Neurocognitive abilities in individuals with Down syndrome-a narrative review by Sidra Kaleem Jafri, Karen Elizabeth Harman

“…Despite the variability in expression, there is a distinct developmental phenotype characterized by deficits in learning/memory, executive functions, and language skills accompanying the psychomotor delay. …”
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Prosumer Cost Efficiency and Ensuring Grid Stability Through a Hierarchical PPO Framework in Decentralized Community Energy Management by Ajay Singh, Bijaya Ketan Panigrahi

“…This research introduces a methodology based on reinforcement learning to address the cost reduction challenges faced by prosumers in a smart home-based community. …”
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Leveraging logit uncertainty for better knowledge distillation by Zhen Guo, Dong Wang, Qiang He, Pengzhou Zhang

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Integration Processes in the System of Lifelong Education at the Comrat State University by Serghei Zaharia, Oxana CURTEVA

“…Realizing the idea of lifelong learning, the Center for Continuing Education has been successfully functioning at the Comrat State University. …”
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How Mobile Solutions Impact the Education in Universities by Rares-Constantin CIOBANU

“…This paper seeks to explore the range of existing mobile learning applications, focusing on their functionalities and user experience. …”
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CHILDREN LANGUAGE ACQUISITION PROCESS by Loli Safitri

“…After the data had been collected, the researcher finds out that Azka was 18 months old baby who was in holophrastic functions: the one-word utterances stage of language development. …”
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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study by Ziyao Meng, MEng, Zhouyu Guan, MD, Shujie Yu, MBBS, Yilan Wu, BSc, Yaoning Zhao, BSc, Jie Shen, PhD, Cynthia Ciwei Lim, MMed, Tingli Chen, MD, Dawei Yang, PhD, An Ran Ran, PhD, Feng He, MSc, Haslina Hamzah, BSc, Sarkaaj Singh, MSc, Anis Syazwani Abd Raof, MSc, Jian Wen Samuel Lee-Boey, MBBS, ProfMBBS Soo-Kun Lim, MBBS, ProfMD Xufang Sun, MBBS, Shuwang Ge, MD, ProfMD Gang Xu, MD, Prof Hua Su, MD, Yang Cheng, MD, Feng Lu, PhD, ProfPhD Xiaofei Liao, PhD, ProfPhD Hai Jin, PhD, Chenxin Deng, MMed, Lei Ruan, MD, ProfMD Cuntai Zhang, MD, Chan Wu, MD, ProfMD Rongping Dai, MD, Yixiao Jin, MSc, Wenxiao Wang, PhD, Tingyao Li, BSc, Ruhan Liu, PhD, Jiajia Li, PhD, Jia Shu, BEng, Yuwei Lu, MBBS, Xiangning Wang, MD, Qiang Wu, MD, Yiming Qin, BEng, Jin Tang, MD, Xiaohua Sheng, MD, Qiong Jiao, MD, ProfPhD Xiaokang Yang, MD, ProfPhD Minyi Guo, MD, Gareth J McKay, PhD, ProfPhD Ruth E Hogg, PhD, Gerald Liew, MBBS, Evelyn Yi Lyn Chee, PhD, ProfPhD Wynne Hsu, PhD, ProfPhD Mong Li Lee, PhD, Simon Szeto, MBChB, ProfMD Andrea O Y Luk, MBChB, ProfMD Juliana C N Chan, MBChB, Carol Y Cheung, PhD, Gavin Siew Wei Tan, FRCSEd, Yih-Chung Tham, PhD, ProfMD PhD Ching-Yu Cheng, PhD, Charumathi Sabanayagam, MD PhD, ProfPhD Lee-Ling Lim, MD PhD, ProfMD PhD Weiping Jia, MD PhD, ProfMD PhD Huating Li, MD PhD, Bin Sheng, Prof, Tien Yin Wong, Prof

“…We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. …”
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ACGNet: An Alternating Conjugate Gradient Optimization-Based Neural Network for SAR Image Despeckling by Xin Mao, Ying Liu, Chenghao Qiu, Cong Lin

“…Synthetic aperture radar (SAR) images are characterized by unique speckle noise, and maintaining image details while effectively reducing this noise has always been a challenging problem. Current deep-learning-based denoising methods mainly rely on global noise models or local feature learning, but these methods often fail to achieve a balance between noise suppression and detail preservation. …”
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High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI by Qinyang Shou, Chenyang Zhao, Xingfeng Shao, Megan M Herting, Danny JJ Wang

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Integrated Transcriptomic and Machine Learning Analysis Identifies EAF2 as a Diagnostic Biomarker and Key Pathogenic Factor in Parkinson’s Disease by Peng H, Cheng Y, Chen Q, Qin L

