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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Pagnier, Laurent Hyett, Criston Ferrando, Robert Goldshtein, Igal Alisse, Jean Saban, Lilah Chertkov, Michael University of Arizona Graduate Interdisciplinary Program in Applied Mathematics and Department of Mathematics TucsonAZ85721 United States Noga the Israel Independent System Operator Haifa Israel

ISBN: (纸本)9798350316339

Faced with the complexities of managing natural gas-dependent power system amid the surge of renewable integration and load unpredictability, this study explores strategies for navigating emergency transitions to costlier secondary fuels. Our aim is to develop decision-support tools for operators during such exigencies. We approach the problem through a Markov Decision Process (MDP) framework, accounting for multiple uncertainties. These include the potential for dual-fuel generator failures and operator response during high-pressure situations. Additionally, we consider the finite reserves of primary fuel, governed by gas-flow partial differential equations (PDEs) and constrained by nodal pressure. Other factors include the variability in power forecasts due to renewable generation and the economic impact of compulsory load shedding. For tractability, we address the MDP in a simplified context, replacing it by Markov Processes evaluated against a selection of policies and scenarios for comparison. Our study considers two models for the natural gas system: an oversimplified model tracking linepack and a more nuanced model that accounts for gas flow network heterogeneity. The efficacy of our methods is demonstrated using a realistic model replicating Israel's power-gas infrastructure. © 2024 IEEE.

关键词： Markov processes

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Socially induced ovulation synchrony and its effect on seabird population dynamics

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Journal of Biological Dynamics 2011年第5期5卷 495-516页

作者： Henson, Shandelle M. Cushing, J.M. Hayward, James L. Department of Mathematics Andrews University Berrien Springs MI 49104 United States Department of Mathematics Interdisciplinary Program in Applied Mathematics University of Arizona Tucson AZ 85721 United States Department of Biology Andrews University Berrien Springs MI 49104 United States

Spontaneous oscillator synchrony is a form of self-organization in which populations of interacting oscillators ultimately cycle together. This phenomenon occurs in a wide range of physical and biological systems. In rats and humans, oestrous/menstrual cycles synchronize through social stimulation with pheromones acting as synchronizing signals. In previous work, we showed that glaucous-winged gulls (Larus glaucescens) can lay eggs synchronously on an every-other-day schedule, and that synchrony increases with colony density. We posed a discrete-time mathematical model for reproduction during the breeding season based on the hypothesis that pre-ovulatory luteinizing hormone surges synchronize by means of visual, auditory and/or olfactory cues. Here, we extend the seasonal model in order to investigate the effect of ovulation synchrony on population dynamics across reproductive seasons. We show that socially stimulated ovulation synchrony can enhance total population size and allow the population to persist at lower birth rates than would otherwise be possible. © 2011 Taylor & Francis.

关键词： Bifurcation Cannibalism Discrete-time model Gull colony Ovulation synchrony

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Machine learning from a continuous viewpoint,I

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Science China mathematics 2020年第11期63卷 2233-2266页

作者： Weinan E Chao Ma Lei Wu Department of Mathematics Princeton UniversityPrincetonNJ 08544USA The Program in Applied and Computational Mathematics Princeton UniversityPrincetonNJ 08544USA Beijing Institute of Big Data Research Beijing 100871China

We present a continuous formulation of machine learning,as a problem in the calculus of variations and differential-integral equations,in the spirit of classical numerical *** demonstrate that conventional machine learning models and algorithms,such as the random feature model,the two-layer neural network model and the residual neural network model,can all be recovered(in a scaled form)as particular discretizations of different continuous *** also present examples of new models,such as the flow-based random feature model,and new algorithms,such as the smoothed particle method and spectral method,that arise naturally from this continuous *** discuss how the issues of generalization error and implicit regularization can be studied under this framework.

关键词： machine learning continuous formulation flow-based model gradient flow particle approximation

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Microfluidic droplet detection using synthetic data generated by a Generative Adversarial Network

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Marziyeh Karimzadeh Mohsen Alambardar Meybodi Mohammadreza Shams Zahra Goli-Malekabadi Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan 81746-73441 Iran Department of Computer Engineering Shahreza Campus University of Isfahan Isfahan Iran Department of Medical Technology and Tissue Engineering School of Life science Engineering College of Interdisciplinary Science and Technology University of Tehran Tehran Iran

