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Data-driven modeling of background radiation structure utilizing matrix profile in nuclear security.
Sci Rep
January 2025
Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, 78249, USA.
The inherently stochastic nature of radiation emissions makes modeling background radiation structure a particularly challenging research area. In source identification scenarios, which are critical to nuclear security, the complexity of background radiation modeling is intensified by dynamically changing factors that influence radiation measurements. Consequently, accurately modeling and estimating background radiation can significantly improve our nuclear security capabilities by enhancing the detection of anomalies within radiation data.
View Article and Find Full Text PDFThe classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine learning algorithms. Diabetes is one of the chronic diseases with a high prevalence worldwide and is considered a disease in its own right. Given the widespread nature of this chronic condition, numerous researchers are striving to develop robust machine learning algorithms for accurate classification.
View Article and Find Full Text PDFA new epidemic model of sexually transmittable diseases: a fractional numerical approach.
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January 2025
Department of Mathematics, College of Science Al-Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia.
This study aims at investigating the dynamics of sexually transmitted infectious disease (STID), which is serious health concern. In so doing, the integer order STID model is progressed in to the time-delayed non-integer order STID model by introducing the Caputo fractional derivatives in place of integer order derivatives and including the delay factors in the susceptible and infectious compartments. Moreover, unique existence of the solution for the underlying model is ensured by establishing some benchmark results.
View Article and Find Full Text PDFSignificance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease.
Sci Rep
January 2025
School of Mathematics and Statistics, Central South University, Changsha, 410083, China.
The coronavirus disease 2019 (COVID-19) interventions in interrupting transmission have paid heavy losses politically and economically. The Chinese government has replaced scaling up testing with monitoring focus groups and randomly supervising sampling, encouraging scientific research on the COVID-19 transmission curve to be confirmed by constructing epidemiological models, which include statistical models, computer simulations, mathematical illustrations of the pathogen and its effects, and several other methodologies. Although predicting and forecasting the propagation of COVID-19 are valuable, they nevertheless present an enormous challenge.
View Article and Find Full Text PDFDynamic urban morphology mapping in Chinese cities based on local climate zone approach.
Sci Data
January 2025
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China.
Urbanization have been significantly reshaping the form of urban areas and natural landscapes, leading to complex urban morphologies. In 2012, the Local Climate Zone (LCZ) classification was proposed to address this issue and has since been widely adopted in urban climate studies globally. Despite its prevalence, literature on dynamic mapping of urban morphology remains sparse, making it difficult to delve into the study of urban renewal year by year.
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