Revitalizing agriculture with the potential of cashew nutshell liquid: a comprehensive exploration and synergy with AI by Sneha Nayak, Roopa B. Hegde, Abhishek S. Rao, H. K. Sachidananda

“…Additionally, it highlights the burgeoning role of artificial intelligence and machine learning models in predicting CNSL emissions, yield, crop health, and cashew kernel quality checks, offering a holistic decision support system for supply chain optimization. …”
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Advancing deformation calculation: a physics-informed deep graph learning framework for hyperelastic materials by Faras Brumand-Poor, Matthias Trautmann, Katharina Schmitz

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Production Capacity Prediction for Tight Gas Reservoirs Based on ADASVRLGBM by MENG Sihai, ZHANG Zhansong, GUO Jianhong, HAN Zihao, ZENG Weijie, LYU Hengyang

“…The model utilizes GridSearchCV (Grid Search Cross-Validation) to fine-tune the hyperparameters of each algorithm and applies a genetic algorithm to optimize the weight combinations of the sub-models. …”
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Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges by Yuan Mao, Dangang Shangguan, Qi Huang, Ling Xiao, Dongsheng Cao, Hui Zhou, Yi-Kun Wang

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Challenges of the Biopharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review by Johnderson Nogueira de Carvalho, Felipe Rodrigues da Silva, Erick Giovani Sperandio Nascimento

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Interpretable AI-driven multi-objective risk prediction in heart failure patients with thyroid dysfunction by Massimo Iacoviello, Vito Santamato, Alessandro Pagano, Agostino Marengo

“…In this study, we propose an AI-driven machine learning (ML) approach to predict mortality and hospitalization risk in HF patients with coexisting thyroid disorders.MethodsUsing a retrospective cohort of 762 HF patients (including euthyroid, hypothyroid, hyperthyroid, and low T3 syndrome cases), we developed and optimized several ML models—including Random Forest, Gradient Boosting, Support Vector Machines, and others—to identify high-risk individuals.ResultsThe best-performing model, a Random Forest classifier, achieved robust predictive accuracy for both 1-year mortality and HF-related hospitalization (area under the ROC curve ∼0.80 for each). …”
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Multimodal ultrasound radiomics containing microflow images for the prediction of central lymph node metastasis in papillary thyroid carcinoma by Jiangyuan Ben, Jiangyuan Ben, Qiying Yv, Pengfei Zhu, Junhao Ren, Pu Zhou, Guifang Chen, Ying He, Ying He

“…Finally, the optimal model was interpreted and visualized via Shapley additive explanation (SHAP).ResultsIn each modality, 1561 features were extracted from the ultrasound images. …”
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Research on the Prediction of Pipelines Corrosion Rate Based on GA-LSSVM by CHEN Yong-hong, SU Yong-sheng, HU Ping

“…Corrosion rate is an important characteristic parameter to reflect the corrosion dynamics process of pipeline.In order to accurately evaluate the long-term operation reliability and remaining life of pipeline, the prediction of corrosion rate is particularly important.Least squares support vector machine(LSSVM)is a method based on machine learning, which is often used in classification and prediction research.Since penalty parameters γ and kernel parameters σ2 are two important parameters of LSSVM, the value of these two parameters can only be obtained by experience in calculation, causing a great impact on the calculation results.In this paper, the genetic algorithm(GA)was used to optimize the parameters, the GA-LSSVM prediction model was built and the model was applied to the prediction of pipeline corrosion rate.Compared with the results of other prediction models, the results showed that the accuracy of GA-LSSVM model and prediction results were relatively higher, which could realize the prediction of pipeline corrosion rate.…”
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Transient High-Frequency Electromagnetic Force Calculation for Linear Induction Motors Under Pulse Width Modulation Current Excitation by Mingke Li, Jin Xu, Junjie Zhu, Yuhu Wang, Tairan Chen

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Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel by Xiaochang Xie, Mutong Liu, Ping Yang, Zenan Yang, Chengbo Pan, Chenchong Wang, Xiaolu Wei

