Hasil untuk "Applied mathematics. Quantitative methods"

Jacob Cohen, P. Cohen, S. West et al.

M. Malik

Muhammad Faisal, Talha Anwar, Farah Javed

Efficient thermal management is vital in modern mechanical and energy systems, where conventional engine oils often exhibit limited heat transfer capabilities. This study investigates the enhancement of thermal convection in engine oil by dispersing molybdenum tetrasulfide nanoparticles (MoS₄) to form a high-performance nanofluid. The natural convection behavior of this nanofluid is analyzed within a square porous cavity featuring uniformly heated horizontal walls and isothermally cooled vertical walls. The governing equations are developed using scaling variables and the Boussinesq approximation and solved numerically through the finite element method. The effects of nanoparticle volume fraction (0–0.07), Rayleigh number (103–10⁶), and Darcy number (10⁻⁵–10⁻²) are systematically examined. Results show that increasing the MoS₄ nanoparticle concentration substantially enhances convective heat transfer, with the average Nusselt number rising by up to 28 % and the peak stream function reaching 17.0 at a volume fraction of 0.07 under low Darcy and Rayleigh conditions. These findings demonstrate that even minimal nanoparticle addition can significantly improve the heat transport capability of engine oils in porous enclosures. The study introduces a novel combination of molybdenum tetrasulfide-based nanofluids and porous media analysis, extending beyond prior work by quantifying the coupled effects of nanoparticle concentration and porous resistance on buoyancy-driven flow performance.

Chirath Dasanayaka, Kanishka Dandeniya, Maheshi B. Dissanayake et al.

Access to high-quality dental healthcare remains a challenge in many countries due to limited resources, lack of trained professionals, and time-consuming report generation tasks. An intelligent clinical decision support system (ICDSS), which can make informed decisions based on past data, is an innovative solution to address these shortcomings while improving continuous patient support in dental healthcare. This study proposes a viable solution with the aid of multimodal artificial intelligence (AI) and large language models (LLMs), focusing on their application for generating orthopantomography radiology reports and answering questions in the dental domain. This work also discusses efficient adaptation methods of LLMs for specific language and application domains. The proposed system primarily consists of a Blip-2-based caption generator tuned on DPT images followed by a Llama 3 8B based LLM for radiology report generation. The performance of the entire system is evaluated in two ways. The diagnostic performance of the system achieved an overall accuracy of 81.3%, with specific detection rates of 87.9% for dental caries, 89.7% for impacted teeth, 88% for bone loss, and 81.8% for periapical lesions. Subjective evaluation of AI-generated radiology reports by certified dental professionals demonstrates an overall accuracy score of 7.5 out of 10. In addition, the proposed solution includes a question-answering platform in the native Sinhala language, alongside the English language, designed to function as a chatbot for dental-related queries. We hope that this platform will eventually bridge the gap between dental services and patients, created due to a lack of human resources. Overall, our proposed solution creates new opportunities for LLMs in healthcare by introducing a robust end-to-end system for the automated generation of dental radiology reports and enhancing patient interaction and awareness.

Xiaopeng Xi, Xiaosheng Si, Yichun Niu et al.

Timely prognostics of remaining useful life (RUL) are increasingly critical for engineering systems, especially as long-life components face complex and evolving degradation risks. Nevertheless, conventional degradation models are frequently inadequate in capturing the memory effects and latent global state dependencies inherent in practical degradation processes. These limitations hinder the generalizability of existing methods. To overcome these challenges, this paper proposes a class of nonlinear degradation models that explicitly incorporate generalized spatiotemporal dependencies and memory effects among multiple similar components. The models are formulated using continuous stochastic differential equations and discretized via two numerical schemes to enable efficient parameter estimation through maximum likelihood (ML) methods. Subsequently, RUL predictions are derived using Monte Carlo simulation, with point estimates extracted from the resulting frequency histograms. The proposed method is validated through a numerical example and a blast furnace case study.

