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Answer to "comments on an examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models"

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Environmental science and Pollution Research 2025年第12期32卷 7793-7794页

作者： Ajala, Adewole Adetoro Centre of Excellence for Data Science Artificial Intelligence & Modelling University of Hull Hull United Kingdom

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Metagenomics reveals the microbial community and functional metabolism variation in the partial nitritation-anammox process:From collapse to recovery

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Journal of Environmental sciences 2024年第1期135卷 210-221页

作者： Hong Wang Hui Gong Xiaohu Dai Min Yang State Key Laboratory of Pollution Control and Resource Reuse College of Environmental Science and EngineeringTongji UniversityShanghai 200092China BIOMATH Department of Data Analysis and Mathematical ModellingGhent UniversityGhent 9000Belgium

Mainstream partial nitritation-anammox(PNA)process easily suffers from performance instability and even reactor collapse in ***,it is of great significance to unveil the characteristic of performance recovery,understand the intrinsic mechanism and then propose operational *** this study,we combined long-term reactor operation,batch tests,and metagenomics to reveal the succession of microbial community and functional metabolism variation from system collapse to *** aeration control(0.10-0.25mg O_(2)/L)was critical for performance *** was also found that Candidatus Brocadia became the dominant flora and its abundance increased from 3.5%to 11.0%.Significant enhancements in carbon metabolism and phospholipid biosynthesis were observed during system recovery,and the genes abundance related to signal transduction was dramatically *** up-regulation of sdh and suc genes showed the processes of succinate dehydrogenation and succinyl-CoA synthesis might stimulate the production of amino acids and the synthesis of proteins,thereby possibly improving the activity and abundance of AnAOB,which was conducive to the performance ***,the increase in abundance of hzs and hdh genes suggested the enhancement of the anammox *** in the abundance of key genes involved in nitrogen metabolism indicated that nitrogen removal pathway was more diverse after system *** achievement of performance recovery was driven by anammox,nitrification and denitrification coupled with dissimilatory nitrate reduction to *** results provide deeper insights into the recovery mechanism of PNA system and also provide a potential regulation strategy for the stable operation of the mainstream PNA process.

关键词： Partial nitritation-anammox Metagenomics Microbial community Functional metabolism Recovery

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Chronic disease patients have fewer social contacts:A pilot survey with implications for transmission dynamics

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Infectious Disease modelling 2024年第3期9卷 926-930页

作者： J.Vanderlocht S.Møgelmose K.Van Kerckhove P.Beutels N.Hens Interuniversity Institute of Biostatistics and Statistical Bioinformatics Data Science InstituteHasselt UniversityHasseltBelgium Centre for Health Economic Research and Modelling Infectious Diseases University of AntwerpAntwerpBelgium

Non-communicable diseases(NCD)are the most important cause of death in the *** socio-economic costs associated with NCDs makes it imperative to prevent and control them in the 21st *** severe toll that the COVID-19 pandemic has taken worldwide is an unfortunate illustration of our limited insight into the infectious risk for the global ***-incidence between NCD and infection offers an underexplored opportunity to design preventive *** a pilot survey,we observed that the NCD population displays a substantial reduction in their social contacting behavior as compared to the general *** indicates that existing mathematical models based on contact surveys in the general population are not applicable to the NCD population and that the risk of acquiring an infection following a contact is probably *** demonstration of reduced social mixing in several chronic conditions,raises the question to what extent the social mixing is influenced by the burden of *** advocate the design of disease-specific contact surveys to address how the burden of disease associates with social contact behavior and the risk of *** SARS-CoV-2 pandemic offers an unprecedented opportunity to gain insight into the importance of infection in the NCD population and to find ways to improve healthcare procedures.

关键词： survey mixing insight

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Fractional Diffusion-Wave Modification of Landau-Khalatnikov Model Applied to Polarization Switching in Ferroelectric Nanowires

Fractional Diffusion-Wave Modification of Landau-Khalatnikov...

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2024 International Conference Days on Diffraction, DD 2024

作者： Maslovskaya, Anna G. Moroz, Lubov I. Institute of Data Science and Artificial Intelligence Innopolis University Russia Amur State University Laboratory of Mathematical Modelling of Complex Physical and Biological Systems Russia

ISBN: (纸本)9798331511722

This paper is devoted to modeling of the diffusion-wave polarization processes in ferroelec-Tric nanowires based on time-fractional modification of the Landau-Khalatnikov model. The problem is formalized as an initial-boundary value problem for a semilinear time-fractional partial differential equation. To solve the problem numerically, an implicit computational scheme was derived using the fractional derivative in the Caputo sense. The constructed numerical algorithms were implemented using Matlab software. The designed com-puter program was used to conduct simulations of the spatial-Temporal polarization distributions and hysteresis loops for BaTiO3 nanowires in different dynamical modes. This approach allows one to provide modeling of characteristics of polarization processes in low-dimensional ferroelectric systems due to the variation of regimes by means of changes in the dynamical dimension. © 2024 IEEE.

