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Method for modelling the number of HIV and AIDS cases using least square spline Biresponse nonparametric negative binomial regression

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MethodsX 2025年 14卷 103336页

作者： Ramadan, Arip Chamidah, Nur Budiantara, I Nyoman Lestari, Budi Aydin, Dursun Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Statistics Department Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences The University of Jember Jember 68121 Indonesia Department of Statistics Faculty of Science Muğla Sıtkı Koçman University Muğla 48000 Turkey Research Scholar at Department of Mathematics University of Wisconsin Oshkosh Algoma Blvd Oshkosh 54901 WI United States

We propose a new statistical modeling method to analyze functional relationship between predictor variables and correlated response variables, and apply it for modeling the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users. The proposed method is LS-Spline BNNBR that is a Biresponse Nonparametric Negative Binomial Regression (BNNBR) based on least squares spline (LS-Spline). To validate the proposed method, we compare it with classical method, namely Ordinary Least Square Biresponse Parametric Negative Binomial Regression (OLS-BPNBR). The results show that the LS-Spline BNNBR method provided deviance value of 7.542334 which is smaller than that of OLS-BPNBR method, namely 7.993935. Thus, the proposed method has better prediction fit than the classical method. • This study discusses new statistical modeling method for the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users where the response variables are correlated with each other. • The model estimation results show that the number of HIV and AIDS cases in Indonesia will decrease if the early-age marriages percentage increases and the contraceptive users percentage decreases. • The results are useful to provide a scientific basis for making policies to control the number of HIV and AIDS cases in Indonesia. © 2025 The Authors

关键词： AIDS BLSSNNBR model Contraceptive user Early-age marriage HIV Least square spline

Correlations between student connectivity and academic performance: a pandemic follow-up

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arXiv 2022年

作者： Crossette, Nathan Carr, Lincoln D. Wilcox, Bethany R. Department of Physics University of Colorado Boulder BoulderCO80309 United States Quantum Engineering Program Colorado School of Mines GoldenCO80401 United States Department of Physics Colorado School of Mines GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States

Social network analysis (SNA) has been gaining traction as a technique for quantitatively studying student collaboration. We analyze networks, constructed from student self-reports of collaboration on homework assignments, in two courses from the University of Colorado Boulder and one course from the Colorado School of Mines. All three courses occurred during the COVID-19 pandemic, which allows for a comparison between the course at the Colorado School of Mines (in a fully remote format) with results from a previous pre-pandemic study of student collaboration at the Colorado School of Mines (in a hybrid format). We compute nodal centrality measures and calculate the correlation between student centrality and performance. Results varied widely between each of the courses studied. The course at the Colorado School of Mines had strong correlations between many centrality measures and performance which matched the patterns seen in the pre-pandemic study. The courses at the University of Colorado Boulder showed weaker correlations, and one course showed nearly no correlations at all between students' connectivity to their classmates and their performance. Taken together, the results from the trio of courses indicate that the context and environment in which the course is situated play a more important role in fostering a correlation between student collaboration and course performance than the format (remote, hybrid, in-person) of the course. Additionally, we conducted a short study on the effect that missing nodes may have on the correlations calculated from the measured networks. This investigation showed that missing nodes tend to shift correlations towards zero, providing evidence that the statistically significant correlations measured in our networks are not spurious. © 2022, CC BY.

关键词： Students

Fractional Integrable Nonlinear Soliton Equations

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arXiv 2022年

作者： Ablowitz, Mark J. Been, Joel B. Carr, Lincoln D. Department of Applied Mathematics University of Colorado BoulderCO80309 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States Department of Physics Colorado School of Mines GoldenCO80401 United States Quantum Engineering Program Colorado School of Mines GoldenCO80401 United States

Nonlinear integrable equations serve as a foundation for nonlinear dynamics, and fractional equations are well known in anomalous diffusion. We connect these two fields by presenting the discovery of a new class of integrable fractional nonlinear evolution equations describing dispersive transport in fractional media. These equations can be constructed from nonlinear integrable equations using a widely generalizable mathematical process utilizing completeness relations, dispersion relations, and inverse scattering transform techniques. As examples, this general method is used to characterize fractional extensions to two physically relevant, pervasive integrable nonlinear equations: the Korteweg–deVries and nonlinear Schrödinger equations. These equations are shown to predict super-dispersive transport of non-dissipative solitons in fractional media. © 2022, CC BY.

关键词： Nonlinear equations

Fractional Integrable and Related Discrete Nonlinear Schrödinger Equations

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arXiv 2022年

Integrable fractional equations such as the fractional Korteweg-deVries and nonlinear Schrödinger equations are key to the intersection of nonlinear dynamics and fractional calculus. In this manuscript, the first discrete/differential difference equation of this type is found, the fractional integrable discrete nonlinear Schrödinger equation. This equation is linearized;special soliton solutions are found whose peak velocities exhibit more complicated behavior than other previously obtained fractional integrable equations. This equation is compared with the closely related fractional averaged discrete nonlinear Schrödinger equation which has simpler structure than the integrable case. For positive fractional parameter and small amplitude waves, the soliton solutions of the integrable and averaged equations have similar behavior. © 2022, CC BY.

关键词： Nonlinear equations

Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things

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Intrusion Detection using Deep Neural Network Algorithm on t...

IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Syariful Ikhwan Adi Wibowo Budi Warsito Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia D3 Telecommunication Engineering Institut Teknologi Telkom Purwokerto Banyumas Indonesia Computer Science Informatics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia Statistics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia

ISBN: (数字)9781665460309

ISBN: (纸本)9781665460316

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small.

关键词： Deep learning Training Satellites Neural networks Intrusion detection Training data Feature extraction

Position: Bayesian deep learning is needed in the age of large-scale AI 24

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Position: Bayesian deep learning is needed in the age of lar...

Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Enhancing coastal community preparedness: The role of education, experience, and exposure to information

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Progress in Disaster Science 2025年 26卷

作者： Oktari, Rina Suryani Sofyan, Hizir Hidayati, Alfy Rolanda, Randy Syamsidik Puturuhu, Ferad Sasaki, Daisuke Tsunami & Disaster Mitigation Research Center (TDMRC) Graduate Program in Disaster Science and Department of Family Medicine Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Statistics Faculty of Mathematics and Natural Science Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Civil Engineering Faculty of Engineering and Tsunami & Disaster Mitigation Research Center Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Agricultural Cultivation Faculty of Agriculture Universitas Pattimura Ambon 97233 Indonesia International Research Institute of Disaster Science (IRIDeS) Tohoku University Sendai 980-0845 Japan

Coastal hazards create impact damage on vital assets in lowland coastal zones. While education, experience, and information exposure affect individual preparedness, the combined impact of coastal hazards on community readiness needs further exploration. This study aims to explore factors influencing coastal community preparedness, by examining relationships between preparedness and information exposure variables, and by assessing the impact of knowledge, experience, and information exposure using Structural Equation Modelling (SEM). This study involves 932 respondents in the Indonesian coastal cities of Banda Aceh, Mataram, and Ambon. The SEM analysis confirmed a well-fitting model with the following statistics: χ2 = 17.961, RMSEA = 0.017, GFI = 0.996, CFI = 0.998, AGFI = 0.986, TLI = 0.996, and Normed Chi-Square = 1.283. These results indicate strong alignment between the proposed model and the observed data. The study found positive relationships between preparedness variables and their respective indicators. Similarly, exposure variables related to information sources on coastal hazards and early warnings also showed positive associations with their indicators. Education, experience, and exposure to information were identified as significant factors influencing community preparedness, explaining 89 % of the variability in preparedness variables. These findings underscore the importance of these factors in enhancing community resilience to coastal hazards. © 2025 The Authors

关键词： Climate change Coastal hazards Community resilience Disaster Structural equation

BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: DATASETS, MODELS, TOOLS, AND METRICS FOR CROSS-DATASET GENERALIZATION ANALYSIS

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

Study of Glottal Attack Time and Glottal Offset Time in Neurogenic Voice Disorders During Sustained Phonation

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Journal of Voice 2025年

作者： Henry, Trent M. Deliyski, Dimitar D. Zayernouri, Mohsen Zacharias, Stephanie R.C. Naghibolhosseini, Maryam Department of Communicative Sciences and Disorders Michigan State University 1026 Red Cedar Rd East Lansing MI United States Department of Mechanical Engineering and Statistics and Probability Michigan State University 474 S Shaw Ln East Lansing MI United States Head and Neck Regenerative Medicine Program Mayo Clinic 13400 E Shea Blvd Scottsdale AZ United States Department of Otolaryngology-Head and Neck Surgery Mayo Clinic 5777 E Mayo Blvd Phoenix AZ United States

Objective: Neurogenic voice disorders like adductor laryngeal dystonia (AdLD), vocal tremor, and vocal paresis/paralysis impact the neuromuscular control of the phonatory system, which might lead to an impaired phonation onset/offset. Utilizing laryngeal high-speed videoendoscopy (HSV), this study investigates the glottal attack time (GAT) and glottal offset time (GOT) during soft, habitual, and hard glottal attack modalities in sustained phonation for several neurogenic voice disorders. Methods: HSV data and audio recordings were obtained simultaneously from 14 participants with neurogenic voice disorders and 14 normophonic participants during two productions of the /i/ vowel at soft, habitual, and hard glottal attacks. Using HSV, GAT, defined as the time interval between the first oscillation and the first contact of the vocal folds at phonation onset, and GOT, the time interval between the last oscillation and the last contact of the vocal folds at phonation offset, were measured. GAT and GOT for different glottal attack modalities were compared within each group, also among the different disorders and normophonic participants. Additionally, these values were compared between sustained phonation and connected speech for the normophonic participants and those with AdLD. Results: The neurogenic disorders group exhibited significantly higher GAT values than the normophonic participants during the hard glottal attack modality. Habitual GOTs for vocal tremor were significantly different than those of unilateral vocal fold paralysis and AdLD. Moreover, the normophonic participants shared similar trends between connected speech and sustained phonation across glottal attack modalities for GAT, while the AdLD group did not. AdLD participants shared the same trend for GOT, while normophonic participants did not. Conclusions: This study provides evidence of differences in GAT and GOT values during sustained phonation across different neurogenic voice disorders, contributing

关键词： Neurogenic voice disorders—High-speed videoendoscopy—Sustained phonation—Glottal attack time—Glottal offset time