Data-Driven Networked Optimal Iterative Learning Control for Discrete Linear Time-Varying Systems with One-Operation Bernoulli-Type Communication Delays by Yan Geng, Xiaoe Ruan, Hyo-Sung Ahn

“…This paper develops a type of data-driven networked optimal iterative learning control strategy for a class of discrete linear time-varying systems with one-operation Bernoulli-type communication delays. …”
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What can we learn from The Teahouse? Exploring the spoken grammar in a drama text as a resource for teaching spoken conversational Chinese by Jianfei Chen

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A Multi‐Omics, Machine Learning‐Aware, Genome‐Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction by Xinyu Bi, Yang Cheng, Xueqin Lv, Yanfeng Liu, Jianghua Li, Guocheng Du, Jian Chen, Long Liu

“…Abstract Given the extensive heterogeneity and variability, understanding cellular functions and regulatory mechanisms through the analysis of multi‐omics datasets becomes extremely challenging. …”
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Machine learning analysis of gene expression profiles of pyroptosis-related differentially expressed genes in ischemic stroke revealed potential targets for drug repurposing by Changchun Hei, Xiaowen Li, Ruochen Wang, Jiahui Peng, Ping Liu, Xialan Dong, P. Andy Li, Weifan Zheng, Jianguo Niu, Xiao Yang

“…Through this approach, a core set of 10 PRDEGs consistently emerged as significant across all three machine learning models. Subsequent analysis of these genes yielded significant insights into their functional relevance and potential therapeutic approaches. …”
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Schizophrenia detection from electroencephalogram signals using image encoding and wrapper-based deep feature selection approach by Utathya Aich, Arghyasree Saha, Marcin Woźniak, Muhammad Fazal Ijaz, Pawan Kumar Singh

“…Different machine learning and deep learning models have been used to improve the detection of schizophrenia. …”
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Unraveling the future of genomics: CRISPR, single-cell omics, and the applications in cancer and immunology by A. Vipin Menon, A. Vipin Menon, Bicna Song, Bicna Song, Lumen Chao, Lumen Chao, Diksha Sriram, Pamela Chansky, Pamela Chansky, Ishnoor Bakshi, Jane Ulianova, Wei Li, Wei Li

“…Its simplicity has facilitated large-scale experiments to assess gene functionality across diverse biological contexts, generating extensive datasets that boosted the development of computational methods and machine learning/artificial intelligence applications. …”
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Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking. by Mukuo Wang, Shujing Hou, Yu Wei, Dongmei Li, Jianping Lin

“…In the present study, we describe a multistage virtual screening approach that identifies dual adenosine A1 and A2A receptor antagonists using deep learning, pharmacophore models, and molecular docking methods. …”
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Coastal Urban Ecological Security Pattern Identification Integrating Land Subsidence Factors: A Deep Learning-Based Case Study of Zhuhai City by Yuan Shaoxiong, Gong Qinghua, Ye Yuyao, Wang Jun, Hao Yinlei, Zhang Yaze, Liu Bowen

“…This study makes three key contributions: (1) it establishes a novel methodological framework for incorporating vertical geological factors into ESP construction, addressing a significant gap in traditional approaches; (2) it quantitatively reveals the spatial heterogeneity of land subsidence impacts on different functional ecological zones, providing evidence-based guidance for targeted management; and (3) it demonstrates the effectiveness of deep learning and multisource data fusion techniques in complex ecological-geological system modeling. …”
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The Perils of Perfection: Navigating the Ripple Effects of Organizational Perfectionism on Employee Misbehavior through Job Insecurity and the Buffering Role of AI Learning Self-Ef... by Byung-Jik Kim, Hyun-Joo Oh, Min-Jik Kim, Dong-gwi Lee

“…Moreover, AI learning self-efficacy functions as a moderator in the OPP–job insecurity link, such that the positive link is weaker for members with higher levels of AI learning self-efficacy. …”
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The Effect of Folic Acid on Learning and Spa-tial Memory in Electrical Leisions Model of Nu-cleus Basalis Magnocellularis: Animal Model of Alzheimer’s Disease by Nastaran Zamani

“…Taking into account the role of folic acid on improving cognitive functions in some disease models, the present study aimed to examine the effect of folic acid on spatial learning and memory impairment induced by bilateral electrical lesion of nucleus basalis magnocellularis (NBM) in Alzheimer’s disease model of adult male rats. …”
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Applying Deep Learning Methods for a Large-Scale Riparian Vegetation Classification from High-Resolution Multimodal Aerial Remote Sensing Data by Marcel Reinhardt, Edvinas Rommel, Maike Heuner, Björn Baschek

