Low-frequency spectral graph convolution networks with one-hop connections information for personalized tag recommendation by Zhengshun Fei, Haotian Zhou, Jinglong Wang, Gui Chen, Xinjian Xiang

“…Lastly, we analyze the impact of different internal components, pooling methods, parameter choices, and prediction approaches of LSGCNT on recommendation performance. …”
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Familial Hemiplegic Migraine with Severe Attacks: A New Report with ATP1A2 Mutation by E. Martínez, R. Moreno, L. López-Mesonero, I. Vidriales, M. Ruiz, A. L. Guerrero, J. J. Tellería

“…We identified a variant in heterozygous state in ATP1A2 gene (p.Thr364Met), pathogenic according to different prediction algorithms (SIFT, PolyPhen2, MutationTaster, and Condel). …”
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Machine learning-assisted design of Ti–V–Nb–Mo refractory high-entropy alloys with higher ductility and specific yield strength by Yan Li, Junjie Gong, Shilong Liang, Wei Wu, Yongxin Wang, Zheng Chen

“…The predicted results are in general agreement with the trends of the experimental data. …”
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Spatial Downscaling of Daily Temperature Minima Using Machine Learning Methods and Application to Frost Forecasting in Two Alpine Valleys by Sudheer Bhakare, Michael Matiu, Alice Crespi, Dino Zardi

“…The CNN model also exhibits superior performance in frost prediction, with the highest F1 score (0.78) and lowest False Discovery Rate (0.30) in predicting frost events, particularly in early spring for the short-term forecast period over 2010–2018. …”
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Synchronization frequency analysis and stochastic simulation of multi-site flood flows based on the complicated vine copula structure by X. Yu, Y.-P. Xu, Y. Guo, S. Chen, H. Gu

“…<p>Accurately modeling and predicting flood flows across multiple sites within a watershed presents significant challenges due to potential issues of insufficient accuracy and excessive computational demands in existing methodologies. …”
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Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's disease by Yi-Long Huang, Tsung-Hsien Tsai, Zhao-Qing Shen, Yun-Hsuan Chan, Chih-Wei Tu, Chien-Yi Tung, Pei-Ning Wang, Ting-Fen Tsai

“…Statistical DESeq2 analysis and machine learning methods were employed to identify differentially expressed genes (DEGs) and develop prediction models. Results We discovered a remarkable gender-specific difference in DEGs that distinguish P-MCI from S-MCI. …”
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Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. by Rabia Asghar, Sanjay Kumar, Arslan Shaukat, Paul Hynds

“…Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. …”
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Molecular monitoring of short- and long-term transcriptional effects of hair growth stimulating agents. by Sabrina K Henne, Lara M Hochfeld, Werner Bartmann, Thomas Welss, Markus M Nöthen, Stefanie Heilmann-Heimbach

“…Combined with genetic profiling, this approach may enable personalized prediction of treatment efficacy and compliance.…”
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Renal Dysfunction, Metabolic Syndrome and Cardiovascular Disease Mortality by David Martins, Chizobam Ani, Deyu Pan, Omolola Ogunyemi, Keith Norris

“…Few studies have explored the predictive utility of co-clustering CKD with MetS for cardiovascular disease (CVD) mortality. …”
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New insight into the controlling factors of differences in carbonate buried-hills driven by tectonic activity: a case study of the Weixinan Sag, Beibuwan Basin, China by Yanying Li, Kongyou Wu, Jia Zhong, Yannan Du, Jinyou Wang

“…This study provides significant guidance for the development and reservoir prediction for similarly carbonate buried karst hills.…”
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Simulation Effect Evaluation of CMIP6 Models on Climatic Elements in Huai River Basin by WU Chen, WANG Jingcai, SHAO Junbo, LI Xiangyu, WANG Wenyue

“…The general climate model (CMIP6) is the main means of large-scale simulation and prediction of future climate changes,but the simulation quality of its model data and applicability in different research regions need to be evaluated.Based on the measured data of 19 meteorological stations in the upper and middle reaches of the Huai River Basin from 1960 to 2014,this paper systematically evaluates the simulated climate data of 19 CMIP6 models using five precision indicators.The indicators include equidistant cumulative distribution function method,mean,dispersion coefficient (C_v),Pearson correlation coefficient (PCC),standard deviation (STD),and root-mean-square deviation (RMSE).Finally,the following conclusions are drawn:① Based on the simulation data of 19 CMIP6 models corrected for bias,the top two climate models for precipitation data simulation are preliminarily selected,including ACCESS-ESM1-5 and CMCC-ESM2.The top five climate models for simulating temperature data contain ACCESS-ESM1-5 and CMCC-ESM2;② The historical period data and measured historical climate data of the selected CMIP6 models are evaluated from two aspects of annual change trend and spatial distribution.Then,the CMIP6 model suitable for the upper and middle reaches of the Huai River Basin is verified and selected;③ Based on analyzing the influence of atmospheric circulation factors on climate,AO has a good correlation with the climate data of the upper and middle reaches of the studied basin,and the correlation between the model and atmospheric circulation data is close to the actual situation.The comprehensive evaluation shows that the most suitable models for simulating the upper and middle reaches of the Huai River Basin are ACCESS-ESM1-5 and CMCC-ESM2.The systematic evaluation of CMIP6 model data simulating the upper and middle reaches of the Huai River Basin provides theoretical and technical references for the rational selection and utilization of CMIP6 datasets for the research on climate change factors in future scenarios of the basin.…”
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Temperature dependence of femtosecond photoacoustic process in high-precision characterization for metal nanofilms by Zhongyu Wang, Jing Min, Yong Sun, Xuesong Wang, Xiuguo Chen, Zirong Tang, Shiyuan Liu

