Modelling fatigue induced change in hyperelastic response of SBR/NR blends by Adtihya Nambiar, P. Mythravaruni

“…The experimental data obtained from the tensile test is used in an optimisation algorithm to find the model parameters that provide the best fit to the experimental behaviour. …”
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Multiobjective Optimization System of Extrusion Process Parameters for Targeted Microtubes Based on RSM and NSGA-II by Qingqing Zhang, Guobao Jin, Guanghui Dai

“…Finally, the nondominated sorting genetic algorithm II (NSGA-II) and response surface model (RSM) were mixed to find the optimal parameters of the tube extrusion. …”
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CLINICAL MANIFESTATIONS OF TRAUMATIC INTRACRANIAL HEMATOMAS WITH DIFFERENT OUTCOMES by G. V. Anisimov, Y. I. Kravtsov

“…In addition to the assessment of consciousness and neurological status in the most acute period of TBI a comprehensive study of laboratory indicators of stress reactions should be included in the diagnostic algorithm which will allow to determine compensatory mechanisms status, to predict their changes and the deterioration of the patient.…”
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Analysis of the generalization ability of neural networks based on the NN-MPS-interpolation model by Bo-Tao Wang, Zhe Sun, Li-Yuan Dou

“…A feedforward fully connected neural network interpolation model (NN-MPS-interpolation model) based on the backpropagation algorithm is developed. This model takes parameters with strong correlations to the pressure Poisson equation as inputs to output the desired pressure values, achieving predictions for high-resolution problems using low-resolution examples. …”
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A systematic approach to the scale separation problem in the development of multiscale models. by Pinaki Bhattacharya, Qiao Li, Damien Lacroix, Visakan Kadirkamanathan, Marco Viceconti

“…Throughout engineering there are problems where it is required to predict a quantity based on the measurement of another, but where the two quantities possess characteristic variations over vastly different ranges of time and space. …”
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Short-range order stabilizes a cubic iron alloy in Earth’s inner core by Zhi Li, Sandro Scandolo

“…Ab-initio methods struggle with the alloy’s vast configurational complexity, limiting reliable property predictions. To overcome this, here we integrate a hybrid Monte Carlo sampling algorithm with a deep-learning interatomic potential to compute the Fe-Si binary phase diagram and sound velocities at inner-core boundary pressures. …”
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Position Control Using Trajectory Tracking and State Estimation of a Quad-rotor by Onur Sarıpınar, Muharrem Mercimek

“…State estimation techniques are used to obtain and predict information about the state of the aircraft. …”
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Energy-efficient wireless sensor network for real-time environmental monitoring: Simulink-based design by Fatai Olatunde Adunola, Victor Nnamdi Omeke, Ashraf Adam Ahmad, Asharimantun Reuben Bunu, Alfred John Nkohon, Emmanual Obiajulu

“…Simulation results showed energy reductions of up to 13.4% and battery life increases of up to 15.49% compared to traditional methods enabling the system to remain in the field for longer. …”
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A Lightweight Model for Shine Muscat Grape Detection in Complex Environments Based on the YOLOv8 Architecture by Changlei Tian, Zhanchong Liu, Haosen Chen, Fanglong Dong, Xiaoxiang Liu, Cong Lin

“…Compared with the latest YOLOv11n, our model achieved a 3.3% improvement in mAP, with reductions of 26.4% in parameters, 14.3% in FLOPs, and 14.6% in inference time, demonstrating comprehensive enhancements. …”
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Numerical study on the suppression effect of potassium salt on the infrared radiation signatures of rocket exhaust plumes by Qinglin Niu, Jie Ma, Wenqiang Gao, Xiaying Meng, Hongli Wang

“…In addition, afterburning is nearly completely suppressed at 4 % KOH, with infrared signature reductions of 79.1 % in the 2.7 μm band and 56.7 % in the 4.3 μm band. …”
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ALLERGIC DISEASES AND METABOLIC SYNDROME: SOME ASPECTS OF THE COMBINED COURSE by V.D. Sakevych, V.I. Sakevych, T.A. Trybrat, S.V. Shut

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MMG-Based Motion Segmentation and Recognition of Upper Limb Rehabilitation Using the YOLOv5s-SE by Gangsheng Cao, Shen Jia, Qing Wu, Chunming Xia