关键词： Ferroelectricity

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Efficiency for data Parallel Computation in Deep Neural Networks 3

Efficiency for Data Parallel Computation in Deep Neural Netw...

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3rd International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Feng, Yujun Major in Computational Modelling and Data Analytics Department of Science Virginia Polytechnic Institute State University BlacksburgVA24061 United States

ISBN: (纸本)9781510661301

The data parallelism provides greater efficiency under multiple-node systems. Under these circumstances, there is more and more utilisation on multiple GPUs for better efficiency for computation and lower cost of time for accomplishing the project. However, some typical, ordinary, and common circumstances in which using multiple GPUs is worse than using one GPU in a machine. This paper aims to prove that using various GPUs is not omnipotent in efficiency and cost of time when running a program. This paper represents the limitations of computation with CPU, GPU, and multiple GPUs. Two main parts represent the limitations of the experiment. They are the comparison between one running CPU and one running GPU and the comparison between one running GPU and running multiple GPUs with the cost of time after running a program by numerous times and different data sizes. © 2023 SPIE.

关键词： Deep neural networks

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Spatiotemporal characteristics and dynamic prediction of agricultural carbon compensation potential in the middle and lower reaches of the Yellow River Basin

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Environmental science and Pollution Research 2025年第4期32卷 1903-1917页

作者： Luo, Jikang Zhao, Zhen Pang, Jing College of Science Inner Mongolia University of Technology Hohhot015000 China School of Mathematics and Statistics Ningbo University Ningbo315000 China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modelling Inner Mongolia University of Technology Hohhot015000 China

Climate change, driven by carbon emissions, has emerged as a pressing global ecological and environmental challenge. Here, we leverage the panel data of five provinces and above prefecture-level cities in the middle and lower reaches of the Yellow River Basin to estimate the agricultural carbon emissions (CEs), carbon sinks (CSs), carbon compensation rate (CCR), and carbon compensation potential (CCP) from 2001 to 2022 and investigate the spatiotemporal evolution characteristics for this region. We propose an improved GLM-stacking ensemble learning method for CE prediction with limited sample data. The findings indicate the following: (i) From 2001 to 2022, the overall CEs show a trend of "development - decline - stabilization" and reach a peak of 172.54 Mt in 2005. CCR first exceeded the "CCR = 1" in 2008, which also indicates that reducing CEs and increasing CSs are the paths to achieving agricultural carbon neutrality. (ii) Although each province has achieved "net-zero emissions," the CCP of most urban agglomerations is about 0.5 and shows a certain agglomeration trend, indicating significant room for further carbon offset. (iii) The novel GLM-stacking model has higher prediction accuracy when compared to a single model. These findings provide scientific and technological support to realize the provincial dual carbon goals in China. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Carbon capture and utilization

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Q-Rung Orthopair Fuzzy Sets-Enhanced FMEA for COVID-19 Risk Assessment

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 81-92页

作者： Abdullah, Lazim Awang, Noor Azzah Qiyas, Muhammad Special Interest Group of Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Mathematics Riphah International University Failsabad Campus Pakistan

Failure Modes and Effects Analysis (FMEA) is a widely used tool for risk analysis, primarily to identify risk factors affecting system quality. Due to the limitations of the traditional FMEA model, several recent models incorporating advanced fuzzy set extensions have been developed to enhance the reliability of risk assessment outcomes. However, most of these models limit expert flexibility in expressing preferences and often overlook the impact of unequal expert weights and the stability of risk ranking results. This study introduces a new FMEA model based on Q-Rung Orthopair Fuzzy Sets (Q-ROFSs), termed Q-ROFSs-FMEA. Q-ROFSs, an extension of intuitionistic fuzzy sets, introduce a new linguistic term. The Q-ROFSs-FMEA model considers the unequal weights of experts, enabling a dynamic representation of expert preferences. These weights and the linguistic evaluation of risk factors are integrated through an aggregation operator, facilitating consensus among experts. The model is applied to a case study on COVID-19 risk factors, revealing that ‘older age’ (risk priority number 0.000012) is the highest risk factor, while ‘gender’ (risk priority number -0.0037) is the lowest. It is found that the ranking of risk factors determined by the Q-ROFSs-FMEA model is obtained as FM1 FM3 FM6 FM4 FM5 FM7 FM8 FM2 . Furthermore, a comparative analysis indicates consistent ranking results across different models, demonstrating the reliability of the proposed model. The case study and comparative analysis validate the effectiveness and applicability of the Q-ROFSsbased risk assessment model. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Failure modes

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A Single Shot Multi-Head Gender, Age, and Landmarks Detection using Shared Convolution Features

A Single Shot Multi-Head Gender, Age, and Landmarks Detectio...

