Unidirectional glass fiber-reinforced thermoplastic (UGFT) composite tapes are promising recyclable structural materials for applications such as composite pressure pipes. However, their durability under hydrothermal environments remains a critical concern. This study emphasizes metrology-driven evaluation of aging behavior in polypropylene-based UGFT tapes. Specimens were conditioned at 95 °C in a deionized-water environment for up to 4 weeks, and multiple complementary measurement techniques were applied to quantify degradation. Mass-change metrology was performed to characterize water uptake kinetics and establish diffusion-driven aging progression. Tensile testing enabled quantitative assessment of mechanical strength retention, defining a >25% reduction in strength as a threshold for significant deterioration. Acoustic emission (AE) acted as the central non-destructive monitoring method, capturing high-fidelity waveforms generated during loading. AE waveform descriptors, such as amplitude, rise time, and frequency content, served as measurable indicators of internal damage mechanisms including matrix cracking, interfacial debonding and fiber breakage. To process large AE datasets, principal component analysis was used for dimensionality reduction, followed by k-means clustering to group signals by damage type. Optical microscopy provided microstructural verification of these classifications. The integrated metrological framework demonstrates a reliable pathway to monitor, identify, and quantify damage evolution in hydrothermally aged UGFT structures.
Gravitational-wave (GW) astronomy has advanced our understanding of compact mergers through instruments like the Laser Interferometer Gravitational-Wave Observatory (LIGO). However, the extreme sensitivity required for these detections makes the instruments susceptible to short-duration transient noise, or glitches, which obscure GW data. Current tools such as Omega Scan and GravitySpy assist in identifying and classifying such noise, but are limited by manual inspection or dependence on large training sets. To address these challenges, we present \textit{OmegaNeuron}, a machine-learning tool that integrates GravitySpy's image similarity methods with Omega Scan's transient analysis to automate the identification of auxiliary channels that witness glitches. Applied to multiple glitch examples, OmegaNeuron consistently highlighted plausible witness channels and showed strong agreement with existing correlation tools, while providing clearer ranking through a quantitative similarity metric. Integrated into the \texttt{gwdetchar} package, OmegaNeuron enables faster analysis that improves glitch witness identification, enhancing both detector sensitivity and the reliability of gravitational-wave observations.
Khushdil Ahmad, Liliana Guran, Muhammad Adnan Farid
et al.
Abstract In this paper, we study the four-step iterative process of Garodia and Uddin in the framework of Banach spaces and obtain convergence results for multivalued generalized α-nonexpansive mappings. We provide a numerical example supporting our claim that this process converges faster than other leading iterative schemes. Our results improve, extend, and adjust existing results in the literature.
Leukemia is the name for a blood cancer that develops in the bone marrow. Leukemia is a global public health issue caused by the uncontrolled growth of immature white blood cells in the bloodstream. In this study, we consider a fractional-order five-compartment mathematical model (MM) of leukemia, which includes susceptible blood cellsS1(t), infected blood cells I1(t), cancer cells C1(t), immune blood cells W1(t), cytokine cells C2(t), and we analyze the dynamics of transmission of the disease. We developed a model to examine the spread of the leukemia virus and analyze the effects of adoptive T-cell therapy. This study presents a model of the well-known leukemia virus utilizing Caputo fractional order (CFO) and Beta derivatives. In this, the extended system characterizing the virus spread is addressed using two analytical methods: the Laplace perturbation method (LPM) and the Homotopy decomposition method (HDM). Iterative schemes were employed to obtain specific solutions of the extended system, and numerical simulations were conducted based on selected theoretical parameters. Moreover, the concerned analytical solutions that have been found using the methods are compared. The corresponding plots against various orders of the differentiations are plotted using specific values for the model’s parameters. We emphasize the significance of fractional-order (FO) modeling in understanding the spread of leukemia and highlight the critical need for global access to this immunotherapy.
Antonio Alison Pinheiro Martins, Isabel Cristina Rodrigues de Lucena , Maria de Fátima Vilhena da Silva
et al.
