Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs by Samir Abdaljalil, Hasan Kurban, Rachad Atat, Erchin Serpedin, Khalid Qaraqe

“…Our method successfully captures temporal patterns, making it robust against subtle anomalies and structural changes. …”
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Acute health effects of ambient air pollution including ultrafine particles in a semi-experimental setting in young, healthy individuals by Elisabeth Folwarczny, Felix Forster, Rudolf A. Jörres, Stefan Rakete, Sheng Ye, Mark Wenig, Nadine Gawlitta, Jürgen Schnelle-Kreis, Richard Winterhalter, Alexander Müller, Dennis Nowak, Stefan Karrasch

“…Results The sites showed the planned pattern of exposure levels but with large overlap. …”
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Identification of age-specific risk factors for hyperuricemia: a machine learning-driven stratified analysis in health examination cohorts by ChuXia Tan, Yuan Liu, Lijun Li, Ying Li, Pingting Yang, Yinglong Duan, Xingxing Wang, Huiyi Zhang, Jingying Wang, Honglian Zhang

“…Results The overall detection rate of HUA was 22.8%, and the level of serum uric acid increased significantly with age. …”
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Hymecromone Administration in Real Clinical Practice: Results of the Prospective Multicentre Observational Study in the Republic of Kazakhstan by A. V. Nersesov, D. A. Kaybullaeva, V. S. Rakhmetova, I. A. Lozinskaya, A. K. Kurmangalieva, V. S. Ayupova, A. B. Orazbaeva, A. K. Kanabaeva, Yu. O. Shulpekova

“…A visual analogue scale and RAPID questionnaire were used to characterise biliary pain; severity of associated symptoms, bowel habit and a quality of life according the SF-12 were also assessed. A rate of a ≥50 % reduction in symptom severity was used as a primary efficacy criterion; a rate of a ≥10 improvement in the SF-12 quality of life score was used as a secondary efficacy criterion. …”
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CRYPTOCURRENCY TIME SERIES FORECASTING MODEL USING GRU ALGORITHM BASED ON MACHINE LEARNING by Melina Melina, Sukono Sukono, Herlina Napitupulu, Norizan Mohamed, Yulison Herry Chrisnanto, Asep ID Hadiana, Valentina Adimurti Kusumaningtyas

“…The high fluctuation and volatility of cryptocurrency prices and the complexity of non-linear relationships in data patterns attract investors and researchers who want to develop accurate cryptocurrency price forecasting models. …”
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Full-Color LCD Microdisplay System Based on OLED Backlight Unit and Field-Sequential Color Driving Method by Sungho Woo, Dae-Hwan Kim, Yoon Soo Han, Byeong-Dae Choi

“…The field-sequential driving frequency was set to 255 Hz to allow each of the RGB colors to be generated without color mixing at the given display frame rate. A prototype FSC LCD microdisplay system consisting of a 0.7′′ LCD microdisplay panel and the 0.76′′ OLED BLU successfully exhibited color display and moving picture images using the FSC driving method.…”
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Development of oculomotor digital biomarkers using clinical examinations as “Ground Truth” by Brittany Trotter, Melissa Hunfalvay, Melissa Hunfalvay, Nicholas P. Murray, Greg C. Mathews, Frederick Robert Carrick, Frederick Robert Carrick, Frederick Robert Carrick, Frederick Robert Carrick

“…A board-certified neurologist with 16 years of experience also conducted an oculomotor examination to mirror eye movement patterns.ResultsData analysis included a series of single-block logistic regressions to examine the scoring of the six eye-tracking tests (RightEye, LLC) to predict clinician-rated eye movement classifications (i.e., normal or abnormal). …”
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Scalability analysis of heavy-duty gas turbines using data-driven machine learning by Shubhasmita Pati, Julian D. Osorio, Mayank Panwar, Rob Hovsapian

“…The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). …”
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Consensus-Driven Development of an Exercise Base Manual Programme for Prediabetic Patients: A Delphi Study by Hafeez S, Shakil-Ur-Rehman S, Riaz S, Hafeez S, Hafeez JS, Mumtaz H