Luiz Gabriel Correia, Jesús P. Mena-Chalco

Abstract The manipulation of the Impact Factor (IF) through editorial decisions inflating self-citations is a growing concern in academic publishing. This work introduces an agent-based model simulating journals as rational agents competing for IF ranking positions in a zero-sum game, generating synthetic citation networks that reproduce key patterns such as the Matthew Effect and the specialization of manipulation strategies across interconnected disciplines. By comparing IF calculation policies with and without self-citations, statistical analysis of simulation results demonstrates that excluding self-citations significantly reduces incentives for manipulative tactics, offering computational evidence for developing policies that promote scientific integrity within the academic publishing ecosystem.

James A. Hay, I. Routledge, Saki Takahashi

We present a review and primer of methods to understand epidemiological dynamics and identify past exposures from serological data, referred to as serodynamics. We discuss processing and interpreting serological data prior to fitting serodynamical models, and review approaches for estimating epidemiological trends and past exposures, ranging from serocatalytic models applied to binary serostatus data, to more complex models incorporating quantitative antibody measurements and immunological understanding. Although these methods are seemingly disparate, we demonstrate how they are derived within a common mathematical framework. Finally, we discuss key areas for methodological development to improve scientific discovery and public health insights in seroepidemiology.

Subhajit Panda, B. Nayak, Rupa Baithalu et al.

In biomedical engineering, the behavior of gyrotactic microorganisms with non-Newtonian fluids such as tangent hyperbolic fluids improve the design of targeted drug delivery systems. In this system control over microorganism movement is essential. The present study deals with the synergistic influence of gyrotactic microorganisms and bimolecular reactions on the bidirectional flow of tangent hyperbolic fluids under Nield boundary conditions. Further, the flow characteristic of the non-Newtonian fluid is enhanced by incorporating the impact of thermal radiation, heat sources, Brownian motion, and thermophoresis. The presentation of these phenomena is vital for an extensive range of applications, including industrial processes, biomedical engineering, and environmental management. The analysis employs advanced mathematical modeling which needs suitable transformation rules to get the non-dimensional form and further numerical simulation is presented with the assistance of the “shooting-based fourth-order Runge–Kutta technique”. The results are depicted for the several contributing factors via the built- in-house function bvp4c in “MATLAB”. The authentication of the study with the prior research is a benchmark to precede further research in this direction. However, the outstanding results are; the fluid velocity is controlled by increasing non-Newtonian Weissenberg number whereas the velocity slip shows dual characteristics on the axial velocity distribution. Further, the motile microorganism profile is controlled by the enhanced bioconvection Lewis number.

Gianluigi Rozza, Oliver Schütze, Nicholas Fantuzzi

This Special Issue comprises the second collection of papers submitted by both the Editorial Board Members (EBMs) of the journal <i>Mathematical and Computational Applications</i> (<i>MCA</i>) and the outstanding scholars working in the core research fields of <i>MCA</i> [...]

Abdulsalam Elnaeem Balila, Ani Bin Shabri

IntroductionPredicting property prices is a crucial task in the real estate market, and machine learning algorithms offer valuable tools for accurate predictions. In this study, we introduce a comprehensive comparison of eight well-known machine learning algorithms, namely, ensemble empirical mode decomposition (EEMD)–stochastic (S) + deterministic (D)–support vector machine (EEMD-SD-SVM), support vector machine (SVM), gradient boosting, random forest, K-nearest neighbors (KNN), linear regression, artificial neural networks (ANN), and decision trees. The focus is on predicting property prices in Dubai, with the primary objective of assessing the predictive performance of these algorithms within this specific market context.MethodsThe evaluation is based on four key performance metrics: R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into prediction errors, accuracy in percentage terms, and the proportion of variance in property prices explained by independent variables. The study compares the strengths and limitations of each algorithm for predicting property prices in Dubai, highlighting scenarios where certain algorithms excel based on the nature of decision boundaries, handling complex data, capturing localized patterns, and offering interpretability.ResultsFindings from the comparative analysis shed light on the performance of each algorithm in predicting property prices in Dubai. EEMD-SD-SVM and SVM excel in scenarios requiring precise decision boundaries, while gradient boosting and random forests demonstrate robust performance with complex and noisy property price data. KNN captures localized patterns effectively, linear regression is suitable for straightforward regression tasks, ANN excels with extensive datasets, and decision trees offer interpretability in understanding factors influencing property prices.DiscussionThe study emphasizes the significance of model tuning, feature selection, and data pre-processing to enhance predictive power. Additionally, practical aspects such as computational efficiency, model interpretability, and scalability in real-world applications are discussed. The comparative analysis provides valuable guidance for stakeholders, including real estate professionals, data scientists, and stakeholders interested in selecting the most suitable machine learning algorithm for predicting property prices in Dubai, with a focus on the essential evaluation metrics of MSE, RMSE, MAPE, and R2. This study offers insights into the applicability and performance of different machine learning algorithms for predicting property prices in Dubai. Stakeholders such as real estate agents, buyers, sellers, or investors can leverage these insights to make informed decisions in the Dubai real estate market.