“…Compared to experimental results, our dynamic models significantly improved prediction accuracy for both copper and AlCu nanofilms. …”
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Non-apical plateau potentials and persistent firing induced by metabotropic cholinergic modulation in layer 2/3 pyramidal cells in the rat prefrontal cortex. by Nicholas Hagger-Vaughan, Daniel Kolnier, Johan F Storm

“…These L2/3 non-apical plateau potentials may be involved in prefrontal functions, such as access consciousness, working memory, and executive functions such as planning, decision-making, and outcome prediction.…”
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Electrochemical sensors for the detection of immune checkpoint related proteins and their role in cancer companion diagnostics by Louise Barnaby, Andrew G. Watts, Pedro Estrela

“…This review investigates the potential biomarkers relevant to predicting ICI success, as well as the current electrochemical sensors that have been developed to determine the expression levels of these proteins.…”
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ESD-YOLOv8: An Efficient Solar Cell Fault Detection Model Based on YOLOv8 by Lingyun Zhang, Xu Wu, Zihan Liu, Panlin Yu, Mingfen Yang

“…The Unified IoU (UIoU) metric is employed to optimise the loss function and enhance the accuracy of fault prediction. The results of the performance test demonstrate that the F1 Score of ESD-YOLOv8 in mAP@0.5 reaches 91.8% and mAP@0.5:0.95 reaches 58.0%. …”
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Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble by Xianglong Zhu, Ming Meng, Zewen Yan, Zhizeng Luo

“…Finally, we employ a stacking ensemble approach, where the prediction results of base classifiers corresponding to different domain features and the set of significant features undergo linear discriminant analysis for dimensionality reduction, yielding discriminative feature integration as input for the meta-classifier for classification. …”
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Adversarial examples defense method based on multi-dimensional feature maps knowledge distillation by Baolin QIU, Ping YI

“…The neural network approach has been commonly used in computer vision tasks.However, adversarial examples are able to make a neural network generate a false prediction.Adversarial training has been shown to be an effective approach to defend against the impact of adversarial examples.Nevertheless, it requires high computing power and long training time thus limiting its application scenarios.An adversarial examples defense method based on knowledge distillation was proposed, reusing the defense experience from the large datasets to new classification tasks.During distillation, teacher model has the same structure as student model and the feature map vector was used to transfer experience, and clean samples were used for training.Multi-dimensional feature maps were utilized to enhance the semantic information.Furthermore, an attention mechanism based on feature map was proposed, which boosted the effect of distillation by assigning weights to features according to their importance.Experiments were conducted over cifar100 and cifar10 open-source dataset.And various white-box attack algorithms such as FGSM (fast gradient sign method), PGD (project gradient descent) and C＆amp;W (Carlini-Wagner attack) were applied to test the experimental results.The accuracy of the proposed method on Cifar10 clean samples exceeds that of adversarial training and is close to the accuracy of the model trained on clean samples.Under the PGD attack of L2 distance, the efficiency of the proposed method is close to that of adversarial training, which is significantly higher than that of normal training.Moreover, the proposed method is a light-weight adversarial defense method with low learning cost.The computing power requirement is far less than that of adversarial training even if optimization schemes such as attention mechanism and multi-dimensional feature map are added.Knowledge distillation can learn the decision-making experience of normal samples and extract robust features as a neural network learning scheme.It uses a small amount of data to generate accurate and robust models, improves generalization, and reduces the cost of adversarial training.…”
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Comparison of accuracy in freehand versus computer-assisted (dynamic and static) dental implant placement: A systematic review and meta-analysis by Saurav Banerjee, Anasua Debnath, Priyanjali Paul, Tridib Nath Banerjee

“…Freehand techniques demonstrated the highest deviations, with platform deviations up to 3.48 mm and angular deviations up to 10.09°. Prediction intervals indicated consistent superiority of dynamic guidance across metrics. …”
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Modeling Radiation Belt Electrons With Information Theory Informed Neural Networks by Simon Wing, Drew L. Turner, Aleksandr Y. Ukhorskiy, Jay R. Johnson, Thomas Sotirelis, Romina Nikoukar, Giuseppe Romeo

“…Based on the test set, the model prediction efficiency (PE) increases with increasing L*, ranging from −0.043 at L* = 3 to 0.76 at L* = 6.5. …”
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Membrane-embedded CdaA is required for efficient synthesis of second messenger cyclic di-AMP by Alexander J. Foster, Haoyang Li, Panagiotis Drougkas, Gea. K. Schuurman-Wolters, Joeri ten Kate, Cristina Paulino, Bert Poolman

“…Cryo-EM and in-silico structure prediction of CdaA show that the two DAC dimers engage in a head-to-head interaction, leading to cyclic-di-AMP formation. …”
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