Nonlinear Fractional Evolution Control Modeling via Power Non-Local Kernels: A Generalization of Caputo–Fabrizio, Atangana–Baleanu, and Hattaf Derivatives by F. Gassem, Mohammed Almalahi, Osman Osman, Blgys Muflh, Khaled Aldwoah, Alwaleed Kamel, Nidal Eljaneid

“…This framework utilizes a power non-local fractional derivative (PFD), which is a generalized fractional derivative that unifies several well-known derivatives, including Caputo–Fabrizio, Atangana–Baleanu, and generalized Hattaf derivatives, as special cases. It uniquely features a tunable power parameter “<i>p</i>”, providing enhanced control over the representation of memory effects compared to traditional derivatives with fixed kernels. …”
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Pharmacological potential of 6,11-dihydro[1,2,4]triazolo[4’,3’:1,6]-pyrido[3,4-b]-5-carboxylic acid and its esters by S. O. Fedotov, A. S. Hotsulia

“…The online resource SwissADME has been used to analyse the physicochemical properties and pharmacokinetic parameters of the studied compounds. The molecular docking method, based on computational algorithms, has been used to predict and study interactions, including the identification of potential binding sites, estimation of energy parameters and spatial configuration of molecules. …”
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Exact collective occupancies of the Moshinsky model in two-dimensional geometry by Arkadiusz Kuroś, Adam Pieprzycki, Edyta Gawin, Przemysław Kościk

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A multimodal functional structure-based graph neural network for fatigue detection by Dongrui Gao, Zhihong Zhou, Zongyao Peng, Haokai Zhang, Shihong Liu, Manqing Wang, Hongli Chang

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Automatic Identification of the Main Ionospheric Trough in Total Electron Content Images by Gregory Starr, Sebastijan Mrak, Yukitoshi Nishimura, Michael Hirsch, Prakash Ishwar, Joshua Semeter

“…We show that statistics computed on the resulting labels are robust to our choice of algorithm parameters and that we are able to match the results of Aa, Zou, et al. (2020, doi:https://doi.org/10.1029/2019JA027583) with a particular selection of the parameters. …”
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Wavefront Sensor for the Determination of Nanostructured Surface Defects by V.V. Buchenko, A.A. Goloborodko, V.V. Lendel, O.S. Oberemok

“…The theoretical analysis and computer simulation of the wavefront sensor operation to detect the spatial distribution of the refractive index are performed. …”
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Transferable deep learning with coati optimization algorithm based mitotic nuclei segmentation and classification model by Amal Alshardan, Nazir Ahmad, Achraf Ben Miled, Asma Alshuhail, Yazeed Alzahrani, Ahmed Mahmud

“…Abstract Image processing and pattern recognition methods have recently been extensively implemented in histopathological images (HIs). These computer-aided techniques are aimed at detecting the attentive biological markers for assisting the final cancer grading. …”
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Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation by Khondker Fariha Hossain, Sharif Amit Kamran, Joshua Ong, Alireza Tavakkoli

“…Through this technique, the student model inherits the capacity for fine segmentation while operating with a significantly reduced parameter set and computational footprint. Additionally, introducing a novel training strategy optimizes knowledge transfer, ensuring the student model captures the intricate mapping of features essential for high-fidelity segmentation. …”
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DSFA-SwinNet: A Multi-Scale Attention Fusion Network for Photovoltaic Areas Detection by Shaofu Lin, Yang Yang, Xiliang Liu, Li Tian

“…Secondly, a hybrid loss function has been developed with weights optimized employing the Bayesian Optimization algorithm to provide a strategic method for parameter tuning in similar research. Lastly, the fixed window size of Swin-Transformer is dynamically adjusted to enhance computational efficiency and maintain accuracy. …”
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Instantaneous Metabolic Energetics: Data-Driven Modeling Using Function-Based Surrogates and Gradient Boosting by Christopher Buglino, William Z. Peng, Stacy Ashlyn, Hyunjong Song, Howard J. Hillstrom, Joo H. Kim

