This study investigates the effects of viscous dissipation, Joule heating, and a heat source on the magnetohydrodynamic flow of an Eyring-Powell fluid over a convectively heated stretched sheet with convective boundary conditions. The fluid flows over a surface heated by a hot fluid from beneath through convection. The fluid's viscosity follows a Reynolds model, while its thermal conductivity varies linearly with temperature. By applying the standard similarity method, the governing nonlinear partial differential equations are transformed into ordinary differential equations. These equations are then solved numerically using the Spectral Quasi-Linearization Method (SQLM). The study presents and discusses graphical results that highlight the key fluid variables influencing velocity and temperature profiles. The findings of this research could have significant implications for various engineering applications, such as heat exchanges, chemical reactors, and thermal management systems.
The problem of unsteady MHD flow of the nanofluids past an exponentially accelerated vertical plate embedded in a permeable medium is investigated. The three fluids are assumed to be electrically conducting water-based nanofluids. Thermal radiation and electromagnetic field impacts are taken into account. The resultant nondimensionalized boundary value problem is solved numerically using the finite difference method used in the procurement numerical results of the governing equations. The comparisons of velocity and temperature fields of Ethylene glycol-water and Fe3O4-Water are presented graphically. The velocity of Ethylene glycol-water is greater than that of Fe3O4-Water. The temperature of Ethylene glycol-water is greater than the temperature of Fe3O4-Water.
This study presents a novel deep learning framework for classifying visual images based on brain responses recorded through electroencephalogram (EEG) signals. The primary challenge in EEG-based visual pattern recognition lies in the inherent spatiotemporal variability of neural signals across different individuals and recording sessions, which severely limits the generalization capabilities of classification models. Our work specifically addresses the task of identifying which image category a person is viewing based solely on their recorded brain activity. The proposed methodology incorporates three primary components: first, a brain hemisphere asymmetry-based dimensional reduction approach to extract discriminative lateralization features while addressing high-dimensional data constraints; second, an advanced channel selection algorithm utilizing Fisher score methodology to identify electrodes with optimal spatial representativeness across participants; and third, a Dynamic Temporal Warping (DTW) alignment technique to synchronize temporal signal variations with respect to selected reference channels. Comprehensive experimental validation on a visual image classification task using a publicly available EEG-based visual classification dataset, ImageNet-EEG, demonstrates that the proposed disambiguation framework substantially improves classification accuracy while simultaneously enhancing model convergence characteristics. The integrated approach not only outperforms individual component implementations but also accelerates the learning process, thereby reducing training data requirements for EEG-based applications. These findings suggest that systematic spatiotemporal disambiguation represents a promising direction for developing robust and generalizable EEG classification systems across diverse neurological and brain–computer interface applications.
Anshu Kumar, Kemi Olimba, Vyacheslav Kungurtsev
et al.
Computational fluid dynamics (CFD) has become a cornerstone of modern water engineering, providing quantitative tools for the analysis, prediction, and management of complex hydraulic systems across a wide range of spatial and temporal scales. This survey reviews the mathematical models and numerical methods that underpin CFD applications in water engineering, from depth-averaged formulations such as the shallow water equations to fully three-dimensional Navier-Stokes models, as well as selected alternative modeling approaches. We examine the historical development of these models, their mathematical structure, and the numerical discretization and solution strategies commonly employed in practice, including finite difference, finite volume, and finite element methods. Beyond core solver technology, the survey addresses practical modeling issues such as source-term treatment, wetting and drying, turbulence modeling, free-surface representation, and computational efficiency. The growing role of data integration is also discussed, encompassing data assimilation, uncertainty quantification, and emerging machine-learning-assisted approaches that complement physics-based solvers. To illustrate the impact of modeling and numerical choices on real-world applications, representative case studies from large-scale water management systems are reviewed. By integrating theory, numerical techniques, and applied perspectives, this survey provides a unified reference for researchers and practitioners seeking to understand both the foundational principles and contemporary challenges of CFD in water engineering.