Cellular heterogeneity, even among genetically identical cells, results in variations in their properties and behaviors, making single-cell analysis crucial for obtaining detailed insights. However, isolating single cells from a cell population poses major challenges, as conventional laboratory techniques often risk cell damage and involve complex procedures. Droplet microfluidics has emerged as a promising approach for encapsulating cells, particularly single cells, into individual droplets without causing harm. Despite this, factors like cell sedimentation and aggregation can reduce encapsulation efficiency and lead to deviations from the expected Poisson distribution. To address these challenges, leveraging artificial intelligence and deep learning to monitor, detect, and regulate encapsulation conditions in real-time is critical for enhancing system performance. However, deep learning models require substantial training data, and issues like microfluidic channel clogging and the scarcity of certain cell types often limit data availability. To overcome this limitation, researchers are turning to synthetic data generation to supplement training datasets and address data scarcity challenges effectively. This study emphasizes the potential of integrating synthetic data with cutting-edge deep learning techniques to enhance the accuracy and efficiency of single-cell analysis within droplet microfluidic systems. A diverse dataset integrating synthetic and real images was used to train the YOLOv8s model for automated detection and classification of microfluidic droplets, enhancing accuracy and system performance. The model trained on a combination of real and synthetic data outperformed the one trained using conventional data augmentation methods, achieving an mAP 0.5 of 98% due to the increased diversity of training images. It also demonstrated faster and more stable training. Additionally, the YOLOv8 network, with a detection rate of approximately 2338 droplets per se

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Semi-Eulerian and High Order Gaussian Beam Methods for the Schrödinger Equation in the Semiclassical Regime

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Communications in Computational Physics 2011年第3期9卷 668-687页

作者： Shi Jin Hao Wu Xu Yang Department of Mathematics University of WisconsinMadisonWI 53706USA Department of Mathematical Sciences Tsinghua UniversityBeijing 10084China Program in Applied and Computational Mathematics Princeton UniversityNJ 08544USA

A novel Eulerian Gaussian beam method was developed in[8]to compute the Schrödinger equation efficiently in the semiclassical *** this paper,we introduce an efficient semi-Eulerian implementation of this *** new algorithm inherits the essence of the Eulerian Gaussian beam method where the Hessian is computed through the derivatives of the complexified level set functions instead of solving the dynamic ray tracing *** difference lies in that,we solve the ray tracing equations to determine the centers of the beams and then compute quantities of interests only around these *** yields effectively a local level set implementation,and the beam summation can be carried out on the initial physical space instead of the phase *** a consequence,it reduces the computational cost and also avoids the delicate issue of beam summation around the caustics in the Eulerian Gaussian beam ***,the semi-Eulerian Gaussian beam method can be easily generalized to higher order Gaussian beam methods,which is the topic of the second part of this *** numerical examples are provided to verify the accuracy and efficiency of both the first order and higher order semi-Eulerian methods.

关键词： Schrödinger equation semi-Eulerian Gaussian beam method high order methods

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Heterogeneous Multiscale Methods: A Review

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Communications in Computational Physics 2007年第3期2卷 367-450页

作者： Weinan E Bjorn Engquist Xiantao Li Weiqing Ren Eric Vanden-Eijnden Department of Mathematics and Program in Applied and Computational Mathematics Princeton UniversityPrincetonNJ 08544USA Department of Mathematics The University of Texas at AustinAustinTX 78712USA Department of Mathematics Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Mathematics Courant Institute of Mathematical SciencesNew York UniversityNew YorkNY 10012USA.

This paper gives a systematic introduction to HMM,the heterogeneous multiscale methods,including the fundamental design principles behind the HMM philosophy and the main obstacles that have to be overcome when using HMM for a particular *** is illustrated by examples from several application areas,including complex fluids,micro-fluidics,solids,interface problems,stochastic problems,and statistically self-similar *** is given to the technical tools,such as the various constrained molecular dynamics,that have been developed,in order to apply HMM to these *** of mathematical results on the error analysis of HMM are *** review ends with a discussion on some of the problems that have to be solved in order to make HMM a more powerful tool.

关键词： Multi-scale modeling heterogeneous multi-scale method multi-physics models constrained micro-scale solver data estimation.

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Optimization production and characterization of fish protein hydrolysate (FPH) from Sardinella Lemuru head with papain protease

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AIP Conference Proceedings 2025年第1期3186卷

作者： Jannatul Firdaus Evi Susanti Suharti Suharti Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Negeri Malang Jl. Semarang 5 Malang 65145 Indonesia Biotechnology Program Department of Applied Science Faculty of Mathematics and Natural Sciences Universitas Negeri Malang Jl. Semarang 5 Malang 65145 Indonesia

Sardinella lemuru head has potential as Fish Protein Hydrolysate (FPH). This study aims to determine the optimum conditions and characterization of FPH from lemuru fish heads produced enzymatically with papain. This study was conducted through the following stages with (1) isolation of crude extract of papain, (2) optimization of papain enzymatic FPH production methods, (3) characterization of FPH including determined of proximate value, FTIR analysis, molecular weight, the antibacterial and antioxidant activity FPH produced from lemuru fish head powder was a short peptide measuring about 25 kDa and less. FPH has water, protein, and fat value of 24.54; 28.76; and 0.224 % (w/w). The resulting yield was at an optimum condition of 18.87% under the following conditions: 1 g of fish head powder was hydrolyzed with 0.705 U papain in 8 mL of phosphate buffer pH 7 0.1 M, incubated for 3 hours at room temperature, continued for 90 minutes at 75°C and at 90°C for 5 minutes. At a concentration of 10,000 ppm, the FPH can inhibit the growth of Escherichia coli bacteria by producing an inhibition zone diameter of 4.537±0.265 and Staphylococcus aureus bacteria by 5.5±0.212 and has an IC50 value to inhibit DPPH free radical oxidation of 70.175 ppm.