“…Initial recommendations were rated by panalists through a 5-point Likert scale. …”
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D4Care: A Deep Dynamic Memory-Driven Cross-Modal Feature Representation Network for Clinical Outcome Prediction by Binyue Chen, Guohua Liu

“…In addition, we also introduce a memory-aware constrained layer normalization to alleviate the challenges of multi-modal feature heterogeneity. Besides, we use gating mechanisms and dynamic memory components to enable the model to learn feature information of different historical-current patterns, further improving the model’s performance. …”
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A Single RF MIMO Loading Network for High-Order Modulation Schemes by Bo Han, Vlasis I. Barousis, Antonis Kalis, Constantinos B. Papadias, Athanasios G. Kanatas, Ramjee Prasad

“…MIMO transmission in this case is achieved by shaping directly the radiation pattern with the aid of analog tunable loads attached to the parasitics. …”
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Predicting hydrocarbon reservoir quality in deepwater sedimentary systems using sequential deep learning techniques by Xiao Hu, Jun Xie, Xiwei Li, Junzheng Han, Zhengquan Zhao, Hamzeh Ghorbani

“…Three sequential deep learning models—Recurrent Neural Network and Gated Recurrent Unit—were developed and optimized using the Adam algorithm. …”
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Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network by Li-li Li, Kun Chen, Jian-min Gao, Hui Li

“…PNN recognized successfully both single pattern and mixed pattern of control charts because of its simple network structure and excellent recognition effect. …”
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Prediction of litchi flower induction in South China region based on the CMIP6 climate model by HOU Wei, ZHANG Liuhong, ZHANG Lei, LUAN Lan, ZHANG Mingjie, WANG Xiuzhen, ZHANG Hui

“…We then constructed a florescence climate index model, correlating it with the litchi flower formation rate for both early-maturing and medium-late maturing varieties. …”
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Experimental and Theoretical Research on the Stress-Relaxation Behaviors of PTFE Coated Fabrics under Different Temperatures by Yingying Zhang, Shanshan Xu, Qilin Zhang, Yi Zhou

“…With temperature increasing, the decrease rate of membrane stress decreases and the final stable value increases. …”
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New parallel processing strategies in complex event processing systems with data streams by Fuyuan Xiao, Cheng Zhan, Hong Lai, Li Tao, Zhiguo Qu

“…Finally, the application of our work is demonstrated using experiments on the StreamBase system regardless of the increased input rate of the stream or the increased time window size of the operator.…”
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The Deformation, Failure Mechanisms, and Stability Control of the Surrounding Rock of Deep Cross-Measure Roadway: A Case Study by Wenzheng Du, Rui Pan, Hongjin Sun

“…On this basis, two types of evaluation indicators, that is, the deformations of the surrounding rock and the plastic ranges in the four typical parts (roof, shoulder, floor, and sidewalls) of the roadway, were selected to rate the stability of the roadway under different working conditions. …”
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MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition by Kowovi Comivi Alowonou, Ji-Hyeong Han

“…On the other hand, the TMST module integrates a Gated Multi-stage Temporal Convolution (GMSTC) with a Temporal Multi-Head Self-Attention (TMHSA) to capture global temporal features and accommodate both long-term and short-term dependencies within action sequences. …”
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GT-SRR: A Structured Method for Social Relation Recognition with GGNN-Based Transformer by Dejiao Huang, Menglei Xia, Ruyi Chang, Xiaohan Kong, Shuai Guo

“…In order to overcome these restrictions, this essay suggests a SRR model that integrates Gated Graph Neural Network (GGNN) and Transformer. …”
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Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models by Ningchang Kang, Zhaocai Wang, Anbin Zhang, Hang Chen

“…These components are then modeled independently: long short-term memory (LSTM) and convolutional neural networks (CNN) handle trend and seasonal patterns, while gated recurrent units (GRU) and Transformer process residual fluctuations. …”
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