Automated Symbolic Upscaling: 2. Model Generation for Extended Applicability Regimes by Kyle Pietrzyk, Ilenia Battiato

“…This strategy extends the applicability of homogenized models with respect to classical homogenization theory, as demonstrated in Part 1 where upscaled models are rigorously derived in moderately reactive physical regimes. After encoding the algorithm into Symbolica, an automated upscaling framework, we upscale two reactive mass transport problems and numerically validate the resulting nonlinear homogenized models by showing the absolute error estimates predicted by homogenization theory are satisfied. …”
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Intelligent scheduling mechanism of time-sensitive network modal in polymorphic network by Sijin YANG, Lei ZHUANG, Yu SONG, Jiaxing WANG, Xinyu YANG

“…For the problems of uncertain forwarding scheduling and long solving time of time-sensitive network modal in polymorphic network, a joint routing and scheduling mechanism of time-sensitive network modal based on CSQF was proposed.Considering the requirement of bounded delay, network state and different routing mechanisms, a hybrid resource scheduling problem of joint cache queue and routing was formulated to optimize the resource usage of the entire network.Then, the traffic characteristics and cache queue utilization was used to predict the cache utilization of the next cycle, which was based on deep reinforcement learning.In addition, by using multi-queue CSQF forwarding scheduling mechanism and explicit routing algorithm based on cache utilization, an iterative scheduling algorithm was proposed to achieve deterministic forwarding and resource allocation.Simulation results show that the mechanism can effectively adjust the transmission scheduling of deterministic applications according to the resource usage of the network, and has better schedulability compared with other off-line scheduling mechanisms.…”
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Intelligent scheduling mechanism of time-sensitive network modal in polymorphic network by Sijin YANG, Lei ZHUANG, Yu SONG, Jiaxing WANG, Xinyu YANG

“…For the problems of uncertain forwarding scheduling and long solving time of time-sensitive network modal in polymorphic network, a joint routing and scheduling mechanism of time-sensitive network modal based on CSQF was proposed.Considering the requirement of bounded delay, network state and different routing mechanisms, a hybrid resource scheduling problem of joint cache queue and routing was formulated to optimize the resource usage of the entire network.Then, the traffic characteristics and cache queue utilization was used to predict the cache utilization of the next cycle, which was based on deep reinforcement learning.In addition, by using multi-queue CSQF forwarding scheduling mechanism and explicit routing algorithm based on cache utilization, an iterative scheduling algorithm was proposed to achieve deterministic forwarding and resource allocation.Simulation results show that the mechanism can effectively adjust the transmission scheduling of deterministic applications according to the resource usage of the network, and has better schedulability compared with other off-line scheduling mechanisms.…”
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Fingernail analysis management system using microscopy sensor and blockchain technology by Shih Hsiung Lee, Chu Sing Yang

“…The performance of each feature extraction algorithm was analyzed for the two classifiers and the deep neural network algorithm was used comparatively. …”
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A Statistical Analysis Based Probabilistic Routing for Resource-Constrained Delay Tolerant Networks by Jixing Xu, Jianbo Li, Shan Jiang, Chenqu Dai, Lei You

“…In this paper, we propose an improved probabilistic routing algorithm that fully takes into account message's time-to-live when predicting the delivery probability. …”
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Probabilistic Solar Proxy Forecasting With Neural Network Ensembles by Joshua D. Daniell, Piyush M. Mehta

“…Currently, the USAF contracts Space Environment Technologies (SET), which uses a linear algorithm to forecast F10.7cm. In this work, we introduce methods using neural network ensembles with multi‐layer perceptrons (MLPs) and long‐short term memory (LSTMs) to improve on the SET predictions. …”
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Prognostic implications of the high sensitivity C-reactive protein to albumin ratio for major cardiovascular adverse events in patients undergoing noncardiac surgery by Yingjie Chen, Jingfang Lin, Dongxu Chen, Jundan Jiang, Yongxin Huang, Cansheng Gong, Yawen Xie, Xiaohui Chen, Ting Zheng, Yanling Tang, Xiaochun Zheng, Yanling Liao

“…The feature significance and predictive models were developed via the Boruta algorithm and machine learning techniques. …”
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Multiclass Classification of Solar Flares in Imbalanced Data Using Ensemble Learning and Sampling Methods by Haodi Jiang, Ryoma Matsuura, Jason T. L. Wang

“…Furthermore, we develop an ensemble algorithm that uses nine classifiers as base learners and logistic regression as meta-learner. …”
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Quantification of transplant-derived circulating cell-free DNA in absence of a donor genotype. by Eilon Sharon, Hao Shi, Sandhya Kharbanda, Winston Koh, Lance R Martin, Kiran K Khush, Hannah Valantine, Jonathan K Pritchard, Iwijn De Vlaminck