Sachin Kumar, Akhil Gupta, Ranjit Kumar Bindal

Load Frequency Control (LFC) and Voltage Control (VC) are critical aspects of hybrid generation systems. In this work, the performance comparison of three different control approaches for LFC and VC: Genetics Algorithm (GA)-tuned Proportional Integral Differentiator (PID), Particle Swarm Optimization (PSO)-PID, and a conventional PID controller is presented. Especially, the performance is assessed and analyzed for convergence speed and computational complexity for each approach. Mathematical framework for each approach is discussed, including the required equations for hybrid generation system. It is reported that the traditional PID controller exhibits fast convergence due to its direct adjustment of control parameters. Simulation results reveal that it requires manual tuning and has low computational complexity. In contrast, the GA-PID utilizes a GA optimization process which automatically tunes the PID gains. Although, it may require multiple generations to converge to the optimal solution, however, it offers better control performance. Moreso, it comes at the cost of higher computational complexity compared to the traditional PID controller. In contrast, the PSO-PID employs an algorithm for parameter optimization. It converges faster than the GA-PID but still requires more iterations than the traditional PID controller. Similar to the GA-PID, it has higher computational complexity due to fitness function evaluation and particle updates. The optimization results provide insights into the convergence speed and computational complexity trade-offs between the three control approaches. Practitioners in the field of hybrid energy systems can utilize the outcomes to make informed decisions based on their specific requirements and available computational resources.

Riski Aspriyani, Nur'aini Muhassanah

This research aims to determine a prediction model using Simple Linear Regression for time series data on the number of elementary school students in Cilacap from 2010 to 2023 and to obtain predicted results on the number of elementary school students in Cilacap for the following year. The data pattern of the number of elementary school students in Cilacap is known to have a decreasing trend. The time series data was subjected to the Durbin-Watson test to see whether there was autocorrelation. It was found that data on the number of elementary school students in Cilacap from 2010 to 2023 did not have autocorrelation with the Durbin-Watson (d) computing value of 1.385. The requirements for time series data have been met, so that forecasting analysis can be carried out using Simple Linear Regression and it is found that the regression equation is y ̂=168698.604-1600.519x. This regression equation is used to predict the value of the number of elementary school students in Cilacap for the next year. The forecasting accuracy level is 97.303% or with a MAPE error value of 2.697%, which means that the ability of the regression model to predict is very accurate. Thus, the predicted data on the number of elementary school students in Cilacap for the next period in 2024 is 144690 students. Keywords: Forecasting, Time Series, Linear Regression

Rosa Martha López-gutiérrez, Lılıana Cardoza Avendaño, Ana Medina et al.

In the present work a detailed study is presented, on the design, programming, and investigation of the behavior of flocking (Movement type flock), through the model of BOIDS, for its acronym in English "Bird Oid Object" (Object type bird), which was devised by Craig Reynolds in 1986. This complex flocking behavior that occurs arises from the interaction of simple local rules, in which complexity and sensitivity to initial conditions are present. A measure of chaotic compound will be introduced to the algorithm by means of a new four-dimensional autonomous hyperchaotic system based on the 3D Méndez-Arellano-Cruz-Martínez (MACM) system. The measures proposed herein, therefore, may have the potential to predict, control, and exemplify the behavior of group intelligence study systems that occur in nature, allowing the implementation of these systems in groups of robots through the implementation of hyperchaotic trajectories in the future, to obtain greater speed and efficiency, obstacle and collisions avoidance in their flights.