“…Results: The model accurately predicts instantaneous MEE without subject-specific input parameters. Shapley Additive Explanations were used to investigate energetic features of the learned MEE function and demonstrate alignment with literature. …”
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A software pipeline for systematizing machine learning of speech data by Jimuel Celeste, Mashrura Tasnim, Amable J. Valdés Cuervo, Enrique A. de la Cal, Eleni Stroulia

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COMPLEXITY IN A COURNOT DUOPOLY GAME WITH DIFFERENTIATED GOODS BETWEEN SEMI-PUBLIC AND PRIVATE FIRMS by Georges SARAFOPOULOS, Kosmas PAPADOPOULOS

“…Numerical simulations are carried out to show the complex behaviour. The chaotic features are justified numerically via computing Lyapunov numbers, sensitive dependence on initial conditions, bifurcation diagrams and strange attractors.…”
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Parametric optimization of flow in a solar chimney power plant under variable semi elliptical constraints by Rajamurugu Natarajan, S. Yaknesh, K B Prakash, Mohammed Al Awadh, Qasem M. Al-Mdallal

“…Initially, ANSYS-FLUENT were utilized for computational approximation. The models with greater maximum flow parameter magnitudes were selected for experimentation. …”
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Theoretical Insights into the Impact of Pyrrole and Imidazole Substituents on the BODIPY Chromophore by Patrycja Piękoś, Paweł Lipkowski, Wim Dehaen, Robert Wieczorek, Aleksander Filarowski

“…This paper concerns the in silico studies of the influence of heterocyclic substituents as well as their protonated and deprotonated forms on the spectral characteristics of BODIPY (4,4-difluoro-4-bora-3a,4a-diaza-<i>s</i>-indacene) dyes. Computational studies were carried out in order to reveal the most effective method of modeling of the spectral features of fluorescent BODIPY dyes. …”
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Assimilating Satellite-Based Biophysical Variables Data into AquaCrop Model for Silage Maize Yield Estimation Using Water Cycle Algorithm by Elahe Akbari, Ali Darvishi Boloorani, Jochem Verrelst, Stefano Pignatti

“…In this paper, the AquaCrop model, a water-driven crop growth model, was selected for recalibration and assimilation of satellite-derived biophysical products due to its simplicity and lack of computational complexity. To this end, field samples of soil (sampled before cultivation) and crop features were collected during the growing season of silage maize. …”
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Preoperative multiclass classification of thymic mass lesions based on radiomics and machine learning by Yan Zhu, Li Wang, Aichao Ruan, Zhiyu Peng, Zhenzhong Zhang

“…Therefore, the objective of this study is to rely on clinical parameters and radiomic features extracted from chest computed tomography (CT) scans to facilitate the preoperative classification of TMLs. …”
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逆向法选择渐开线齿轮的变位系数 by 赵振江

“…A new kind of method for the choice of gear modification coefficients is presented.Its main idea is regard gear modification coefficient as a known parameter.The method take full advantage of a computer’s features of quick operation and accurate calculation and judgement function.A computer can find modification coefficients from a lots of modification coefficients which meet the needs of given condition,in the same time user needs input all kinds of given parameters and limited conditions only.…”
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Harmonic Path Integral Diffusion by Hamidreza Behjoo, Michael Chertkov

“…This feature positions H-PID as a strong alternative to traditional methods sampling, such as simulated annealing, particularly for applications that demand analytical control, computational efficiency, and scalability.…”
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Population dynamics and monitoring applied to decision-making by M. J. Conroy, D. C. Lee

“…They also employ a ayesian approach and overcome some of the computational challenges by using Markov chain–Monte Carlo methods. …”
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Classification of Epileptic Seizures by Simple Machine Learning Techniques: Application to Animals&#x2019; Electroencephalography Signals by Illia Pidvalnyi, Anna Kostenko, Oleksandr Sudakov, Dmytro Isaev, Oleksandr Maximyuk, Oleg Krishtal, Olena Iegorova, Ievgen Kabin, Zoya Dyka, Steffen Ortmann, Peter Langendorfer

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