O presente estudo é uma pesquisa qualitativa, do tipo estado do conhecimento, que tem por objetivo discutir o que tem sido pesquisado em relação ao uso dos jogos digitais, no ensino da Matemática, na Educação Básica brasileira, no período de 2016 a 2021. A busca foi realizada na base de dados do portal de periódicos da CAPES e resultou na seleção de 13 artigos. A fundamentação teórica é centrada nos jogos digitais no ensino da Matemática a partir de autores como: Brito et al. (2017), Borges et al. (2021), Brito e Sant’ana (2020), dentre outros. Diante dos dados, observou-se que os jogos digitais têm sido criados e/ou apresentados no ensino da Matemática como ferramentas didáticas que possibilitam a construção de significados e de aprendizagem de conceitos matemáticos e como alternativas de inclusão, no processo de ensino, das tecnologias digitais, bastante difundidas no meio social dos estudantes.
Special aspects of education, Applied mathematics. Quantitative methods
Ana-Maria Ștefan, Nicu-Răzvan Rusu, Elena Ovreiu
et al.
This article introduces a groundbreaking medical information system developed in Salesforce, featuring an automated classification module for ocular and skin pathologies using Google Teachable Machine. Integrating cutting-edge technology with Salesforce’s robust capabilities, the system provides a comprehensive solution for medical practitioners. The article explores the system’s structure, emphasizing innovative functionalities that enhance diagnostic precision and streamline medical workflows. Methods used in development are discussed, offering insights into the integration of Google Teachable Machine into the Salesforce framework. This collaborative approach is a significant stride in intelligent pathology classification, advancing the field of medical information systems and fostering efficient healthcare practices.
Understanding how cooperation emerges in public goods games is crucial for addressing societal challenges. While optional participation can establish cooperation without identifying cooperators, it relies on specific assumptions -- that individuals abstain and receive a non-negative payoff, or that non-participants cause damage to public goods -- which limits our understanding of its broader role. We generalize this mechanism by considering non-participants' payoffs and their potential direct influence on public goods, allowing us to examine how various strategic motives for non-participation affect cooperation. Using replicator dynamics, we find that cooperation thrives only when non-participants are motivated by individualistic or prosocial values, with individualistic motivations yielding optimal cooperation. These findings are robust to mutation, which slightly enlarges the region where cooperation can be maintained through cyclic dominance among strategies. Our results suggest that while optional participation can benefit cooperation, its effectiveness is limited and highlights the limitations of bottom-up schemes in supporting public goods.
Mathematical Epidemiology (ME) shares with Chemical Reaction Network Theory (CRNT) the basic mathematical structure of its dynamical systems. Despite this central similarity, methods from CRNT have been seldom applied to solving problems in ME. We explore here the applicability of CRNT methods to find bifurcations at endemic equilibria of ME models. We adapt three CRNT methods to the features of ME. First, we prove that essentially all ME models admit Hopf bifurcations for certain monotone choices of the interaction functions. Second, we offer a parametrization of equilibria Jacobians of ME systems where few interactions are not in mass action form. Third, for a quite general class of models, we show that periodic oscillations in closed systems imply periodic oscillations when demography is added. Finally, we apply such results to two families of networks: a general SIR model with a nonlinear force of infection and treatment rate and a recent SIRnS model with a gradual increase in infectiousness. We give both necessary conditions and sufficient conditions for the occurrence of bifurcations at endemic equilibria of both families.
In this paper, a mathematical model is developed to describe the evolution of the concentration of compounds through a gas chromatography column. The model couples mass balances and kinetic equations for all components. Both single and multiple-component cases are considered with constant or variable velocity. Non-dimensionalisation indicates the small effect of diffusion. The system where diffusion is neglected is analysed using Laplace transforms. In the multiple-component case, it is demonstrated that the competition between the compounds is negligible and the equations may be decoupled. This reduces the problem to solving a single integral equation to determine the concentration profile for all components (since they are scaled versions of each other). For a given analyte, we then only two parameters need to be fitted to the data. To verify this approach, the full governing equations are also solved numerically using the finite difference method and a global adaptive quadrature method to integrate the Laplace transformation. Comparison with the Laplace solution verifies the high degree of accuracy of the simpler Laplace form. The Laplace solution is then verified against experimental data from BTEX chromatography. This novel method, which involves solving a single equation and fitting parameters in pairs for individual components, is highly efficient. It is significantly faster and simpler than the full numerical solution and avoids the computationally expensive methods that would normally be used to fit all curves at the same time.
Pathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing plays a critical role in disease control and transmission prevention. Group testing is a well-established method for reducing the number of tests needed to screen large populations when the disease prevalence is low. However, it does not fully utilize the quantitative information provided by qPCR methods, nor is it able to accommodate a wide range of pathogen loads. To address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to efficiently screen populations via two-stage qPCR testing. The SQGT method quantizes cycle threshold ($Ct$) values into multiple bins, leveraging the information from the first stage of screening to improve the detection sensitivity. Dynamic $Ct$ threshold adjustments mitigate dilution effects and enhance test accuracy. Comparisons with traditional binary outcome GT methods show that SQGT reduces the number of tests by $24$% while maintaining a negligible false negative rate.
This study uses the Problem Based Learning (PBL) learning model. The purpose of this study is to determine the level of students' mathematical literacy skills before and after it is applied to students of SMPIT Insan Harapan. This research method is quantitative and the type of research is Quasi Experiment with the research design of One Group Pretest-Post-test Design. The analysis technique of this research is descriptive statistics and inferential analysis statistics. The results of this study indicate that by applying the PBL learning model there is an increase in the mathematical literacy ability of SMPIT Insan Harapan students. % in the medium category. Then the percentage increased after the PBL learning model was applied, namely 20% in the medium category and 90% in the high category. The results of inferential statistical analysis (Paired Sample T-test) obtained a significant value <0.05, which means, there is an increase in students' mathematical literacy skills after the application of the PBL learning model in class VII D SMPIT Insan Harapan.
Students' mathematical abilities are still considered low due to the lack of students' mathematical connection abilities. One effort that can be done in overcoming the low ability of students' mathematical connections is to involve the culture around students in the learning process. This study aims to analyze students' mathematical connection ability through the application of learning strategies based on local wisdom a'bulo sibatang, assamaturu, mappesabbi and sipakatau. The research method used is a quantitative quasi-experimental type of nonequivalent control group design. Through purposive sampling technique, it was obtained class XI MIPA 3 (experimental class) and XI MIPA 1 (control class). The instrument used is a test to indicators of mathematical connection ability. The results showed an increase in indicator of mathematical connection ability in the experimental class, namely 88.52% indicator I, 85.35% indicator II and 83.87% indicator III. Meanwhile, in the control class applied problem-based learning strategies only got a score of 65%. Based on the results of the analysis, it can be concluded that local wisdom-based learning in the experimental class is able to improve students' mathematical connection ability better than the control class that applies problem-based learning.
Hamidreza Naderian, Moe M. S. Cheung, Elena Dragomirescu
et al.
This paper proposes an efficient numerical technique for simulating hybrid fiber-reinforced polymer (FRP) bridge systems. An integrated finite strip method (IFSM) is proposed to evaluate the free vibration performance of cable-stayed FRP bridges. The structural performance of the ultra-long span cable-stayed bridge (ULSCSB) is totally different than steel and concrete bridge structures due to the complexity of the mechanical behavior of the FRP deck. Herein, the anisotropic nature of the FRP laminated deck is considered in the analysis by introducing so-called laminate spline strips in the integrated finite strip solution. The structural interactions between all the components of the bridge can be handled using the proposed method. Column strips and cable strips are introduced and used to model the towers and cables, respectively. In addition, a straightforward scheme for modeling boundary conditions is developed. A case study is presented through which the accuracy and efficiency of the IFSM in modeling such structures, as well as in performing natural frequency analysis of long-span cable-stayed FRP bridges, are evaluated. The finite strip results are verified against the finite element analysis, and a significant enhancement in efficiency in terms of reduction in computational cost is demonstrated with the same level of accuracy.
With the emergence of energy communities, where a number of prosumers invest in shared generation and storage, the issue of fair allocation of benefits is increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings - however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we first conduct a systematic review of the literature on the use of Shapley value in energy-related applications, as well as efforts to compute or approximate it. Next, we formalise the main methods for approximating the Shapley value in community energy settings, and propose a new one, which we call the stratified expected value approximation. To compare the performance of these methods, we design a novel method for exact Shapley value computation, which can be applied to communities of up to several hundred agents by clustering the prosumers into a smaller number of demand profiles. We perform a large-scale experimental comparison of the proposed methods, for communities of up to 200 prosumers, using large-scale, publicly available data from two large-scale energy trials in the UK (UKERC Energy Data Centre, 2017, UK Power Networks Innovation, 2021). Our analysis shows that, as the number of agents in the community increases, the relative difference to the exact Shapley value converges to under 1% for all the approximation methods considered. In particular, for most experimental scenarios, we show that there is no statistical difference between the newly proposed stratified expected value method and the existing state-of-the-art method that uses adaptive sampling (O'Brien et al., 2015), although the cost of computation for large communities is an order of magnitude lower.