“…In this study, we collected 216 creep data of austenitic heat-resistant steel, selected a variety of different machine learning algorithms to establish creep life prediction models, calculated and introduced a large amount of physical metallurgy knowledge highly related to creep based on Thermo-Calc, and converted the creep life into the form of the Larson–Miller parameter to optimize the data distribution, which effectively improved the prediction accuracy and interpretability of the model. …”
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Classification of SERS spectra for agrochemical detection using a neural network with engineered features by Mateo Frausto-Avila, Monserrat Ochoa-Elias, Jose Pablo Manriquez-Amavizca, María del Carmen González-López, Gonzalo Ramírez-García, Mario Alan Quiroz-Juárez

“…In this context, we present a machine-learning model based on a feedforward neural network for the rapid and accurate classification of SERS spectra. …”
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A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management by Juliano Pimentel, Alistair A. McEwan, Hong Qing Yu

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Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations by Martina Casari, Piotr A. Kowalski, Laura Po

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Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district by Yi Zhao, Shuya Yang, Haibin Shi, Haoqi Han, Yunlei Dong, Xianyue Li, Jianwen Yan, Yan Yan, Xu Dou, Feng Tian, Qingfeng Miao

“…The soil salinity inversion model constructed using Random Forest demonstrates higher R2 values and lower MAE and RMSE compared to the Support Vector Machine and Gradient Boosting Tree, establishing it as the optimal model for soil salinity inversion. …”
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Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas by Cosmina-Mihaela Rosca, Madalina Carbureanu, Adrian Stancu

“…For this purpose, 19 predictive models were developed and compared: 12 machine learning algorithms, 7 deep learning, and 1 forecasting model based on structural component analysis. …”
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Comprehensive Evaluation of the Rheological, Tribological, and Thermal Behavior of Cutting Oil and Water-Based Metalworking Fluids by Florian Pape, Belal G. Nassef, Stefan Schmölzer, Dorothea Stobitzer, Rebekka Taubmann, Florian Rummel, Jan Stegmann, Moritz Gerke, Max Marian, Gerhard Poll, Stephan Kabelac

“…Additionally, the thermal conductivity and heat capacity of water-based fluids were substantially higher than those of the cutting oils, contributing to more efficient heat dissipation during machining. These findings, along with the reported data, intend to guide future researchers and industry in selecting the most appropriate cutting fluids for their specific applications and provide valuable input for computational models simulating the influence of MWFs in the primary and secondary shear zones between cutting tools and the workpiece/chiplet.…”
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Water source, latrine type, and rainfall are associated with detection of non-optimal and enteric bacteria in the vaginal microbiome: a prospective observational cohort study neste... by Anna E Czapar, Souvik Paul, Garazi Zulaika, Fredrick Otieno, Walter Agingu, Adit Chaudhary, Runa Bhaumik, Anna Maria van Eijk, Stefan J Green, Elizabeth Nyothach, Penelope A Phillips-Howard, Supriya D. Mehta

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Researching the landscape of predictive emissions monitoring system: a review of literature and technology trends by Fanxing Wang, Yu Pang, Ling Bai, Marc Godin

“…Machine learning-based PEMS has significant advantages in handling complex, non-linear problems where simpler models may struggle. …”
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Third-generation biomass for bioplastics: a comprehensive review of microalgae-driven polyhydroxyalkanoate production by Rijuta Ganesh Saratale, Si-Kyung Cho, Ram Naresh Bharagava, Anil Kumar Patel, Vivekanand Vivekanand, Shashi Kant Bhatia, Luiz Fernando Romanholo Ferreira, Han Seung Shin, Mukesh Kumar Awasthi, Sankha Chakrabortty, Ramesh Kumar, Ganesh Dattatraya Saratale

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AI-powered approaches for enhancing remote sensing-based water contamination detection in ecological systems by Li Yang, Zhang Ziwen, Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

“…Recent advancements in artificial intelligence (AI) offer promising solutions to enhance water contamination detection, particularly by leveraging machine learning algorithms and sensor networks for continuous monitoring.MethodsThis paper presents a novel AI-powered approach for improving water contamination detection, which incorporates real-time data processing and predictive modeling to identify contamination events and optimize response strategies. …”
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