Deheng Kong, C. Saroglou, Faquan Wu et al.

Abstract Remote sensing techniques (e.g., terrestrial laser scanning and digital photogrammetry) have been developed and applied rapidly in recent years for identification and analysis of rock mass characteristics. A direct validation of these digital measurements against well-constrained datasets obtained from conventional survey methods is reported here. A high-resolution digital outcrop model (DOM) generation method for rock exposures based on unmanned aerial vehicle (UAV) photogrammetry integrated with a structure from-motion (SfM) technique was introduced. A digital procedure based on mathematical algorithms for discontinuity detection, trace mapping and quantitative discontinuity characterization was established. The proposed method was applied to two rock slopes (from Greece and China) and fundamental discontinuity parameters, i.e., the orientation, number of sets, trace length, set spacing, linear frequency, areal frequency and areal intensity were statistically extracted and calculated. Their values were compared with carefully planned manual measurements, i.e., individual discontinuity measurements, scanline method, and window sampling method on the same sites. The results of two sites showed that most deviations are less than 5° for dip direction and dip angle, 0.13 m for exposed discontinuity length, 0.37 m for set spacing, 0.09 m -1 for linear frequency, 0.06 m-2 for areal frequency, and 0.15 m-1 for areal intensity. These deviations are reasonable and acceptable, which confirmed the reliability and accuracy of the developed method. With improved efficiency and workability comparing to conventional methods especially in difficult surveying environments, the UAV-SfM Photogrammetry can potentially become a routine method for on-site rock mass characterization .

Johannes C. Joubert, Daniel N. Wilke, Patrick Pizette

This paper presents a GPU-based, incompressible, multiphase generalized finite difference solver for simulating multiphase flow. The method includes a dampening scheme that allows for large density ratio cases to be simulated. Two verification studies are performed by simulating the relaxation of a square droplet surrounded by a light fluid and a bubble rising in a denser fluid. The scheme is also used to simulate the collision of binary droplets at moderate Reynolds numbers (250–550). The effects of the surface tension and density ratio are explored in this work by considering cases with Weber numbers of 8 and 180 and density ratios of 2:1 and 1000:1. The robustness of the multiphase scheme is highlighted when resolving thin fluid structures arising in both high and low density ratio cases at We = 180.

Hong Lai Zhu

This paper uses Z transformations to obtain the general solutions of a large number of second-order, third-order and fourth-order linear partial differential equations for the first time, including one-dimensional inhomogeneous wave equation. Using general solutions, we have obtained plenty of exact solutions of the typical definite solution problems, which proves the important value of general solutions. We propose the Z4transformation for the first time and initially use it to solve a specific case. We successfully obtain the Fourier series solution using the series general solution of the one-dimensional homogeneous wave equation, which successfully solves a famous unresolved debate in the history of mathematics.

Tim Moser, Boris Lohmann

ABSTRACTWe present a new structure-preserving model order reduction (MOR) framework for large-scale port-Hamiltonian descriptor systems (pH-DAEs). Our method exploits the structural properties of the Rosenbrock system matrix for this system class and utilizes condensed forms which often arise in applications and reveal the solution behaviour of a system. Provided that the original system has such a form, our method produces reduced-order models (ROMs) of minimal dimension, which tangentially interpolate the original model’s transfer function and are guaranteed to be again in pH-DAE form. This allows the ROM to be safely coupled with other dynamical systems when modelling large system networks, which is useful, for instance, in electric circuit simulation.