“…Additionally, RT-qPCR experiments were conducted to validate our findings in clinical specimens. Functional enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed to explore the functional and pathway mechanisms of the identified genes in PD. …”
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A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway by Jun-luan Mo, Xi Li, Lin Lei, Ji Peng, Xiong-shun Liang, Hong-hao Zhou, Zhao-qian Liu, Wen-xu Hong, Ji-ye Yin

“…We recruited new research subjects as validation samples and used PCR-based quantitative detection to validate the key small RNAs screened by the machine learning model. Finally, target gene prediction and functional enrichment analysis were performed on these key RNAs. …”
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A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine by S. Syama, J. Ramprabhakar, R Anand, V. P. Meena, Josep M. Guerrero

“…Then, a unique swarm intelligence technique, the non-linear dimension learning Hunting Whale Optimization Algorithm (NDLHWOA), is devised to optimize regularized extreme learning machine model parameters to capture the implicit information of each reconstructed sub-series. …”
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ATP6AP1 drives pyroptosis-mediated immune evasion in hepatocellular carcinoma: a machine learning-guided therapeutic target by Lei Tang, Xiyue Wang, Zhengzheng Xia, Jiayu Yan, Shanshan Lin

“…Methods We integrated large-scale datasets from TCGA and GEO databases to identify core modules by weighted gene co-expression network analysis (WGCNA), while mutation profiling and survival analysis verified clinical relevance. Multiple machine learning techniques, including GBM (gradient boosting machine), XGBoost (extreme gradient boosting machine), SVM (support vector machine), LASSO (least absolute shrinkage and selection operator) and random forest, as well as functional analysis, were used to systematically investigate the role of ATP6AP1 in HCC. …”
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Integrated multi-omics and machine learning reveals immune-metabolic signatures in osteoarthritis: from bulk RNA-seq to single-cell resolution by Hui He, Xiumei Zhao, Bo Zhang, Shijian Zhao, Yinteng Wu

“…Subsequently, Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules associated with OA and immune-metabolism scores, followed by enrichment analysis to reveal the functional characteristics of these modules. To identify immune-metabolism related differentially expressed genes (DEGs), we utilized seven machine learning methods, including lasso regression, random forest, bagging, gradient boosting machines (GBM), Xgboost-xgbLinear, Xgboost-xgbtree, and decision trees, to construct predictive models and validate their reliability. …”
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Development of a Novel Microphysiological System for Peripheral Neurotoxicity Prediction Using Human iPSC-Derived Neurons with Morphological Deep Learning by Xiaobo Han, Naoki Matsuda, Makoto Yamanaka, Ikuro Suzuki

“…A microphysiological system (MPS) is an in vitro culture technology that reproduces the physiological microenvironment and functionality of humans and is expected to be applied for drug screening. …”
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Land-Cover Semantic Segmentation for Very-High-Resolution Remote Sensing Imagery Using Deep Transfer Learning and Active Contour Loss by Miguel Chicchon, Francisco James Leon Trujillo, Ivan Sipiran, Ricardo Madrid

“…We assessed the U-Net-scSE, FT-U-NetFormer, and DC-Swin architectures, incorporating transfer learning and active contour loss functions to improve performance on semantic segmentation tasks. …”
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UAV Path Planning Using a State Transition Simulated Annealing Algorithm Based on Integrated Destruction Operators and Backward Learning Strategies by Jianping Liu, Xiaoxia Han, Fengyi Liu, Jinde Wu, Wenjie Zhang

“…DRSTASA improves global search capabilities by initializing the population with Latin hypercube sampling, combined with destruction operators and backward learning strategies. Testing on 23 benchmark functions demonstrates that the algorithm outperforms both traditional and advanced metaheuristic algorithms in solving single and multimodal problems. …”
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Supervisors’ Challenges with Online Supervision Using Microsoft Teams in Supervising Open Distance and E-Learning (ODeL) Pre-Service Teachers by Matshidiso Taole, Katharine Naidu, Nduduzo Gcabashe, Thabisile Maphumulo

“…This study recommends that training be conducted for supervisors and pre-service teachers in using MS Teams and its various functions before supervision. Further research should explore pre-service teachers’ engagement with online supervision tools to inform the future practice of teacher education programmes. …”
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Data-Selective Learning Algorithm Using Resonance Parameters Based on Stacked Data Augmentation for Wideband Impedance Prediction of Printed Spiral Coils by Joojoong Kim, Eakhwan Song

“…In recent years, various fields have conducted extensive research on neural network learning to address the growing demand for miniaturization and multi-functionalization of wireless devices. …”
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Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals by Kun Chen, Meng Li, Xiaolian Li, Guangzhi Cui, Jia Tian, JiaLe Li, RuoYao Mu, JunJie Zhu

“…However, traditional methods for the automatic detection of microseismic events rely heavily on characteristic functions and human intervention, often resulting in suboptimal performance when dealing with complex and noisy data. …”
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