Multivariate predictive modeling of compressive strength in ground granulated blast furnace slag/fly ash-based alkali-activated concrete by Dina A. Emarah

“…The findings offer actionable insights for optimizing AAC formulations and further support the broader adoption of AAC as an eco-friendly construction material. …”
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An Improved Short-Term Electricity Load Forecasting Method: The VMD–KPCA–xLSTM–Informer Model by Jiawen You, Huafeng Cai, Dadian Shi, Liwei Guo

“…This paper proposes a hybrid forecasting method (VMD–KPCA–xLSTM–Informer) based on variational-mode decomposition (VMD), kernel principal component analysis (KPCA), extended long short-term memory network (xLSTM), and the Informer model. …”
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Anesthesia depth prediction from drug infusion history using hybrid AI by Liang Wang, Yiqi Weng, Wenli Yu

“…Performance was assessed using Mean Squared Error (MSE) and compared against other models. Results The hybrid model demonstrated superior predictive performance compared to conventional regression approaches. …”
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Evaluation of Unmanned Multicopters’ Performance Indicators for Pesticide and Agrochemical Application by V. P. Asovskiy, A. S. Kuzmenko, O. V. Khudolenko

“…(Materials and methods) The authors used scientific and technical information and experimental materials, applied methods of system, statistical and functional-cost analysis, mathematical modeling, object and process parameter optimization, as well as previously developed methodological approaches to studying the aerial distribution of substances. …”
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Reconstructing the Spectrum of a Polyharmonic Signal under Slow Fluctuations in the Sampling Period by A. A. Monakov

“…The error in estimating the sampling period comprised 5 % of its mean value.Conclusion. …”
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Simulation and Experimental Studies of Heat and Moisture Transfer and Solute Migration during Hot Air Drying of Apple Slices by ZHAO Zhe, YUAN Yuejin, LIU Zhongbin, PENG Yiting, SHE Hailong, YIN Peng

“…The results of this study provide a theoretical basis for the analysis and optimization of the drying quality of fruits and vegetables and for energy saving and efficiency improvement in the drying process.…”
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A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors by Nahshon Obiri, Kristof van Laerhoven

“…Compared to a baseline model that considers only Multiple Walls (LDPLSM-MW), the enhanced approach reduced the root mean square error (RMSE) from 10.58 dB to 8.04 dB and increased the coefficient of determination (R2) from 0.6917 to 0.8222. …”
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Three-dimensional Thermal-hydraulic Design of Fuel Element for Small Modular Fluoride-salt-cooled High-temperature Advanced Reactor by DING Tongwei1, ZHANG Dalin2, CHEN Shuo3

“…Comparing with the experimental results, the maximum relative error of SST k-ω model is 7.8%, which shows the best numerical accuracy. …”
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Formación continuada y uso de listas de verificación: 2 factores determinantes para mejorar la atención a la parada cardiorrespiratoria by Jerónima Vicens Ferrer, David Salomón Sánchez Cuadrado Olea, Maria Isabel Ceniceros Rozalén, Catalina Terrasa Arrom, Miguel Agudo García, Jaume Gaspar Servera, Maria del Mar Ponce Abellán

“…Conclusion: Periodic training and checklists allow for the optimization of CPA management, reducing the insecurity of those leading the effort, minimizing errors attributable to human factors, and facilitating the analysis of interventions performed during resuscitation.…”
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Cereal and Rapeseed Yield Forecast in Poland at Regional Level Using Machine Learning and Classical Statistical Models by Edyta Okupska, Dariusz Gozdowski, Rafał Pudełko, Elżbieta Wójcik-Gront

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Comparison of Modeling with Fuzzy Logic Method and Mixture Design in Predicting the Formulation of Ziziphora Essential Oil Nanoemulsion Production by Reza Beigzadeh, Omid Ahmadi

“…The comparison of the fuzzy logic method's errors with those of the mixture design model (84.2% and 72.7% errors for estimating average particle size and antioxidant property) clearly highlights the superiority of the fuzzy modeling technique.…”
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Defining Biological Variability, Analytical Precision and Quantitative Biophysiochemical Characterization of Human Urinary Extracellular Vesicles by Edita Aksamitiene, Jaena Park, Marina Marjanovic, Stephen A. Boppart

