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Threshold dynamics of an age-structured vaccinated epidemic model with both direct and indirect routes of infections
No full textAge-structured epidemic models play a crucial role in the study of epidemiological modeling. Motivated by this, in this article, we propose an epidemic model with age since infection and vaccination age of individuals, a coupled system of PDE and integro-differential equations (IDE). Here, two contagious routes, (a) direct human-to-human contact and (b) indirect environmentally contaminated surfaces or objects are considered. First, we establish the well-posedness of the model, followed by the basic reproduction number (R0) and the role of the threshold value of R0 in the asymptotic profile of the solution semi-flow is established. We observe the global stability of the disease-free steady state for R0\u3c1, the uniform persistence of the disease and the existence of the endemic steady state for R0\u3e1. This endemic steady state is also globally asymptotically stable for R0\u3e1. We have further analyzed the influence of vaccination age and age since infection in the threshold parameter R0. Our analysis shows that the threshold parameter R0 does not depend explicitly on vaccination age, but it strictly decreases with the natural depletion rate of the contaminated environment. Finally, the model is discretized using the finite difference method to illustrate our theoretical results numerically
Teaming Strategy Optimization: An Analysis Of NBA Statistics, Shot Charts, And Constraints
Get PDFIn the dynamic realm of NBA team management, balancing the intricacies of player performance and strategic signings present formidable challenges. Negotiating salary caps, player roles, on-court minutes, and contract durations requires a nuanced approach. This study comprehensively evaluated player efficiency and team performance, scrutinizing key statistical indicators like Points, Rebounds, Assists, Blocks, PER, RPM, Shot Charts, and others. Leveraging machine learning algorithms, including logistic, ridge, and lasso regressions, facilitated modeling the intricate relationship between player performance and team winning rates. Based on that, incorporating practical constraints yielded diverse and effective teaming strategies. Analyzing NBA player and game statistics from 2012 to 2022, the experimental findings underscore the accuracy of prediction models and the success of player selection strategies. This research provides actionable insights for NBA franchises seeking to streamline team operations and achieve triumphs on the court.
Index Terms: Court coverage, machine learning, NBA, performance evaluation, shot charts, sports analysis, teaming strategy
(R2086) Circular Restricted Three-Body Interaction Problem With Various Perturbations
Get PDFThe motion properties of the infinitesimal body is studied under the forces due to kerr-like oblate heterogeneous primary, continuation fractional potential for secondary, solar sail, three-body interactions, Coriolis and centrifugal forces in the circular restricted three-body problem. The equations of motion of infinitesimal body are evaluated under the above-said perturbations. Using these equations of motion, we illustrate the locations of equilibrium points, their stability, the periodic orbits and Poincaré surfaces of section. This study will applicable on the motion of the artificial satellite
Is Science for All an Elusive Goal? Disparities in U.S. Science Education
Get PDFAn examination of published reports on Free or Reduced-Price Lunch (FRPL) regarding Per Pupil Spending, Technology Resources, Science Course Offerings, and Race and Ethnicity Distribution in FRPL Groups shows that science for all remains an elusive goal in the United States. Science for all requires long-term solutions, including adequate fiscal resources for high FRPL quartile schools and effective policies to ensure quality science learning experiences for low-income students. Teachers in high FRPL schools need access to high-quality instructional resources, technology tools, and effective strategies to engage students in science learning. Schools should invest in technology tools like virtual reality and simulations, and teachers should be prepared to apply inquiry-based pedagogies. Additionally, students in high FRPL schools deserve teachers with context knowledge to teach advanced placement and international baccalaureate courses afforded to students in low FRPL schools. Education stakeholders, including small- to large-scale enterprises and local, state, and federal governments, must collaborate with the scientific education community to eliminate inequalities in science education. Existing socioeconomic disparities in science education remain an impediment to science for all. By addressing the genesis and dynamics of disparities, every student in K-12 classrooms can benefit from a quality science education
Enhancement Of Network Anomaly Detection Using Artificial Intelligence Techniques