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International Conference on Automation and Computing (ICAC)

作者： Gulraiz Khan Kevin Pimbblet Kenneth Wertheim Waqas Ahmed Data Science AI and Modelling Centre (DAIM) University of Hull Hull UK

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

Considering the face as a vital and most informative portion of the human body, it reflects different high-level information about an individual. This high-level information includes Age, Gender, and Emotion. Facial muscles’ shape and movement can be the best descriptors for the automatic extraction of these high-level facial features. Detection of these high-level features has applications in different areas including entertainment, surveillance, multimedia, and educational training. However, with the varying nature of these features, it becomes difficult to capture one class with the variability of other classes. This article presents a lightweight heterogeneous neural network with one shared backbone and three network heads to predict multiple face features: landmarks, age, and gender. The proposed system (MultiHeadCNN) captures these high-level facial features in the wild with extreme face pose, occlusions, and lightening conditions. The system is capable of predicting one type of feature with different variability of other types: predicting gender for different age groups and vice versa. The system is tested on comprehensive (UTKFace) and complex (Adience) datasets with varying age, gender, pose, and lightening conditions. The experiment shows promising results in terms of accuracy, with results for age and gender detection on the UTKFace and Adience datasets being 99.9%, 99.7%, 90.3%, and 61.7%, respectively. Furthermore, the parallel inference speed is 20 frames per second.

关键词： Training Accuracy Convolution Shape Surveillance Neural networks Entertainment industry Feature extraction Faces Facial features

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DN-ODE: data-driven neural-ODE modeling for breast cancer tumor dynamics and progression-free survivals

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Computers in Biology and Medicine 2024年 180卷 108876页

作者： Xiang, Jinlin Qi, Bozhao Cerou, Marc Zhao, Wei Tang, Qi Data and Data Science Sanofi 450 Water St CambridgeMA02141 United States Data and Data Science Sanofi 55 Corporate Dr BridgewaterNJ08807 United States Translational Disease Modelling Oncology Data and Data Science Sanofi R&D 55 Corporate Dr Chilly-Mazarin91380 France

Pharmacokinetic/Pharmacodynamic (PK/PD) modeling is crucial in the development of new drugs. However, traditional population-based PK/PD models encounter challenges when modeling for individual patients. We aim to explore the potential of constructing a pharmacodynamic model for individual breast cancer pharmacodynamics leveraging only limited data from early clinical trial phases. While previous studies on Neural Ordinary Differential Equations (ODEs) suggest promising results in clinical trial practices, they primarily focused on theoretical applications or independent PK/PD modeling. PD modeling from complex and irregular clinical trial data, especially when interacting with PK parameters, is still unclear. To achieve that, we introduce a data-driven Neural Ordinary Differential Equation (DN-ODE) modeling for breast cancer tumor dynamics and progression-free survival data. To validate this approach, experiments are conducted with early-phase clinical trial data from the Amcenestrant (an oral treatment for breast cancer) dataset (AMEERA 1-2), aiming to predict pharmacodynamics in the later phase (AMEERA 3). DN-ODE model achieves RMSE scores of 8.78 and 0.21 in tumor size and progression-free survival, respectively, with R2 scores over 0.9 for each task. Compared to PK/PD methodologies, DN-ODE is able to predict robust individual tumor dynamics with only limited cycle data. We also introduce Principal Component Analysis visualizations for encoder results, demonstrating the DN-ODE's capability to discern individual distributions and diverse tumor growth patterns. Therefore, DN-ODE facilitates comprehensive drug efficacy assessments, pinpoints potential responders, and aids in trial design. © 2024 Elsevier Ltd

关键词： Ordinary differential equations

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Quantifying Uncertainty in Mid-Infrared Spectroscopy Calibration Models Using a Monte Carlo Approach

SSRN

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SSRN 2024年

作者： Wright, Liam Harris, Peter Chu, Suoda Nordon, Alison Data Analytics & Modelling Data Science National Physical Laboratory London United Kingdom University of Strathclyde Glasgow United Kingdom

Background: Trustworthiness is essential to the validity of any results, conclusions, or comparisons based on measurement, and defining traceable methods for quantifying uncertainty is crucial. Generally, uncertainties in chemometrics are largely ignored or inadequately treated. We aim to address the lack of robust uncertainty quantification in one aspect of chemometric data processing by proposing a numerical approach, based on the Monte Carlo method, for propagating uncertainty through a partial least squares regression model for mid-infrared spectroscopy. (75) Results: We provide a novel Monte Carlo method for quantifying uncertainty associated with the output of a Partial Least Square regression calibration model whilst providing a pseudo-algorithm in order for the reader to use this technique. We examine the effect of varying the amount of available input data using two sampling techniques: rudimentary random sampling for a low-level of input data, and mapped random sampling for a dataset that is partially large. We show that the low-level random sampling results show erroneous correlation in the output dataset and produces falsely low levels of uncertainty. Mapped random sampling results were much more robust, indicating the need for more input data to increase the trustworthiness of the output and the associated uncertainties. Both random sampling and mapped random sampling results are compared using various statistical metrics. (133) Significance: Although the Monte Carlo method provides a valuable tool for uncertainty propagation through the chemometric data processing pipeline, we find that the results obtained are dependent on the nature of the available input data and the way that data is used, thus highlighting the importance of trustworthiness in data gathering and modelling. (52) © 2024, The Authors. All rights reserved.

关键词： Monte Carlo methods

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