Learning protocols for the fast and efficient control of active matter by Corneel Casert, Stephen Whitelam

“…Abstract Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. …”
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Meshing Characteristics Analysis of a High-speed-ratio Compound Gear with Small Tooth Difference by Zhu Feihong, Song Chaosheng, Chang Le, Lü Zhenyu, Du Xuesong, Huang Yucheng

“…The transmission error curve is stable and periodic, without noticeable long waves, and the peak-to-peak value of the transmission error of the whole machine is 46.719 5″. This research provides a theoretical basis for the optimal design of dynamic characteristics and meshing characteristics of the high-speed-ratio compound gear with small tooth difference.…”
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GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection by Yogesh Kumar Sharma, Deepak Singh Tomar, R. K. Pateriya, Surendra Solanki

“…Federated learning preserves user privacy by training the model across a distributed network. A reinforcement learning model initiates proactive defenses, identifying optimal actions against malware threats. …”
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Artificial Bee Colony Algorithm Based on the Division Between Exploration and Exploitation and Its Application in Esophageal Cancer Prediction by WANG Yingcong, YAN Jun, SUN Junwei, WANG Yanfeng

“…In addition, ABC_DEE exhibited superiority in terms of convergence speed and time efficiency. Regarding the optimization problem of the esophageal cancer prediction model based on Kernel Extreme Learning Machine (KELM), in terms of accuracy, ABC_DEE‒KELM achieved 85.83%, which was 3.00% higher than the second-ranked RNSABC‒KELM and 15.72% higher than ABC‒KELM. …”
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YOLOv8-Based Estimation of Estrus in Sows Through Reproductive Organ Swelling Analysis Using a Single Camera by Iyad Almadani, Mohammed Abuhussein, Aaron L. Robinson

“…We then harnessed the power of machine learning to train our model using annotated images, which facilitated keypoint detection and segmentation with the state-of-the-art YOLOv8 algorithm. …”
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Integrating petrophysical data into efficient iterative cluster analysis for electrofacies identification in clastic reservoirs by Mohammed A. Abbas, Watheq J. Al-Mudhafar, Aqsa Anees, David A. Wood

“…The optimized EM model developed successfully predicts the core-derived facies classification in two of the studied wells. …”
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The role of artificial intelligence in vascular care by Nehaar Nimmagadda, BS, Edouard Aboian, MD, Sharon Kiang, MD, Uwe Fischer, MD

“…Specific attention is given to the use of machine learning and deep learning in imaging analysis, AI-driven predictive analytics for patient stratification and risk modeling, and the evolution of robotic-assisted surgical techniques. …”
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TECHNOLOGICAL ADVANCES IN ELECTROPLATING: ARTIFICIAL INTELLIGENCE TO PREDICT ZINC COATING THICKNESS ON SAE 1008 LOW CARBON STEELS by Luciano M. L. de Oliveira, Fabiana L. da Silva, Paulo R. Janissek, Juliano C. Toniolo

“…Statistical analysis and supervised machine learning algorithms, including multivariate regression, random forest, and extreme gradient boosting (XGBoost), were employed to develop prediction models. …”
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Oil Development Engineering Company, Tehran, Iran by Seyed Ali Mohammad Tajalli, Mazda Moattari, Vahid Naghavi, Mohammad Reza Salehizadeh

“…Leveraging a hybrid approach that combines the Extended Kalman Filter (EKF), Real-Time Transient Modeling (RTTM), and machine learning algorithms, the framework offers accurate insights into the pipeline’s operational status. …”
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Key technology research and development and prospects for outdoor large- and medium-sized snow groomers by Jiawei SHENG, Zhehan CHEN, Yanli YANG, Chun JIN, Lifang ZHENG, Xixia LIU, Weidong LUO

