Machine learning tools for deciphering the regulatory logic of enhancers in health and disease by Spyros Foutadakis, Vasiliki Bourika, Ioanna Styliara, Panagiotis Koufargyris, Asimina Safarika, Eleni Karakike

“…We present the above computational tools and discuss their diverse applications towards cracking the enhancer regulatory code, which could have far-reaching ramifications for uncovering essential regulatory mechanisms and diagnosing and treating diseases. …”
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Methamphetamine-induced adaptation of learning rate dynamics depend on baseline performance by Hans Kirschner, Hanna M Molla, Matthew R Nassar, Harriet de Wit, Markus Ullsperger

“…These results present novel evidence for the involvement of catecholaminergic transmission on learning flexibility and highlights that baseline performance modulates the effect of the drug.…”
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System usability, stress and mood among teachers using distance learning by Maria Grazia MAGGIO, Amelia RIZZO, Maria Chiara STAGNITTI, Alfredo MANULI, Rocco Salvatore CALABRO

“…Introduction: Literature on teachers’ stress and depression over the past two years is vast, conversely, evidence regarding relationships between system usability and mood is relatively small. The present cross-sectional study aimed to explore system usability, stress, and mood among teachers using distance learning (DL). …”
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Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels by Chengce Yuan, Yimin Shi, Zhichen Ba, Daxin Liang, Jing Wang, Xiaorui Liu, Yabei Xu, Junreng Liu, Hongbo Xu

“…The escalating global climate crisis and energy challenges have made the development of efficient radiative cooling materials increasingly urgent. This study presents a machine-learning-based model for predicting the performance of radiative cooling aerogels (RCAs). …”
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Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide Glasses by Miruna-Ioana Belciu, Alin Velea

“…Chalcogenide glasses (ChGs) are a class of amorphous materials presenting remarkable mechanical, optical, and electrical properties, making them promising candidates for advanced photonic and optoelectronic applications. …”
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Science Through Machine Learning: Quantification of Post‐Storm Thermospheric Cooling by Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer, Douglas P. Drob, W. Kent Tobiska, Jean Yoshii

“…Abstract Machine learning (ML) models are universal function approximators and—if used correctly—can summarize the information content of observational data sets in a functional form for scientific and engineering applications. …”
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A high-performance tensor computing unit for deep learning acceleration by Qiang Zhou, Tieli Sun, Taoran Shen, York Xue

“…Our first-generation deep learning accelerator, tensor computing unit, was presented with hardware and software solutions. …”
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Effects of Cognitive-Behavioral Therapy on Academic Anxiety of Children with Learning Disorders by Hassan Shafaei

“…Objective: Reports showed that a cognitive-behavioral therapy effectively helps patients overcome a wide variety of maladaptive behaviors such as anxiety. The aim of the present study was to further examine this issue by exploring the effects of a cognitive-behavioral therapy on academic anxiety of children with learning disorders.Methods: The quasi-experimental research design was pretest-posttest with a control group. …”
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Lessons Learned from a Cooperative Box Experiment with Wild Baboons by William O’Hearn, Louis Frank, Stefanie Keupp, Julia Fischer

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Managing Timing Uncertainties in Worst-Case Design of Machine Learning Applications by Robin Hapka, Rolf Ernst

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Observing the observer (I): meta-bayesian models of learning and decision-making. by Jean Daunizeau, Hanneke E M den Ouden, Matthias Pessiglione, Stefan J Kiebel, Klaas E Stephan, Karl J Friston

“…In this paper, we present a generic approach that can be used to infer how subjects make optimal decisions under uncertainty. …”
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deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction by Justyna D. Kryś, Maksymilian Głowacki , Piotr Śmieja , Dominik Gront

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Predicting Atmospheric Dispersion of Industrial Chemicals Using Machine Learning Approaches by Maria Valle, Jairo A. Cardona, Cesar Viloria-Nunez, Christian G. Quintero M.

“…This study presents an intelligent framework for assessing atmospheric dispersion in industrial accident scenarios involving chemical substances. …”
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Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem. by Jun Yi Wang, Michael M Ngo, David Hessl, Randi J Hagerman, Susan M Rivera

“…However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. …”
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ENHANCING MEDIA AND INFORMATION LITERACY THROUGH EXPERIENTIAL GAME-BASED LEARNING by Huey Shyh Tan, Soon Hin Hew, Heidi Yeen Ju Tan

“…This study explores an innovative pedagogical approach by integrating Experiential Learning Theory (ELT) into non-digital game-based learning to bridge gaps in MIL knowledge and skills among Malaysian secondary and tertiary students, while also addressing the digital divide. …”
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A global community-based approach to supporting social and emotional learning by Jae H. Paik, Shinchieh Duh, Rita Rodriguez, Won Kyung Sung, Ji Young Ha, Lisa Wilken, Jong Tak Lee

“…We argue for the necessity of a global community-based approach to create a flexible SEL delivery model. We present two distinct SEL programs, bringing multiple international institutions together to provide enriching learning experiences for all. …”
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Evolutionary sparse learning reveals the shared genetic basis of convergent traits by John B. Allard, Sudip Sharma, Ravi Patel, Maxwell Sanderford, Koichiro Tamura, Slobodan Vucetic, Glenn S. Gerhard, Sudhir Kumar

“…These unmistakable examples of adaptive evolution offer opportunities to gain insight into their genetic origins and mechanisms through comparative analyses. Here, we present an approach to build genetic models that underlie the independent origins of convergent traits using evolutionary sparse learning with paired species contrast (ESL-PSC). …”
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Deep Learning-Based Draw-a-Person Intelligence Quotient Screening by Shafaat Hussain, Toqeer Ehsan, Hassan Alhuzali, Ali Al-Laith

“…To develop the artificial intelligence-based models, various deep learning algorithms were employed, including Convolutional Neural Network (CNN) and state-of-the-art CNN (Transfer Learning) models such as Mobile-Net, Xception, InceptionResNetV2, and InceptionV3. …”
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Secondary Frequency Control of Islanded Microgrid Based on Deep Reinforcement Learning by Li WANG, Yuxiang JIANG, Xiangjun ZENG, Bin ZHAO, Junhao LI

“…To address this, a secondary frequency control method based on deep reinforcement learning is proposed. The droop control characteristics of islanded microgrids are analyzed, and a secondary frequency controller structure based on deep Q-Networks is presented. …”
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Risk-Aware Reinforcement Learning Framework for User-Centric O-RAN by Shahrukh Khan Kasi, Fahd Ahmed Khan, Sabit Ekin, Ali Imran

“…The evolution of Open Radio Access Networks (O-RAN) presents an opportunity to enhance network performance by enabling dynamic orchestration of configuration and optimization parameters (COPs) through online learning methods. …”
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