Get PDFTraditional signature-based network intrusion detection systems, which capture network attributes, are inadequate against zero-day attack. The smaller number of attacks creates an imbalanced dataset, the major problem during anomaly detection. Machine Learning (ML) and Deep Learning (DL) approaches are promising for network anomaly detection because they can efficiently analyze big network traffic data for malicious activities and detect zero-day attacks. The appropriate selection of the ML/DL algorithm, hyperparameter tuning, and techniques, such as sampling methods, ensemble methods, and reduction of number of classes, can enhance the anomaly detection performance of the anomaly detection methods on an imbalanced network intrusion-based dataset. The efficacy of various traditional ML models such as Random Forest (RF), J48, Naïve Bayes, Bayesian Network, Bagging, AdaBoost, and Support Vector Machine (SVM) is examined. Different combinations of deep learning models, including convolutional neural networks, bidirectional long-short term memory (LSTM) models, ensemble techniques, sampling techniques, and class reduction approaches, are applied to different sets of network-based intrusion datasets (KDD99, UNSW-NB15, CIC-IDS2017). These
experiments are conducted using different tools (WEKA, Jupyter Notebook) on the Anaconda platform. Investigation results reveal that traditional ML models are suitable for smaller data and low computational power. Deep learning models outperform huge datasets with large numbers of features but require significantly more computational power. The proposed heterogeneous ensemble method, which combines a number of different models along with a wise selection of hyperparameters and class size reduction techniques, has been demonstrated to significantly enhance anomaly detection performance on communication network-based intrusion datasets. Implementing different sampling techniques on different training and testing dataset combinations provided insight into application sampling techniques to deal with imbalance network intrusion datasets. The sampling is only efficient for the single set of working data, but the class reduction method to deal with class imbalance problems results in more efficient performance in regard to the single or different set of training and testing data given for network anomaly detection. The overall combination of results and conclusions will provide a comprehensive study of artificial intelligence techniques to enhance network anomaly detection in communication networks.
Index Terms— ADASYN, Bi-LSTM, CIC-IDS2017, class reductions, CNN-BLSTM, deep learning, heterogeneous ensemble learning, imbalance dataset, KDD99, LSTM, machine learning, network intrusion detection system, NSL-KDD, Random Over Sampling (ROS), Random Under Sampling (RUS), SMOTE, SMOTEENN, UNSW-NB15
Panther - March 2016 - Vol. XCVI, Issue12
Get PDFhttps://digitalcommons.pvamu.edu/pv-panther-newspapers2016/1001/thumbnail.jp
(R2108) Global Asymptotic Stability Analysis of Discrete Time Population Model with Allee Effect through Lyapunov Function
Get PDFThis study explores the idea of stability analysis by using Lyapunov functions in discrete time population models; this work focuses on the nth generation. Further, this investigation aims to extend global stability concepts to discrete-time models and considers the influence of the Allee effect on population dynamics. Mathematical formulations and specific modelling approaches are utilized to investigate the behavior of the population system. The results reveal a larger range of stability in comparison to previous findings, emphasizing the effectiveness of the Lyapunov function approach. Specifically highlighted here are the extension of global stability concepts to the nth generation providing insights into how the Allee effect impacts the stability of discrete time population models. Also, the next-generation results confirm an augmented stability region. A key contribution of the research lies in its exploration of stability concepts beyond the traditional scope, particularly extending to the nth generation. The Allee effect adds novelty to the analysis and provides a more nuanced understanding of population dynamics in discrete time models. This study’s findings have potential applications in various fields, including ecology and population management. Understanding the extended stability concepts in discrete time models can offer insights into the long-term behavior of populations, aiding in more effective conservation strategies
(R2100) Optimality Conditions of a TOPSIS Optimization Model and its Application on Interval-Valued Data
Get PDFThe Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is widely used in the field of multi-criteria decision analysis. Despite its popularity and widespread application, little attention has been given to the mathematical foundation that underlies the TOPSIS algorithm. The existing literature on this subject is far from comprehensive, leaving many aspects of the algorithm unexplored. This paper aims to address this gap in the literature by delving into the optimization problem associated with TOPSIS. Unlike traditional interval analysis theory, which only covers a limited scope, our approach extends to a broader range of scenarios and offers valuable practical applications. Moreover, we have identified the necessary prerequisites for obtaining the optimal solution points in TOPSIS. Additionally, we have established a significant relationship between the TOPSIS Optimization Problem and Variational Inequality Problem. Through our comprehensive analysis and investigation, we make substantial contributions to the understanding and advancement of the TOPSIS methodology
Panther - April 2015 - Vol. XCV, Issue 6
Get PDFhttps://digitalcommons.pvamu.edu/pv-panther-newspapers2015/1000/thumbnail.jp