“…Our algorithm predicts heart and lung allograft rejection with an accuracy that is similar to conventional GTD. …”
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Yield Estimation in Banana Orchards Based on DeepSORT and RGB-Depth Images by Lei Zhou, Zhou Yang, Lanhui Fu, Jieli Duan

“…This system provides managers with bunch weight predictions and statistical plant information to achieve real-time yield estimations for banana orchards. …”
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An Improved Diagnosis Approach for Short-Circuit Fault Diagnosis in MPC-Based Current Source Inverter System by Jonggrist Jongudomkarn, Pirat Khunkitti, Apirat Siritaratiwat

“…In the presence of faulty switches, the algorithm identifies the phase of the reference current where the predicted currents align with the reference values. …”
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An integrated machine learning framework for developing and validating a prognostic risk model of gastric cancer based on endoplasmic reticulum stress-associated genes by Gang Wei, Yan Wang, Ru Liu, Lei Liu

“…This risk model proved to have a good predictive performance for estimating the overall survival of these patients. …”
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Machine learning and SHAP values explain the association between social determinants of health and post-stroke depression by Zhiwei Song, Jilin Weng, Yupeng Han, Wangyu Li, Yiya Xu, Yingchao He, Yinzhou Wang

“…Logistic regression was employed to analyse the association between SDoH and PSD, whereas Cox regression was utilized to assess the correlation between SDoH and all-cause mortality in PSD. The Boruta algorithm was employed for feature selection, and four machine learning models were constructed (CatBoost, Logistic, Multilayer Perceptron, and Random Forest) to evaluate the predictive effectiveness, calibration, and clinical applicability of these ML models. …”
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Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease by Hsin-Yueh Liang, Hsin-Yueh Liang, Kai-Cheng Hsu, Kai-Cheng Hsu, Kai-Cheng Hsu, Shang-Yu Chien, Chen-Yu Yeh, Ting-Hsuan Sun, Meng-Hsuan Liu, Kee Koon Ng

“…A convolutional recurrent neural network algorithm, integrating electrocardiographic (ECG) signals and features from ExECG reports, was developed to predict the risk of significant CAD. …”
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Developing Laterality-Specific Computable Phenotypes from Electronic Health Record Data, Employing Treatment-Warranted Diabetic Macular Edema as a Use Case by Kaili Ding, MBBS, MS, Tracy Z. Lang, BS, Roberta McKean-Cowdin, PhD, Hossein Ameri, MD, PhD, Narsing A. Rao, MD, Brian C. Toy, MD

“…Purpose: To develop a general algorithm employing structured and unstructured electronic health record (EHR) data to identify laterality-specific treatment-warranted disease more accurately at the longitudinal eye level. …”
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An indoor positioning method based on bluetooth array/PDR fusion using the SVD-EKF by Chenhui Li, Jie Zhen, Jianxin Wu

“…The dynamic test results in the room show that after SVD-EKF algorithm optimization, the positioning error is reduced by 0.05m, which is equivalent to the EKF algorithm. …”
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ABC and HML-methods application for determination and optimization of stock for electrical equipment accessory parts by A. V. Beloglazov, A. G. Rusina, O. V. Fomenko, D. A. Pekhota, V. A. Fyodorova

“…To describe the use of ABC and HML-methods for predicting the volume of emergency stock for main electrical equipment accessory parts. …”
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Optimal design of solar cells grid electrodes based on quadratic curves by Yongjiang Liu, Kai Li, Bo Zhou, Xiangquan Li, Peizheng Li

“…For 12-busbar architectures, more pronounced benefits were achieved, with efficiency gains reaching 4.71 %, 5.33 %, 5.87 % and remarkable volume reductions of 46.55 %, 49.42 %, 51.51 % across all cell sizes. …”
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Research and practice on self-organization architecture of smart pipe in mobile internet by Fei XUE, Yuhui XU, Liang ZHANG, Ying LI

“…According to the 3GPP specification,a new self-organization network of smart pipe in mobile internet was addressed.By introducing integrated policy control function,the external signals were supervised periodically,and the best optimized network strategies were predicted by the recurrent neural network algorithm,delivered by PCRF,and updated automatically with feedback of the regulations.This solution was effectively applied in scenarios such as speed limit of large traffic users,network protection under high load,and online advertisement recommendation etc.…”
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Strong verb lemmas from a corpus of old english. Advances and issues by Darío Metola Rodríguez

“…The conclusions of the article insist on the limits of automatic lemmatisation as well as the paths of refinement of the lemmatisation method in order to accomodate less predictable forms.…”
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