“…This involved focusing on four aspects: promotion system, operation system, whole machine optimization and manufacturing, and testing evaluation. …”
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Sustainable E-Health: Energy-Efficient Tiny AI for Epileptic Seizure Detection via EEG by Moez Hizem, Mohamed Ould-Elhassen Aoueileyine, Samir Brahim Belhaouari, Abdelfatteh EL Omri, Ridha Bouallegue

“…Because of its superior performance, the ExtraTrees Classifier was selected as the preferred model. For the optimized TinyML model, the accuracy remained practically unchanged, whereas inference time was significantly reduced. …”
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Securing fruit trees future: AI-driven early warning and predictive systems for abiotic stress in changing climate by Muhammad Ahtasham Mushtaq, Muhammad Ateeq, Muhammad Ikram, Shariq Mahmood Alam, Muhammad Mohsin Kaleem, Muhammad Atiq Ashraf, Muhammad Asim, Khalid F. Almutairi, Mahmoud F. Seleiman, Fareeha Shireen

“…AI integrated approaches such as stress prediction, irrigation optimization, and image-based phenotyping have enhanced agriculture, while machine learning models like Random Forest and Gradient Boosting improve stress management. …”
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Harmonizing Traditional Journalistic Values With Emerging AI Technologies: A Systematic Review of Journalists’ Perception by Sangyon Oh, Jaemin Jung

“…Three theoretical models—field theory, human–machine communication, and the technology acceptance model—are employed to understand these dynamics, with field theory addressing power shifts and human–machine communication and the technology acceptance model examining human–AI interaction. …”
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Methods for Designing Technologies of Organic Crop Production by A. M. Zakharov, A. A. Ustroev, E. A. Murzaev, A. D. Komoedov

“…(Results and discussion) At the initial stage of implementing the Waterfall model; a block diagram was developed to outline the general algorithm for designing machine technologies applicable in organic crop production. …”
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Dual Convolution Neural Networks of Ensemble Learning with Attention Mechanism for Rice Classification by Cheng Linfeng

“…In the future, it is necessary to continuously optimize the structure of integrated models and adjust their hyperparameters to achieve better stability.…”
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Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway by YaoFeng Hu, Sen Qin, RuCui Deng

“…Core genes were identified through WGCNA, and a total of 101 machine learning models were constructed, with LASSO+GBM selected as the optimal model, demonstrating robust validation performance. …”
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A Semi-Supervised-Learning-Aided Explainable Belief Rule-Based Approach to Predict the Energy Consumption of Buildings by Sami Kabir, Mohammad Shahadat Hossain, Karl Andersson

“…Experimental results show 20 ± 0.71% higher accuracy of the semi-supervised BRBES than state-of-the-art semi-supervised machine learning models. Moreover, the semi-supervised BRBES framework turns out to be 29 ± 0.67% more explainable than these semi-supervised machine learning models.…”
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Investigation into the monitoring and control of mechanical dynamics in inclined mining equipment utilizing digital twin technology by Panshi XIE, Hang YANG, Yongping WU, Baofa HUANG, Weidian LIN, Leilei YI

“…This system facilitates data visualization, robust human-machine interaction, and process self-optimization during mining operations. …”
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Comprehensive analysis of experimental studies in cold metal transfer welding by Mir Qurrat Ul Ain, Deepak Kumar Naik, Dinesh Kumar Rajendran, Shiva Sekar

“…Future research must prioritize real-time monitoring, machine-learning-assisted optimization, and comprehensive testing standards to expand CMT's industrial adoption.…”
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Spatio-temporal dynamics of human-induced carbon emissions in Southeast Asia (1992–2022) based on nighttime light by Chaoqing Huang, Qian Wu, Yujie Chen, MinhThu Nguyen, Bin Chen, Song Hong, Chao He

“…This study develops a spatial visualization model to estimate carbon emissions in Southeast Asia using calibrated nighttime light data, with DMSP-OLS (Defense Meteorological Satellite Program Operational Linescan System) and NPP-VIIRS (National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite) standardized through polynomial regression and machine learning to ensure consistency. …”
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