“…ABSTRACT The magnitude of combined analytical errors of urinary extracellular vesicle (uEV) preparation and measurement techniques (CVA) has not been thoroughly investigated to determine whether it exceeds biological variations. …”
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Intelligent prediction and oriented design of high-hardness high-entropy ceramics by Anzhe Wang, Jicheng Liu, Linwei Guo, Kejie Qu, Haishen Xie, Yawei Li, Bin Du

“…This work utilizes machine learning and heuristic optimization algorithms to achieve accurate predictions of bulk high-entropy ceramics hardness (with validation set errors <10 %) and the oriented design of high-entropy ceramics with a hardness of 25 GPa (with an average error of 2.6 %). …”
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Hybridized Deep Learning Model for Perfobond Rib Shear Strength Connector Prediction by Jamal Abdulrazzaq Khalaf, Abeer A. Majeed, Mohammed Suleman Aldlemy, Zainab Hasan Ali, Ahmed W. Al Zand, S. Adarsh, Aissa Bouaissi, Mohammed Majeed Hameed, Zaher Mundher Yaseen

“…In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient (R = 0.96) and lowest value root mean square error (RMSE = 0.03936 KN). …”
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Building electrical consumption patterns forecasting based on a novel hybrid deep learning model by Nasser Shahsavari-Pour, Azim Heydari, Farshid Keynia, Afef Fekih, Aylar Shahsavari-Pour

“…Accurate prediction of electrical energy consumption in smart buildings is a critical challenge for optimizing energy management systems, reducing costs, and improving overall efficiency. …”
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Prediction of teaching quality in the context of smart education: application of multimodal data fusion and complex network topology structure by Chunzhong Li, Chenglan Liu, Wenliang Ju, Yuanquan Zhong, Yonghui Li

“…The prediction model based on attention mechanism optimized deep neural network achieved an average accuracy of 94.16% in the first test; the average F1 score was 90.60%; the AUC (Area Under the Curve) value was 0.975; the average mean square error was 0.271. …”
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Construction of mathematical models to predict M25 and M10 coke quality indices by E. N. Stepanov, A. N. Smirnov, D. I. Alekseev

“…It is proposed to use them to optimize the petrographic charge factors by various optimality criteria of the coke quality indices.…”
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Determination of 34 Elements in Ground Substrate of Black Soil by ICP-MS/OES with Alkali Melting Digestion by Haiyue YU, Qi ZHANG, Bing LU, Chenhao HAN, Taoyuan WANG, Mingliang LI

“…On this basis, the detection limit of the samples was 0.010−50.13μg/g by the experimental method of control samples, and the results satisfied the requirements of soil analysis and detection. At the same time, different kinds of soil reference materials were selected for practical testing, and the measured values of each element were basically consistent with the standard values, with the relative standard deviations (RSDs) between 0.48%−4.53% and the relative errors between −5.23%−4.85%. …”
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Drying kinetics and characteristics of Acori tatarinowii rhizoma under hot air thin-layer drying at different temperatures by Yingna Le, Kun Shi, Chengfu Wang, Jingying Guo, Yaohui Ye, Yuexing Ma, Ruiping Wang, Fangyan Cai, Shaolong Ma, Jinlian Zhang, Lingyun Zhong

“…Moreover, this model demonstrated lower error metrics ( $${\chi }^{2}$$ , SSE, and RMSE) with minimal variations between temperatures. …”
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Long and short term fault prediction using the VToMe-BiGRU algorithm for electric drive systems by Lihui Zheng, Xu Fan, Zongshan Kang, Xinjun Jin, Wenchao Zheng, Xiaofen Fang

“…The VToMe-BiGRU is an intelligent analysis method applied to automobile workshops, which is closer to the data source for data processing and analysis, alleviates the strong dependence on real-time network transmission, reduces the time consuming and labor-intensive process of manually extracting and analyzing the features, and improves the accuracy and reliability of the fault prediction. …”
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