“…The unique vegetation in riparian zones is fundamental for various ecological and socio-economic functions in these transitional areas. Sustainable management requires detailed spatial information about the occurring flora. …”
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Emerging Frontiers in Robotic Upper-Limb Prostheses: Mechanisms, Materials, Tactile Sensors and Machine Learning-Based EMG Control: A Comprehensive Review by Beibit Abdikenov, Darkhan Zholtayev, Kanat Suleimenov, Nazgul Assan, Kassymbek Ozhikenov, Aiman Ozhikenova, Nurbek Nadirov, Akim Kapsalyamov

“…Robotic prostheses offer a promising solution by mimicking many of the functions of a natural arm, leading to an increasing need for advanced prosthetic designs. …”
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CHILDREN’S GIFTEDNESS AND ASSOCIATED PROBLEMS. THE TWICE EXCEPTIONALITY PHENOMENON. GIFTEDNESS AND LEARNING PROBLEMS. GIFTEDNESS AND ATTENTION DEFICIT/ HYPERACTIVITY DISORDER (А RE... by O. A. Pylaeva

“…According to the available data, approximately 3–5 % of children fall into a category of intellectually gifted ones; some children (2–5 to 20 % or more of all gifted children according to different findings) may have learning problems. The terms “twice-gifted” or “twice-exceptional” are proposed to characterize children with giftedness concurrent with learning problems, attention deficit (including ADHD), or other impairments of cognitive functions and behavior (including oppositional disorder and obsessive-compulsive disorder). …”
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HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning by Jinchen Sun, Haoran Zheng

“…Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. …”
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Integrated transcriptomic analysis of COVID-19 stages and recovery: insights into key gene signatures, immune features, and diagnostic biomarkers through machine learning by Zhiyuan Gong, He An

“…Differential expression analysis, immune cell infiltration estimation, machine learning (LASSO regression and random forest), and functional enrichment were used to identify key genes and molecular mechanisms.ResultsGene expression analysis revealed dynamic changes during COVID-19 progression. …”
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Facilitating real-time LED-based photoacoustic imaging with DenP2P: An optimized conditional generative adversarial deep learning solution by Avijit Paul, Srivalleesha Mallidi

“…Signal quality can be improved by traditional noise removal algorithms, but deep learning models outperform non-learning methods. Although the most widely used U-Net architecture removes noise, it compromises the structural similarity, introduces blur, and causes edge artifacts. …”
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Multi-task deep learning for simultaneous prediction of steel purity and carbon capture rate using membrane separation technology in integrated steelmaking processes by Somboon Sukpancharoen, Pakon Sakdee, Natacha Phetyim, Rinlada Sirisangsawang, Chayut Sungsook

“…The MTL architecture employed shared hidden layers with task-specific output branches, utilizing ReLU activation functions and Adam optimization. STL achieved 97.62% accuracy with perfect recall for iron purity classification, while MTL demonstrated superior carbon capture prediction (R2 = 0.9948 vs 0.9902), representing 30% improvement through shared process learning. …”
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Chaotic Mountain Gazelle Optimizer Improved by Multiple Oppositional-Based Learning Variants for Theoretical Thermal Design Optimization of Heat Exchangers Using Nanofluids by Oguz Emrah Turgut, Mustafa Asker, Hayrullah Bilgeran Yesiloz, Hadi Genceli, Mohammad AL-Rawi

“…This theoretical research study proposes a novel hybrid algorithm that integrates an improved quasi-dynamical oppositional learning mutation scheme into the Mountain Gazelle Optimization method, augmented with chaotic sequences, for the thermal and economical design of a shell-and-tube heat exchanger operating with nanofluids. …”
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Optimizing HVAC energy efficiency in low-energy buildings: a comparative analysis of reinforcement learning control strategies under Tehran climate conditions by Mohammad Anvar Adibhesami, Amir Hassanzadeh

“…Model B consisted of a deep convolutional network architecture with 256 neurons in each hidden layer, employing rectified linear units as activation functions and the Adam optimizer at a learning rate of 0.001. …”
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Una universidad que aprende: formación y evaluación docente en red / A university that learns: higher teacher education and evaluation network by Eleonora Badilla Saxe, Silvia Chacón Ramírez, Susan Francis Salazar

“…Why educate faculty at UCR? Amongst RIFED´s learning path some of its achievements are highlighted: a re-conceptualization of its functions as well as of the terms education and evaluation. …”
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