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Two-dimensional multi-release software modelling with testing effort, time and two types of imperfect debugging

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International Journal of Reliability and Safety 2016年第4期10卷 368-388页

作者： Kumar, Vijay Sahni, Ramita Shrivastava, A.K. Department of Mathematics Amity School of Engineering and Technology New Delhi110061 India Department of Applied Mathematics Amity Institute of Applied Sciences Amity University Sector 125 Noida UP India Amity Center for Interdisciplinary Research Amity University Noida UP India

An indispensable dependence on software systems for all activities is tremendously increasing in this industrialised era. This dependence demands good quality of the software. In order to fulfil these demands, software firms have strategised to release the software in successive releases. But adding new functionalities in the current version is a tedious task and makes the software more complicated. This complexity increases the risk of facing more faults in the software. Sometimes, the testing team may not have sufficient knowledge of the software to remove faults completely from the complex software or may add extra faults into it. Removing faults from the software needs time and resources. In this paper, the effect of two types of imperfect debugging to develop a generalised framework for a time and resource based multi upgradation model has been incorporated. Further, a two-stage fault removal technique which considers fault detection and correction as two-stage process is also proposed. The proposed models have been validated on real data set for four releases. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Fault detection

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Experimental investigation of nonlinear internal waves in deep water with miscible fluids

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Journal of Ocean Engineering and Marine Energy 2018年第4期4卷 243-257页

作者： Camassa, R. Hurley, M.W. McLaughlin, R.M. Passaggia, P.-Y. Thomson, C.F.C. Department of Mathematics Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Chapel HillNC27599 United States Department of Marine Sciences University of North Carolina Chapel HillNC27599 United States

Laboratory experimental results are presented for nonlinear internal solitary waves (ISW) propagation in ‘deep water’ configuration with miscible fluids. The results are validated against direct numerical simulations and traveling wave exact solutions where the effect of the diffused interface is taken into account. The waves are generated by means of a dam break and their evolution is recorded with laser-induced fluorescence and particle image velocimetry. In particular, data collected in a frame moving with the waves are presented here for the first time. Our results are representative of geophysical applications in the deep ocean where weakly nonlinear theories fail to capture the characteristics of large amplitude ISWs from field observations. © 2018, Springer Nature Switzerland AG.

关键词： Solitons

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Grapenet_Lv1 for the Prediction of Optimum Hand-Picking Time of Nagano Purple Grapes

SSRN

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SSRN 2025年

作者： Islam, Md. Parvez Shinagawa, K. Hatou, K. Abdurrakhman, Arief Dian, K. Department of Biomechanical Systems Faculty of Agriculture Ehime University 3-5-7 Tarumi Matsuyama790-8566 Japan United Graduate School of Agriculture Ehime University 3-5-7 Tarumi Matsuyama790-8566 Japan Department of Instrumentation Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Business Mathematics Program Study School of Applied STEM Universitas Prasetiya Mulya BSD Campus BSD City Kav. Edutown I.1 Tangerang15339 Indonesia Indonesia

We investigated the optimum hand-picking time of Nagano Purple, a rare Japanese table grape variety. The color sensitivity between pure red–purple–black and pure purple–black makes it difficult for farmers to harvest these fruits at the right time while maintaining high sweetness level. We manually extracted 13 color parameters, namely, X, Y, Z, x, y, L*, a*, b*, L, a, b, C*, and h, from 355 grape skin samples using a colorimeter, which is a time-consuming and expensive process. In addition, we developed a 15-layer shallow long short-term memory (LSTM) and bidirectional long short-term memory (BLSTM) neural network, namely, GrapeNet_Lv1, with a learnable parameter of 1.39E+06. This network is compact and requires low computational cost for training. Furthermore, it has the ability to prevent the problems of vanishing gradient and overfitting. The combination of all the 13 aforementioned parameters yielded promising results in predicting color and sweetness levels, with test accuracies of 0.83 and 0.76, respectively. In the prediction of color level, our proposed network GrapeNet_Lv1 achieves a high validation accuracy of 0.92, F1 score of 0.88, recall of 0.87, low validation loss of 0.39, and RMSE of 0.31. However, in the prediction of sweetness level, GrapeNet_Lv1 achieves a relatively low validation accuracy of 0.79, F1 score of 0.70, recall of 0.69, high validation loss of 0.72, and RMSE of 0.33. Further research can help improve the accuracy. GrapeNet_Lv1 can be applied to other crops, where, predicting the whose optimal hand-picking time is difficult to predict based on color recognition. Keywords: Neural network;LSTM-BLSTM;Nagano Purple, color, sweetness © 2025, The Authors. All rights reserved.

关键词： Long short-term memory

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