Juan Su, Wangqing Chen, Ruhan Liu, Xiaoyu He, Shuang Zhao, Kai Huang

“…This review aims to comprehensively summarize the research progress in utilizing AI algorithms to analyze multi-omics data including genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, and histopathology, for predicting cancer drug resistance. …”
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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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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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Is visual inspection with acetic acid (VIA) a useful method of finding pre-invasive cervical cancer? by Maliheh Arab, Atefeh Moridi, Ghazaleh Fazli, Robabeh Ghodssi-Ghasemabadi, Maryam Maktabi, Samaneh Saraeian, Mahdie Sanati

“…The sensitivity, specificity and negative predictive value (NPV) of the VIA were 41.9%, 73.3%, and 98.2%, respectively. …”
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A Distributed Energy Optimized Routing Using Virtual Potential Field in Wireless Sensor Networks by Haifeng Jiang, Yanjing Sun, Renke Sun, Wei Chen, Shanshan Ma, Jing Gao

“…Simulation results show that the algorithm effectively extends the network lifetime, has good performance on energy balance of sensors, and decreases the average hop of data transmission with the increase of the number of sinks, compared with similar algorithms.…”
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Tracking Sumatran Tiger (Panthera tigris sumatrae Pocock, 1929) distribution in Gunung Leuser National Park: The influence of prey presence and environmental variables on habitat s... by M Hadi Saputra, Salmah Widyastuti, Wanda Kuswanda, R. Taufiq Purna Nugraha, Hendra Helmanto, Nunu Anugrah, U Mamat Rahmat, Rudianto Saragih Napitu, Andrinaldi Adnan, Iskandarrudin, Agus Purwoko, Pindi Patana

“…The algorithms used are maximum entropy and random forest. …”
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3D Volumetric Strain Distribution of the Cerro Prieto Geothermal Field Inferred From Inverse Modeling of InSAR and Leveling Data by Luis A. Gallardo, Olga Sarychikhina, Ewa Glowacka, Braulio Robles

“…We present and release a conjugate‐gradient inversion code that searches for the three‐dimensional distribution of the volumetric strain that predicts simultaneously any observed InSAR, leveling and GPS surface displacement. …”
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Computational selection of transcriptomics experiments improves Guilt-by-Association analyses. by Prajwal Bhat, Haixuan Yang, László Bögre, Alessandra Devoto, Alberto Paccanaro

“…We demonstrate that: using the selected experiments there is a statistically significant improvement in correlation between genes in the functional category of interest; the selected experiments improve GBA-based gene function prediction; the effectiveness of the selected experiments increases with annotation specificity; our algorithm can be successfully applied to GBA-based pathway reconstruction. …”
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Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review by Maria Gerakari, Anastasios Katsileros, Konstantina Kleftogianni, Eleni Tani, Penelope J. Bebeli, Vasileios Papasotiropoulos

“…AI has emerged as a powerful solution to overcome the limitations of traditional breeding techniques, which often involve time-consuming, resource-intensive processes with limited predictive accuracy. Through advanced algorithms and predictive models, ML and DL facilitate the identification and optimization of key traits, including higher yield, improved quality, pest resistance, and tolerance to extreme climatic conditions. …”
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Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique by Daniyal Alghazzawi, Hayat Ullah, Naila Tabassum, Sahar K. Badri, Muhammad Zubair Asghar

“…Our methodology, along with an updated ensemble method, bridges the gap between Explainable AI and leverages a variety of machine learning algorithms to improve predictive accuracy. By leveraging Explainable AI’s interpretability to analyze the features, the model elucidates the reasoning behind its classifications leading to a comprehension of hidden patterns associated with suicidal ideations. …”
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Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol by Qunling Luo, Zhiqiang Guo, Danping Lin, Boxue Chang, Yinlan Ruan

“…The modeling performances of three algorithms including Support Vector Machine Regression (SVMR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR) were systematically evaluated, with PLSR identified as the optimal algorithm. …”
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Large‐scale identification of genetic design strategies using local search by Desmond S Lun, Graham Rockwell, Nicholas J Guido, Michael Baym, Jonathan A Kelner, Bonnie Berger, James E Galagan, George M Church

“…Methods based on flux–balance analysis (FBA) and bi‐level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. …”
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Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models by Hugh Dorward, David M. Knowles, Eralp Demir, Mahmoud Mostafavi, Matthew J. Peel

“…Crystal plasticity models are powerful tools for predicting the deformation behaviour of polycrystalline materials accounting for underlying grain morphology and texture. …”
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Finite Element Model Updating in Bridge Structures Using Kriging Model and Latin Hypercube Sampling Method by Jie Wu, Quansheng Yan, Shiping Huang, Chao Zou, Jintu Zhong, Weifeng Wang

“…Compared to the predicted results obtained by using a genetic algorithm, the proposed method can reduce the computational time without losing the accuracy.…”
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P. I. E. N. O.–Petrol-Filling Itinerary Estimation aNd Optimization by Marco Savarese, Antonio de Blasi, Carmine Zaccagnino, Carlo Augusto Grazia

“…Different domains are stressed to reach the goal: microcontroller and OEM to retrieve the fuel level from the car, national authorities to retrieve the daily fuel price, AI models to predict the price trend for the next days, and algorithms to compute the best fuel station and the best time to fill. …”
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Variable Speed Limit Strategies Based on the Macro Hierarchical Control Traffic Flow Model by Shubin Li, Tao Wang, Hualing Ren, Baiying Shi, Xiangke Kong, Jianyong Chai, Xuejuan Wang

“…A solution algorithm is further developed to find the optimal parameters of VSL by minimizing the total travel time and delay. …”
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Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women by Mariana Carolyn Cruz-Mendoza, Roberto Angel Melendez-Armenta, Juana Canul-Reich, Julio Muñoz-Benítez

“…The methodology integrates Random Forest (RF) and Gradient Boosting Classifier (GBC) algorithms to classify IPV cases by leveraging historical data for predictive analysis. …”
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Machine learning applied to the design and optimization of polymeric materials: A review by Sudarsan M. Pai, Karim A. Shah, Sruthi Sunder, Rodrigo Q. Albuquerque, Christian Brütting, Holger Ruckdäschel

“…Machine learning (ML) offers a promising solution to this issue. By leveraging ML algorithms, researchers can accelerate the design process, predict material properties more rapidly, and optimize formulations. …”
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Comprehensive Fault Diagnosis of Three-Phase Induction Motors Using Synchronized Multi-Sensor Data Collection by Kevin Thomas, Ahasanur Rahman, Wesam Rohouma, Md. Faysal Ahamed, Fariya Bintay Shafi, Md. Nahiduzzaman, Amith Khandakar

“…The dataset, organized into ten distinct CSV files covering various operational states, provides a rich resource for developing and testing fault detection algorithms. A Random Forest classifier trained on this dataset achieved an accuracy of 99.82%, demonstrating its suitability for real-time fault diagnosis and predictive maintenance applications. …”
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Microgrid Resilience Enhancement with Sensor Network-Based Monitoring and Risk Assessment Involving Uncertain Data by Tangxiao Yuan, Kossigan Roland Assilevi, Kondo Hloindo Adjallah, Ayité Sénah A. Ajavon, Huifen Wang

“…Both decision processes skillfully utilize Monte Carlo simulation and multi-objective genetic algorithms to effectively manage the uncertainty risks in the decision-making process, thereby significantly enhancing the overall resilience of the microgrid.…”
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