Sze Hui Sim, M. E. M. Mohd Matore

Grasha–Riechmann Teaching Styles have a high potential to be applied in Mathematics especially to help increase teacher educators’ knowledge. However, very little attention has been paid to the study of identifying the teaching style patterns of Mathematics teachers at the primary school National-Type Chinese Primary Schools or Sekolah Jenis Kebangsaan Cina SJKC. There is increasing concern about how this teaching style related to the teaching experience. This study aims to identify the patterns of Grasha–Riechmann Teaching Styles among primary school Mathematics teachers and the relationship between Grasha–Riechmann Teaching Styles with teaching experience. The quantitative approach through a survey was applied to 97 Mathematics teachers of SJKC Kepong, Kuala Lumpur using the simple random sampling method. The instrument was adapted from the Grasha–Riechmann Teaching Styles Questionnaire (1996), which measures five teaching styles such as Personal Model Teaching Style, Expert Teaching Style, Formal Authority Teaching Style, Delegator Teaching Style, and Facilitator Teaching Style. The patterns showed that the Personal Model Teaching Style is the most dominant, and the Facilitator Teaching as the least dominant style. The Spearman’s Rho Correlation also reported a very weak significant correlation between Grasha–Riechmann Teaching Styles with the teachers’ Mathematics teaching experience, specifically for Expert, Formal Authority, and Facilitator Teaching Styles. The study provides practical implications for educators’ professional development to diversify the training of teachers by experience and adapt them to the needs of student learning in primary school. These findings trigger ideas to get a better understanding by other demographic variables such as gender, age, and complexity of Mathematics subject.

Markus Vogl

This study provides a holistic and quantitative overview of over 800 mathematical methods (e.g., financial and risk models, statistical tests, statistics and advanced algorithms) taken out of sampled scientific literature on quantitative modelling, particularly, from financial and risk modelling by applying a bibliometric approach from 2008 to 2019 and a citation network analysis. This is done to elaborate on the influence in the field after the Financial Crisis 2008. We present a content analysis of journals, main topics, applied data sets and frontiers within quantitative modelling and highlight details about quantitative features such as implemented models, algorithms and aggregated model-family combinations. Moreover, we describe explications and ties to empirical stylised facts (e.g., asymmetry or nonlinearity). Finally, we discuss insights such as our main finding, namely, the non-existence of a “single-best”-approach as well as the future prospects.

Hongyu Zhu, Jun-Li Li, X. Deng et al.

Purpose. Recent studies suggest ultra-high dose rate (FLASH) irradiation can spare normal tissues from radiotoxicity, while efficiently controlling the tumor, and this is known as the ‘FLASH effect’. This study performed theoretical analyses about the impact of radiolytic oxygen depletion (ROD) on the cellular responses after FLASH irradiation. Methods. Monte Carlo simulation was used to model the ROD process, determine the DNA damage, and calculate the amount of oxygen depleted (L ROD) during FLASH exposure. A mathematical model was applied to analyze oxygen tension (pO2) distribution in human tissues and the recovery of pO2 after FLASH irradiation. DNA damage and cell survival fractions (SFs) after FLASH irradiation were calculated. The impact of initial cellular pO2, FLASH pulse number, pulse interval, and radiation quality of the source particles on ROD and subsequent cellular responses were systematically evaluated. Results. The simulated electron L ROD range was 0.38–0.43 μM Gy−1 when pO2 ranged from 7.5 to 160 mmHg. The calculated DNA damage and SFs show that the radioprotective effect is only evident in cells with a low pO2. Different irradiation setups alter the cellular responses by modifying the pO2. Single pulse delivery or multi-pulse delivery with pulse intervals shorter than 10–50 ms resulted in fewer DNA damages and higher SFs. Source particles with a low linear energy transfer (LET) have a higher capacity to deplete oxygen, and thus, lead to a more conspicuous radioprotective effect. Conclusions. A systematic analysis of the cellular response following FLASH irradiation was performed to provided suggestions for future FLASH applications. The FLASH radioprotective effect due to ROD may only be observed in cells with a low pO2. Single pulse delivery or multi-pulse delivery with short pulse intervals are suggested for FLASH irradiation to avoid oxygen tension recovery during pulse intervals. Source particles with low LET are preferred for their conspicuous radioprotective effects.