Nantogmah Abdulai Sualey, Philip N. A. Akuka, Baba Seidu
et al.
This study delves into the often-overlooked impact of immature mosquitoes on the dynamics of malaria transmission. By employing a mathematical model, we explore how these aquatic stages of the vector shape the spread of the disease. Our analytical findings are corroborated through numerical simulations conducted using the Runge–Kutta fourth-order method in MATLAB. Our research highlights a critical factor in malaria epidemiology: the basic reproduction number R0. We demonstrate that when R0 is below unity R0<1, the disease-free equilibrium exhibits local asymptotic stability. Conversely, when R0 surpasses unity R0>1, the disease-free equilibrium becomes unstable, potentially resulting in sustained malaria transmission. Furthermore, our analysis covers equilibrium points, stability assessments, bifurcation phenomena, and sensitivity analyses. These insights shed light on essential aspects of malaria control strategies, offering valuable guidance for effective intervention measures.
Nabeela Anwar, Iftikhar Ahmad, Adiqa Kausar Kiani
et al.
The most important factor for increasing crop production is pest and pathogen resistance, which has a major impact on global food security. Pest management also emphasizes the need for farming awareness. A high crop yield is ultimately achieved by protecting crops from pests and raising public awareness of the devastation caused by pests. In this research, we aim to investigate the intricate impacts of nonlinear delayed systems for managing crop pest management (CPM) supervised by Ordinary Differential Equations (ODEs). Our focus will be on highlighting the intricate and often unpredictable relationships that occur over time among crops, pests, strategies for rehabilitation, and environmental factors. The nonlinear delayed CPM model incorporated the four compartments: crop biomass density [B(t)], susceptible pest density [S(t)], infected pest density [I(t)], and population awareness level [A(t)]. The approximate solutions for the four compartments B(t), S(t), I(t), and A(t) are determined by the implementation of sundry scenarios generated with the variation in crop biomass growth rate, rate of pest attacks, pest natural death rate, disease associated death rate and memory loss of aware people, by means of exploiting the strength of the Adams (ADS) and explicit Runge-Kutta (ERK) numerical solvers. Comparative analysis of the designed approach is carried out for the dynamic impacts of the nonlinear delayed CPM model in terms of numerical outcomes and simulations based on sundry scenarios.
Organoids are self-organized 3D cell clusters that closely mimic the architecture and function of in vivo tissues and organs. Quantification of organoid morphology helps in studying organ development, drug discovery, and toxicity assessment. Recent microscopy techniques provide a potent tool to acquire organoid morphology features, but manual image analysis remains a labor and time-intensive process. Thus, this paper proposes a comprehensive pipeline for microscopy analysis that leverages the SegmentAnything to precisely demarcate individual organoids. Additionally, we introduce a set of morphological properties, including perimeter, area, radius, non-smoothness, and non-circularity, allowing researchers to analyze the organoid structures quantitatively and automatically. To validate the effectiveness of our approach, we conducted tests on bright-field images of human induced pluripotent stem cells (iPSCs) derived neural-epithelial (NE) organoids. The results obtained from our automatic pipeline closely align with manual organoid detection and measurement, showcasing the capability of our proposed method in accelerating organoids morphology analysis.
After the Smart City initiative was put forward, cities all over the world started the pilot practice of developing Smart Cities. This triggered a series of thoughts: what is a Smart City, how do we determine the scope of work of a Smart City, and how do we formulate a new strategic agenda of the Smart City to make city smarter and more sustainable? The answer lies not only in finding Smart City solutions, but also leads to the research on the definition of Smart City terminology and the determination of corresponding tasks. Stakeholders of Smart City (e.g., policy makers, municipalities, solution providers, industry, and academia) develop technical and management standards for these tasks jointly. This paper reports the standardization planning on Smart City by the international standardization development organizations (SDOs), that is, the standardization framework of Smart City. It also presents one of the important aspects, namely, the progress of standardization activities on urban infrastructure that are being carried out by the International Telecommunication Union (ITU) via its Study Group 20, in supporting the adoption of information and communication technologies (ICTs) in Smart City. These standards include the classification of urban infrastructure, the interoperability between urban infrastructure and smart city platforms, and the requirements of detailed infrastructure from the perspective of ICT and the Internet of things (IoT). This paper also provides the use cases of application of some standards in global cities.
