High-performance slow-curing polyurea compositions by S. V. Romanov, O. A. Botvinova, E. A. Timakov, D. A. Rashchupkina, Yu. T. Panov

“…For this reason, mathematical modeling was used to optimize experimental design. …”
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Hydrogen–electric–thermal coupling analysis and validation of superconducting turbo-electric hybrid propulsion system by Mingliang Bai, Wenjiang Yang, Ruopu Zhang, Zibing Qu, Juzhuang Yan

“…This study focuses on the design of a liquid hydrogen-cooled superconducting TEHPS, incorporating detailed models for key components, including the hydrogen turbine engine, fuel cell, and superconducting machines. …”
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A Dual-Variable Selection Framework for Enhancing Forest Aboveground Biomass Estimation via Multi-Source Remote Sensing by Dapeng Chen, Hongbin Luo, Zhi Liu, Jie Pan, Yong Wu, Er Wang, Chi Lu, Lei Wang, Weibin Wang, Guanglong Ou

“…<i>langbianensis</i> forests, while GA-optimized machine learning models demonstrate excellent performance, providing strong support for regional-scale forest resource monitoring and carbon stock assessment.…”
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In Silico Perspectives on Triple Negative Breast Cancer: Challenges and Progress by Maham Taqi, Arleen Yousuf, Sidra Shaikh, Faheeda Soomro, Abdul Sattar Qureshi, Kannan RR Rengasamy

“…In silico methods, including molecular docking, machine learning, systems biology, and multi-omics approaches, are shown to enhance drug discovery, biomarker identification, and predictive modeling. …”
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Air Traffic Trends and UAV Safety: Leveraging Automatic Dependent Surveillance–Broadcast Data for Predictive Risk Mitigation by Prasad Pothana, Paul Snyder, Sreejith Vidhyadharan, Michael Ullrich, Jack Thornby

“…To enhance the safe and efficient integration of UAVs into airport environments, this paper presents an analysis of temporal statistical patterns in flight traffic, the predictive modeling of future traffic trends using machine learning, and the identification of optimal time windows for UAV operations within airports. …”
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Convolutional transform learning based fusion framework for scale invariant long term target detection and tracking in unmanned aerial vehicles by Fatma S. Alrayes, Nazir Ahmad, Asma Alshuhail, Menwa Alshammeri, Ali Alqazzaz, Hassan Alkhiri, Jehad Saad Alqurni, Yahia Said

“…Drone-equipped cameras monitor objects at changing altitudes, leading to essential scale variants. The model increases targeted accuracy and decreases false positives using real-time data and machine learning (ML) methods. …”
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Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review by Ping Zhang, Qing Zhang, Xinya Hu, Wei Chi, Weihua Yang

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Implementation of Explainable Ai in Deep Learning Methods for Multiclass Classification of Plant Diseases in Mango Leaves by Menaka Radhakrishnan

“… Maintaining optimal yield plays a crucial role in the prosperity of agriculture and in turn the economy of the country. …”
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Progress on additive manufacturing technology of solid propellants by Christian Ingabire, Dao-lun Liang, Li-xiang Li

“…DIW faces challenges in managing increased viscosity at high solid and energetic content, leading to manufacturing difficulties and the need for binder system optimization. SLA struggles with maintaining resin transparency, balancing mechanical strength with other properties, optimizing curing parameters, and improving the bonding between matrix and solid particles.Future research is expected to focus on developing thermoplastic binders for FDM, exploring energetic copolymer binders and advanced rheological models for DIW, and creating high-energy photopolymer resins while optimizing the SLA process. …”
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Prediction of formation pressure in underground gas storage based on data-driven method by SUI Gulei, FU Yujiang, ZHU Hongxiang, LI Zunzhao, WANG Xiaolin

“…The supervised learning model of formation pressure forecasting is established by three kinds of machine learning algorithms including extreme gradient boosting (XGBoost), support vector regression (SVR), and long short-term memory network (LSTM). …”
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Modelo matemático y algoritmo de solución de un flujo con tiempos dependientes de la secuencia // A mathematical model and solution algorithm to a flow shop task with times dependi... by A. Fiol-Zulueta, J. Arzola-Ruíz, A. da Silva-Carvalho