“…Additionally, the model demonstrated exceptional accuracy in predicting motion categories, achieving an accuracy of 98.9%. …”
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Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing by Ming Li, Klaus Joehnk, Peter Toscas, Luis Riera Garcia, Huidong Jin, Tapas K. Biswas

“…The prediction intervals generally aligned well with nominal levels, demonstrating their reliability. …”
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A multimodal approach for ADHD with coexisting ASD detection for children by Jungpil Shin, Sota Konnai, Md. Maniruzzaman, Yoichi Tomioka, Yong Seok Hwang, Akiko Megumi, Akira Yasumura

“…Each task had two conditions: trace and predict. Various statistical features were derived from pen tablet and fNIRs data for each task. …”
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Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments. by Janice Yang, Neil J Daily, Taylor K Pullinger, Tetsuro Wakatsuki, Eric A Sobie

“…We used the genetic algorithm (GA), a heuristic parameter calibration method, to tune ion channel parameters in a mathematical model of iPSC-CM physiology. …”
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Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree by Giulia Di Teodoro, Marta Monaci, Laura Palagi

“…The interpretability of models has become a crucial issue in Machine Learning because of algorithmic decisions' growing impact on real-world applications. …”
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Nav2Scene: Navigation-driven fine-tuning for robot-friendly scene generation by Bowei Jiang, Tongyuan Bai, Peng Zheng, Tieru Wu, Rui Ma

“…Then, we pre-compute the PPS of 3D scenes from existing datasets and train a ScoreNet to efficiently predict the PPS of the generated scenes. Finally, the predicted PPS is used to guide the fine-tuning of existing scene generators and produce indoor scenes with higher PPS, indicating improved suitability for robot navigation. …”
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Inverse Modeling for Subsurface Flow Based on Deep Learning Surrogates and Active Learning Strategies by Nanzhe Wang, Haibin Chang, Dongxiao Zhang

“…Abstract Inverse modeling is usually necessary for prediction of subsurface flows, which is beneficial to characterize underground geologic properties and reduce prediction uncertainty. …”
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Research on the Molding Design and Optimization of the Molding Process Parameters of the Automobile Trunk Trim Panel by Youmin Wang, Zhaozhe Zhu, Lingfeng Tang, Qinshuai Jiang

“…In order to put forward the theoretical calculation formula for the compression force of the compression mold of the trunk trim panel, obtain the influence trend of the process parameters on the molding quality of the trunk trim panel, and obtain the optimal process parameters combination for the compression molding of the trunk trim panel, four process parameters, the heating temperature, time, compression pressure, and holding time, which affected the compression molding, were selected as the level factors; the maximum thinning rate, maximum thickening rate, and shrinkage rate of the trunk trim panel were selected as evaluation indicators and orthogonal experiments were designed and completed; the comprehensive weighted scoring method was used to obtain the comprehensive score results and obtain the comprehensive evaluation indicators of the best combination of process parameters of trunk trim panel; BP neural network and genetic algorithm were used to study the change trend of the evaluation indicators of trunk trim panel with the changes of process parameters; based on the optimal process parameter combination and the established neural network’s prediction function, the maximum thinning rate, maximum thickening rate, and shrinkage rate under a single process parameter change could be predicted, and the influence of a single process parameter on the maximum thinning rate, maximum thickening rate, and shrinkage rate could be obtained; the process parameters were optimized, and a maximum thinning rate of 28%, a maximum thickening rate of 4.3%, and a shrinkage rate of 0.8% were obtained; the optimal molding process parameters of the trunk trim panel were heating temperature of 209°C, heating time of 62 s, molding pressure of 14 kPa, and holding pressure time of 49 s; after optimization, the maximum shrinkage rate was 28.0880%, the maximum thickening rate was 44.3264%, and the shrinkage rate was 0.8901%; according to the optimal process parameters, the quality of the trunk trim panel was very good, which met the production quality requirements.…”
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Semi-analytical modeling and multi-objective optimization of horizontal-well deep borehole heat exchangers by Zhiwei Wang, Xuemei Zhang, Hui Zhang, Hangyu Liu, Shangkuan Yang, Zheng Qian, Jianguo Feng

“…This study develops a high-efficiency semi-analytical model to predict the long-term thermal behavior of horizontal-well deep borehole heat exchangers (DBHEs). …”
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