Simulación del vaciado continuo de perfiles de aceros al carbono de baja aleación//Simulation of the continuous casting of low carbon steel profiles by Yusdel Díaz-Hernández, Alberto Fiol-Zulueta, José Arzola-Ruiz

“…The most outstanding characteristic of the model was the inclusion of complex processes of heat interchange, metal phase changes, distribution of temperatures in the mould, chemical composition of the metal, flow of water in the primary and<br />secondary cooling system and the casting speed. Moreover, the algorithm permitted to predict the behaviour of the process variables in the continuous casting of steel according to its profile and type.…”
Get full text

Linking dietary fiber to human malady through cumulative profiling of microbiota disturbance by Xin Zhang, Huan Liu, Yu Li, Yanlong Wen, Tianxin Xu, Chen Chen, Shuxia Hao, Jielun Hu, Shaoping Nie, Fei Gao, Gengjie Jia

“…By leveraging microbiota disturbance similarities, we (1) classified 32 types of dietary fibers into six functional subgroups, revealing correlations with fiber solubility; (2) established associations among 161 diseases, uncovering shared microbiota disturbance patterns that explain disease co‐occurrence (e.g., type II diabetes and kidney diseases) and distinct microbiota patterns that discern symptomatically similar diseases (e.g., inflammatory bowel disease and irritable bowel syndrome); (3) designed a body‐site‐specific microbiota disturbance scoring scheme, computing a disturbance score (DS) for each disease and highlighting the pronounced capacity of Crohn's disease to disturb gut microbiota (DS = 14.01) in contrast with food allergy's minimal capacity (DS = 0.74); (4) identified 1659 fiber‐disease associations, predicting the potential of dietary fiber to modulate specific microbiota changes associated with diseases of interest; (5) established murine models of inflammatory bowel disease to validate the preventive and therapeutic effects of arabinoxylan that notably perturbed the Bacteroidetes and Firmicutes phyla, as well as the Bacteroidetes and Lactobacillus genera, aligning with our model predictions. …”
Get full text

A Statistical Approach in Designing an RF-Based Human Crowd Density Estimation System by S. Y. Fadhlullah, Widad Ismail

“…A signal path loss propagation model was also proposed to assist in predicting the human crowd density. The human crowd properties verified by using the statistical approach may offer a new side of understanding and estimating the human crowd density.…”
Get full text

The dual-edged potential of AI autonomously defining loss functions by Abbas Ghori

“…Abstract A loss function is one of the key components considered in machine learning as they steer the model toward the optimal performance by quantifying the discrepancy between the predicted outcome and the actual outcome. They predominantly act as guiding principles for any optimization algorithm, thereby influencing both the convergence characteristics as well as the generalization of the model. …”
Get full text

Reactor modeling and kinetic parameters estimation for diethylbenzene (DEB) dehydrogenation reactions by M.E zeynali, H. Abedini, H. R. Sadri

“…The conversion of DEB and ethylvinyl benzene (EVB) in the reactor was predicted by mathematical modeling and compared with experimental results. …”
Get full text

Control strategies for multi-rotor wind turbines by F. Matras, M. Dinhoff Pedersen

“…In contrast to most literature on the topic, the underlying multi-rotor model includes the aerodynamic interactions between rotors. The model predicts that these interactions are central for effective control of multi-rotor wind turbines under some conditions. …”
Get full text

Digit recognition using decimal coding and artificial neural network by Toufik Datsi, Khalid Aznag, Ahmed El Oirrak

“…The backpropagation algorithm was used for the training dataset and feed-forward for the testing dataset. …”
Get full text

Fuzzy Pheromone Potential Fields for Virtual Pedestrian Simulation by Meriem Mandar, Azedine Boulmakoul

“…Said fields provide virtual pedestrians with better visibility of their surroundings and its various components (goals and obstacles). The predictions provided by the first-order traffic flow theory are confirmed by the results of the simulation. …”
Get full text

Identification of plankton habitats in the North Sea by Rene‐Marcel Plonus, Jens Floeter

“…Plankton distributions are closely linked to climate change and shape the seascape for higher trophic levels, so monitoring plankton distributions and defining ecological niches will help to understand and predict ecosystem responses. Here we apply a machine learning autoencoder and a density‐based clustering algorithm to high‐frequency datasets sampled with a ROTV Triaxus in the North Sea. …”
Get full text

APOPTOSIS IN CITY SYSTEMS: A BIOMIMETIC APPROACH TO CITY REGENERATION by Stephen Ajadi

“…Aided with streamlined programmatic principles, computational and algorithmic design, city systems are studied in an African context. …”
Get full text

Code Generation for Collectible Card Games with Complex APIs by John Licato, Logan Fields, Brayden Hollis

“…Given these practical limitations, is it possible to utilize these massive code-generation LMs to write code compatible with a given API? We develop an algorithm that selects code examples using a smaller LM trained to predict which features of an API are likely to be used in the resulting code, which is a simpler problem than actually generating the code. …”
Get full text

Control and readout of a 13-level trapped ion qudit by Pei Jiang Low, Brendan White, Crystal Senko

“…We report on tools we have developed for predicting energy states that are practical for qudit encoding, validated with good agreement with our experimental data. …”
Get full text

Optimization Design on Functionally Graded Cem for Trains Based on LPM Model with Calibrated Parameters by Ruixian Qin, Bingzhi Chen

“…The influence of parameters in graded function on interfacial force and peak acceleration is investigated and optimal design parameters are obtained by Nondominated Sorting Genetic Algorithm (NSGA-II). It is concluded that considering the behavior of the carbody can improve the accuracy of LPM in predicting the longitudinal response, and the gradient CEM is a potential energy configuration mode to improve the crashworthiness of the train in a collision.…”
Get full text

A numerical framework for simulating fluid-structure interaction phenomena by A. De Rosis, S. de Miranda, C. Burrafato, F. Ubertini

“…The lattice Boltzmann method is used to compute fluid dynamics, while the corotational finite element formulation together with the Time Discontinuous Galerkin method are adopted to predict structure dynamics. The Immersed Boundary method is used to account for the presence of an immersed solid in the lattice fluid background and to handle fluid-structure interface conditions, while a Volume-of-Fluid-based method is adopted to take trace of the evolution of the free surface. …”
Get full text

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

“…With the predicted parameters, the tensile test is simulated in ABAQUS incorporating element deletion and the results of the ABAQUS simulation are compared with experimental behaviour and model response. …”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text