“…In the work a mathematical model, for the<br />case of a hybrid flow shop with times depending of the sequence prosecution and of the machines<br />characteristics, and it implementation using a metaheurístic procedure are proposed, with the<br />purpose of aiding the decisions making process with a view of improving the tools used to achieve<br />the work sequences conciliation of the production shops, due to the necessity of raising the<br />efficiency indicators of these workshops.…”
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FedACT: An adaptive chained training approach for federated learning in computing power networks by Min Wei, Qianying Zhao, Bo Lei, Yizhuo Cai, Yushun Zhang, Xing Zhang, Wenbo Wang

“…Federated Learning (FL) is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security. …”
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Using the LTO Network Level 1 Blockchain to Automate Inter-Organizational Business Processes by Khrypko Serhii L., Shcherbakov Serhii S.

“…Live contracts do not contain values directly, but describe the interaction between the parties, unlike the cryptographic "boxes" of smart contracts. Modeling live contracts as finite state machines allows visualizing them in the form of a flowchart. …”
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Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor by Azam Isam Aladwani, Tarik Adnan Almohamad, Abdullah Talha Sözer, İsmail Rakıp Karaş

“…The results also highlight a trade-off, where the tree model provides sub-optimal predictive performance while significantly reducing computational overhead, making it an attractive choice for low-power and latency-sensitive wireless systems.…”
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Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies by Md. Kabin Hasan Kanchon, Mahir Sadman, Kaniz Fatema Nabila, Ramisa Tarannum, Riasat Khan

“…Furthermore, decision tree, random forest, support vector machine (SVM), logistic regression, XGBoost, blending ensemble, and convolutional neural network (CNN) algorithms with corresponding optimized hyperparameters and synthetic minority oversampling technique (SMOTE) have been applied for learning behavior classification. …”
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Advancing thyroid diagnosis: integrating AI-driven CAD framework with numerical data and ultrasound images by Saleh Ateeq Almutairi

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Intelligent recognition of “geological-engineering” sweet spots in tight sandstone reservoirs - an application to a tight gas reservoir in Ordos Basin, China by Kui Chen, Minghao Zhao, Minghao Zhao, Yansong Feng, Xiaoyan Fu, Yifei Wang, Hui Guo, Jingchen Ding, Qi Chen, Qi Chen

“…This study, based on an integrated geological-engineering perspective and utilizing data analysis and multiple machine learning methods, innovatively proposes a regression prediction model that integrates the Triangulation Topology Aggregation Optimizer (TTAO) algorithm, Random Forest (RF), and Multi-Head Self-Attention Mechanism (MSA), aiming to enhance the accuracy of oil and gas sweet spot identification. …”
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Heuristic Binary Search for Modulated Predictive Control by Rafael FIgueiredo, Igor Oliani, Angelo Lunardi, Alfeu J. Sguarezi Filho

“…Predictive Control optimization often relies on exhaustive enumeration of voltage vectors within the finite control set. …”
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Research on power data security full-link monitoring technology based on alternative evolutionary graph neural architecture search and multimodal data fusion by Zhenwan Zou, Bin Wang, Tao Chen, Jia Chen

“…To solve this problem, this paper proposes a hybrid method that combines multimodal data-aware attacks with Light Gradient Boosting Machine (LightGBM) and Support Vector Regression (SVR) agent models. …”
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Boosting skin cancer diagnosis accuracy with ensemble approach by Priya Natha, Sivarama Prasad Tera, Ravikumar Chinthaginjala, Safia Obaidur Rab, C. Venkata Narasimhulu, Tae Hoon Kim

“…We introduce a new method, the Max Voting method, for optimization of skin cancer classification. On the HAM10000 and ISIC 2018 datasets, we trained and assessed three distinct ML models: Random Forest (RF), Multi-layer Perceptron Neural Network (MLPN), and Support Vector Machine (SVM). …”
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