Mutual coupling of neurons in the circadian master clock: What we can learn from fruit flies by Charlotte Helfrich-Förster, Nils Reinhard

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EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning by Waqas Ali, Wesam Alsabban, Muhammad Shahbaz, Ali Al-Laith, Bassam Almogadwy

“…The ejection fraction (EF) is a vital metric for assessing cardiovascular function through cardiac ultrasound. Manual evaluation is time-consuming and exhibits high variability among observers. …”
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Validity of Deep Learning-Based Motion Capture Using DeepLabCut to Assess Proprioception in Children by Maud van den Bogaart, Nina Jacobs, Ann Hallemans, Pieter Meyns

“…Therefore, accurately measuring proprioceptive function in order to identify deficits as soon as possible is important. …”
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Deep Learning Approach for Pneumonia Prediction from X-Rays using A Pretrained Densenet Model by Ahmad Zein Al Wafi, Febry Putra Rochim, Aisya Fathimah

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A novel surrogate model with deep learning for predicting spacial-temporal pressure in coalbed methane reservoirs by Yukun Dong, Xiaodong Zhang, Jiyuan Zhang, Kuankuan Wu, Shuaiwei Liu

“…This paper proposes a novel deep learning–based data-driven surrogate model, AxialViT-ConvLSTM, which integrates AxialAttention Vision Transformer, ConvLSTM, and an enhanced loss function to predict pressure dynamics in CBM reservoirs. …”
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Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection by Sofia Biju Francis, Sofia Biju Francis, Jai Prakash Verma

“…This design is based on analyses of existing deep learning architectures and feature extraction techniques. …”
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Continuous professional development and instructors’ beliefs about and practice of active learning method in Bahir Dar university by Mulugeta Teka Kahsay

“… The objective of this study was to investigate whether there is an overall difference in instructors’ beliefs about and practice of active learning methods as a function of the ongoing continuous professional development, known as Higher Diploma Program (HDP). …”
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A double broad learning approach based on variational modal decomposition for Lithium-Ion battery prognostics by Xiaojia Wang, Xinyue Guo, Sheng Xu, Xibin Zhao

“…Therefore, in this paper, a novel model based on variational modal decomposition and double broad learning (VMD-DBL) is proposed. First, we use VMD to perform adaptive decomposition of the degraded data to form intrinsic mode function (IMF) components and residual components to solve the data noise problem. …”
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A Novel Dynamic Lane-Changing Trajectory Planning Model for Automated Vehicles Based on Reinforcement Learning by Cenxin Yu, Anning Ni, Jing Luo, Jinghui Wang, Chunqin Zhang, Qinqin Chen, Yifeng Tu

“…To optimize its performance, a reward function is properly designed by combining safety, efficiency, gap, headway, and comfort features. …”
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Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times by Mikel Barrena-Herrán, Itziar Modrego-Monforte, Olatz Grijalba

“…This research contributes to the growing body of knowledge on machine learning (ML) methods for spatiotemporal modeling, laying the groundwork for future studies that can further explore the complexity of urban phenomena.…”
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Predicting filtration coefficient and formation damage coefficient for particle flow in porous media using machine learning by Xuejia Du, George K. Wong

“…This study employs machine learning techniques to predict λ and β using experimental data from open literature. …”
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Continuous professional development and instructors’ beliefs about and practice of active learning method in Bahir Dar university by Mulugeta Teka Kahsay

“… The objective of this study was to investigate whether there is an overall difference in instructors’ beliefs about and practice of active learning methods as a function of the ongoing continuous professional development, known as Higher Diploma Program (HDP). …”
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Attention mechanism based CNN-LSTM hybrid deep learning model for atmospheric ozone concentration prediction by Jiang Yuan, Hua Dengxin, Wang Yufeng, Yang Xueting, Di Huige, Yan Qing

“…It features an attention-based CNN-LSTM hybrid deep learning model with specific settings: a time step of 5, a batch size of 25, 15 units in the LSTM layer, the ReLU activation function, 25 epochs, and an overfitting avoidance strategy with a dropout rate of 0.15. …”
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Descentralizar el aprendizaje: nuevos retos para la educación / Decentralizing learning: new challenges for education by Eleonora Badilla Saxe

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Integrated approach of extreme learning machines and locally weighted linear regression for improved discharge coefficient prediction by Mohammed Majeed Hameed, Mohamed Khalid Alomar, Siti Fatin Mohd Razali, Ali Salem

“…Therefore, this study uses Locally Weighted Linear Regression (LWLR) with radial basis kernel function instead of the linear system to effectively capture nonlinear relationships and enhance local data pattern recognition. …”
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Detecting deformation mechanisms of metals from acoustic emission signals through knowledge-driven unsupervised learning by Boyuan Gou, Yan Chen, Songhua Xu, Jun Sun, Turab Lookman, Ekhard K. H. Salje, Xiangdong Ding

“…To address this challenge, we propose a knowledge-driven unsupervised learning approach. The novel method leverages a family of gradient-driven supervised base learners and integrates them with a knowledge-infused aggregate loss function, effectively transforming the approach into an unsupervised learning framework. …”
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Combined exercise and Hawthorn protect against learning impairments and hippocampal damage from trimethyltin neurotoxicity in rat by Maryam Keshvari, Ali Heidarianpour, Farzaneh Chehelcheraghi

“…We examined the expression of BDNF/Trk-β proteins in the hippocampal tissue in order to confirm or disconfirm the neurogenesis-related learning and memory test. Following the increased of β-Amyloid plaques, acetylcholinesterase activity, inflammation, and decreased antioxidants levels in the hippocampus of neurotoxic rats, the BDNF/Trk-β proteins expression decreased and adverse tissue changes and a great disorder Neurogenesis-dependent spatial learning and memory were observed. 12-weeks of combined training with hawthorn, a good adjustment had in all measured functional and molecular variables. …”
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Machine Learning for Chronic Kidney Disease Detection from Planar and SPECT Scintigraphy: A Scoping Review by Dunja Vrbaški, Boban Vesin, Katerina Mangaroska

“…Machine learning (ML) holds promise for enhancing image analysis and supporting early CKD diagnosis. …”
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Adaptive continuous-discrete variables optimization for active learning with extremely sparse data in optical material design by Serang Jung, Eungkyu Lee

“…In the adaptive scheme, we examine the performance of three machine learning (ML) models—Gaussian process regression, factorization machines (FM), and field-aware FM—for a surrogate function, and ML model-specific optimization algorithms such as discrete particle swarm optimization, artificial bee colony optimization, and simulated annealing. …”
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Effect of rehabilitation training based on HT-CHC mode on cognitive ability of children with specific learning disabilities by LUO Dandan, SHEN Min

“…Results After the rehabilitation treatment， children with SLD showed significant improvement in sensory integration （vestibular sense， proprioception， and learning ability）， writing performance （neatness， pen grip engineering， and writing direction）， visual-perceptual function （visual discrimination， visual memory， spatial relationship， and visual completion）， rapid naming （rapid naming of numbers and pictures）， and attention compared with the pre-treatment period （all <i>P &lt; </i>0.05）. …”
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