Inspection of temperature alteration and it’s prediction possibility in Ardebil province using statistical analysis and adaptive neuro -fuzzy inference system

“…According to present article adaptive Neuro – fuzzy inference system mostly has acceptable function through anticipation of monthly minimum, maximum and average temperature in the stations of Ardebil province.…”
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On the sample complexity of quantum Boltzmann machine learning by Luuk Coopmans, Marcello Benedetti

“…By using the relative entropy as a loss function, this problem can be solved without encountering barren plateaus. …”
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A Note on Radiative Heat Transfer to Peristaltic Flow of Sisko Fluid by Obaid Ullah Mehmood, Constantin Fetecau

“…Analytical solutions for the stream function, axial pressure gradient, axial velocity, skin friction, and Nusselt number are derived for three different cases (i.e., shear thinning fluid, viscous fluid, and shear thickening fluid). …”
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Attention-enhanced StrongSORT for robust vehicle tracking in complex environments by Wei Xu, Xiaodong Du, Ruochen Li, Bingjie Li, Yuhu Jiao, Lei Xing

“…Abstract While multi-object tracking is critical for autonomous driving systems, traditional algorithms exhibit three fundamental limitations in complex scenarios: (1) blurred feature representation under occlusion and re-identification scenarios causing identity switches, (2) insufficient sensitivity to scale-variant targets due to fixed geometric constraints in conventional IoU-based loss functions, and (3) gradient degradation in deep convolutional layers hindering discriminative feature learning. …”
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Moving Front Solution of the Reaction-Diffusion Problem by Evgeny A. Antipov, Vladimir T. Volkov, Natalia T. Levashova, Nikolay N. Nefedov

“…The moving front solution is a function  the distinctive feature of which is the presence in the domain  of its definition  of a subdomain where the function  has a large gradient. …”
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Deep Neural Networks for Accurate Depth Estimation with Latent Space Features by Siddiqui Muhammad Yasir, Hyunsik Ahn

“…To further improve the accuracy of depth boundaries and local features, a new loss function is introduced. This function combines latent loss with gradient loss, helping the model maintain the integrity of depth boundaries. …”
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CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES by Erol Özçekiç, Ümit Yılmaz

“…Liver diseases pose a significant global health challenge due to their impact on metabolic function and the difficulty of early detection. Traditional diagnostic methods such as liver biopsy have limitations due to their invasive nature and high costs. …”
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Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI by Dayeong An, El-Sayed Ibrahim

“…Cardiac MRI was performed 8 and 10 weeks post-treatment to assess global and regional cardiac function. ML algorithms were applied to differentiate sham-treated and irradiated rats based on early changes in myocardial function. …”
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Sign-Changing Solutions for Nonlinear Elliptic Problems Depending on Parameters by Siegfried Carl, Dumitru Motreanu

“…In particular, the existence of extremal constant-sign solutions and their variational characterization as global (resp., local) minima of the associated energy functional will play a key-role in the proof of sign-changing solutions.…”
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Establishment of a low-temperature immersion method for extracting high-activity and high-purity mitochondria from Syntrichia caninervis Mitt. by Wenting Huo, Xiaohua Lin, Mengyu Gao, Xiang Shi, Hongbin Li, Lu Zhuo

“…The purity, structural integrity, and functional activity of the isolated mitochondria were systematically evaluated using western blot analysis, Janus Green B staining, JC-1 membrane potential assays, and electron transport chain (ETC) complex activity measurements. …”
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Resource-Efficient Acoustic Full-Waveform Inversion via Dual-Branch Physics-Informed RNN with Scale Decomposition by Cai Lu, Jijun Liu, Liyuan Qu, Jianbo Gao, Hanpeng Cai, Jiandong Liang

“…Additionally, a downscaled spatiotemporal PIRNN branch is introduced, transforming the original-scale error into a loss function via scale decomposition, which drives the inversion process in the downscaled domain. …”
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Thermo-gaseous-mechanical coupling phase-field model for brittle crack propagation in tungsten by Yuanyuan Wang, Xinxin Hou, Yan-Dong Wang

“…The current developed model builds upon a coupled mechanics, gas diffusion and temperature gradient, driven by minimizing the energy function. …”
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Machine learning based predictive model of the risk of Tourette syndrome with SHAP value interpretation: a retrospective observational study by Aimin Li, Yueying Liu, Yufan Luo, Xue Xiao, Wei Xiao, Ruijin Xie, Xianhui Deng, Zhe Chen, Qian Zhou, Yue Gong, Zhen Chen, Hua Xu

“…Clinical data, encompassing complete blood counts, liver and kidney function assessments, blood glucose levels, and serum electrolyte analyses, were collected. …”
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Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques by Oana Vîrgolici, Daniela Lixandru, Andrada Mihai, Diana Simona Ștefan, Cristian Guja, Horia Vîrgolici, Bogdana Virgolici

“…Several machine learning models, namely linear regression, polynomial regression, Gradient Boosting, and Extreme Gradient Boosting, were employed to predict changes in medical parameters as a function of body weight variation. …”
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Topology Optimization Method of Magnetic Resonance Logging Sensor Magnet Structure by SONG Yanzuo, YU Huiyuan, CHEN Jingzhi, SHEN Yue, XU Xianneng, XU Zheng

“…The moving asymptote method of gradient descent is employed to update the density based on gradient information from the objective function, leading to an iterative approach for achieving an optimal solution. …”
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An object detection model AAPW-YOLO for UAV remote sensing images based on adaptive convolution and reconstructed feature fusion by Yiming Wu, Xiaofang Mu, Hong Shi, Mingxing Hou

“…Finally, we adopt a gradient-enhancing strategy with the Wise Intersection over Union (Wise-IoU) loss function to balance the gradient contributions from anchor boxes of different qualities during training, thereby improving regression accuracy. …”
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A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study by Jing Xu, Wenjin Zhang, Wenli Liu

“…Three machine learning models—Gradient Boosting, Random Forest, and Boosted XGBoost—were used to predict depression risk over three years, incorporating 10 demographic, 5 health, 13 chronic disease, 3 lifestyle, and 2 physical function factors. …”
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Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance by Heming Bai, Zhi Zheng, Yuanpeng Zhang, He Huang, Li Wang

“…., random forest, extreme gradient boosting, gradient boosting regression, and support vector regression). …”
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CO2 adsorption on NaOH and acid modified montmorillonite: Response surface methodology and machine learning modeling by Pardis Mehrmohammadi, Amir Ahmadvand, Ahad Ghaemi

“…Several ML models were evaluated, including Decision Tree Regressor, Random Forest Regressor, AdaBoost Regressor, Gradient Boosting Regressor, Extreme Gradient Boosting Regressor, Support Vector Machine, Multilayer Perceptron (MLP), Radial Basis Function, and Kernel Ridge Regressor. …”
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Two-Layer Electroosmotic Flow in a Parallel Plate Microchannel with Sinusoidal Corrugation by Long Chang, Mandula Buren, Geming Bai, Yanjun Sun, Yongjun Jian

“…The variation of the average velocity increment (roughness function) <i>u</i>_2m related to wall roughness tends to decrease with the increase of the corrugation wave number <i>λ</i>, the electrokinetic width <i>K</i>, the depth ratio <i>h_r</i> of the bottom to top fluid, the zeta potential ratio <i>ζ</i> and the dimensionless pressure gradient <i>G</i>; and increases with the increase of the viscosity ratio <i>μ_r</i> of the bottom to top fluid. …”
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