Christoffer Fyllgraf Christensen, Fengwen Wang, Ole Sigmund
Much work has been done in topology optimization of multiscale structures for maximum stiffness or minimum compliance design. Such approaches date back to the original homogenization-based work by Bendsøe and Kikuchi from 1988, which lately has been revived due to advances in manufacturing methods like additive manufacturing. Orthotropic microstructures locally oriented in principal stress directions provide for highly efficient stiffness optimal designs, whereas for the pure stiffness objective, porous isotropic microstructures are sub-optimal and hence not useful. It has, however, been postulated and exemplified that isotropic microstructures (infill) may enhance structural buckling stability but this has yet to be directly proven and optimized. In this work, we optimize buckling stability of multiscale structures with isotropic porous infill. To do this, we establish local density dependent Willam-Warnke yield surfaces based on local buckling estimates from Bloch-Floquet-based cell analysis to predict local instability of the homogenized materials. These local buckling-based stress constraints are combined with a global buckling criterion to obtain topology optimized designs that take both local and global buckling stability into account. De-homogenized structures with small and large cell sizes confirm validity of the approach and demonstrate huge structural gains as well as time savings compared to standard singlescale approaches.
This study aims to analyze the effectiveness of the Smart card-assisted Brain-Based Learning model against mathematical critical thinking skills, to analyze whether the smart card-assisted Brain-Based Learning model is more effective than the Treffinger model for mathematical critical thinking skills, and to describe students' mathematical critical thinking skills in terms of mathematical anxiety. This research applied quantitative method followed by a description. The population in this research were students of the seventh-grades on three of Junior High School in Ungaran in the academic year 2019/2020. This research applied the cluster random sampling technique as the technique for collecting the data. The subject of the research was selected from the experimental class. The researcher was used purposive sampling technique to select the subject of this research. The data was collected by using the test method, the questionnaire method, and the interview method. The quantitative data analysis used classical completeness test, mean difference test, and proportion difference test. The result of this study indicate that smart card-assisted Brain-Based Learning model is effective for mathematical critical thinking skills, but smart card-assisted Brain-Based Learning model is no more effective than Treffinger on mathematical critical thinking skills. In addition, the description of mathematical critical thinking skills in terms of mathematical anxiety is obtained that: (1) subjects with low mathematical anxiety are able to achieve the indicator (A) clarification well, indicator (B) assessments well, indicator (C) concludes well, and indicator (D) strategy well; (2) subjects with moderate mathematical anxiety are able to achieve indicator (A) well, indicator (B) well, indicator (C) quite well, and indicator (D) quite well; (3) subjects with high mathematical anxiety are able to achieve indicator (A) well, indicator (B) is quite good, indicator (D) is quite good.
Andrea Ponti, Antonio Candelieri, Ilaria Giordani
et al.
Abstract The issue of vulnerability and robustness in networks have been addressed by several methods. The goal is to identify which are the critical components (i.e., nodes/edges) whose failure impairs the functioning of the network and how much this impacts the ensuing increase in vulnerability. In this paper we consider the drop in the network robustness as measured by the increase in vulnerability of the perturbed network and compare it with the original one. Traditional robustness metrics are based on centrality measures, the loss of efficiency and spectral analysis. The approach proposed in this paper sees the graph as a set of probability distributions and computes, specifically the probability distribution of its node to node distances and computes an index of vulnerability through the distance between the node-to-node distributions associated to original network and the one obtained by the removal of nodes and edges. Two such distances are proposed for this analysis: Jensen–Shannon and Wasserstein, based respectively on information theory and optimal transport theory, which are shown to offer a different characterization of vulnerability. Extensive computational results, including two real-world water distribution networks, are reported comparing the new approach to the traditional metrics. This modelling and algorithmic framework can also support the analysis of other networked infrastructures among which power grids, gas distribution and transit networks.