Objective: Quantitative technique based on In-line phase-contrast computed tomography with single scanning attracts more attention in application due to the flexibility of the implementation. However, the quantitative results usually suffer from artifacts and noise, since the phase retrieval and reconstruction are independent ("two-steps") without feedback from the original data. Our goal is to develop a method for material quantitative imaging based on a priori information specifically for the single-scanning data. Method: An iterative method that directly reconstructs the refractive index decrement delta and imaginary beta of the object from observed data ("one-step") within single object-to-detector distance (ODD) scanning. Simultaneously, high-quality quantitative reconstruction results are obtained by using a linear approximation that achieves material decomposition in the iterative process. Results: By comparing the equivalent atomic number of the material decomposition results in experiments, the accuracy of the proposed method is greater than 97.2%. Conclusion: The quantitative reconstruction and decomposition results are effectively improved, and there are feedback and corrections during the iteration, which effectively reduce the impact of noise and errors. Significance: This algorithm has the potential for quantitative imaging research, especially for imaging live samples and human breast preclinical studies.
In this paper, we generalize the typical geometric constants of Banach spaces to modular spaces. We study the equivalence between the convexity of modular and normed spaces, and obtain the relationship between ρ-Neumann-Jordan constant and ρ-James constant. In particular, we extend the convexity and smoothness modular, and obtain the criterion theorems of the uniform convexity and strict convexity.
We give two new global and algorithmic constructions of the reproducing kernel Hilbert space associated to a positive definite kernel. We further present a general positive definite kernel setting using bilinear forms, and we provide new examples. Our results cover the case of measurable positive definite kernels, and we give applications to both stochastic analysis and metric geometry and provide a number of examples.
In 2000, Sri Lanka designed an ambitious plan for the introduction of information and communication technology (ICT) in most government functions and in the public service delivery (PSD) system in the country. This process started in the early 2000s and gained momentum with several local and internationally funded initiatives. A systematic innovation concept was incorporated within the ICT regime, which ensured bottom-up learning for a smooth transformation from paper to digitized PSD systems. Towards this end, the Information Communication Technology Agency (ICTA) and Lanka Government Network (LGN) were established. ICT incorporation covered the operations of most government agencies and departments to improve governance and PSD. We analyzed the efficiency of the ICT regime to understand its impact on public service employee output as well as on services to the public. We collected service delivery data from both the employees and their clients using a Likert-scale questionnaire. The questionnaire enquired about the utility of the ICT regime introduced in various departments and ministries (DMs) of the Sri Lankan government. This paper analyzes the overall and relative effectiveness of the ICT regime in terms of the inputs incurred and the outcomes realized. First, we calculated the Cronbach’s alpha to test the robustness of the data. Second, we applied ordinal logistics analysis to understand the interrelations among various measures (inputs) and their impacts (outcomes). Finally, we conducted specificity, sensitivity, and predictive value analysis to assess the accuracy of the investigative model. Our findings suggest a positive correlation between the inputs and the outcomes of the ICT regime introduced to digitalize PSD. Our results further indicate that although the inputs and the outcomes are positively corelated, this correlation is not sufficiently strong, and the ICT implementation measures need further emphasis to demonstrate any significant impact on user confidence in this regime.
The operational research paper in the transportation model nowadays is heading to the environmental issue. One of the famous operational research models is transshipment. Transshipment is an expanded model of transportation, in each distribution center between the start to the destination point. In this research, the transshipment model is integrated into an environmental function. The challenge is to find the right shipment of each route from the start, distribution, and destination point considering the transportation cost and carbon emission. This research proposed a transshipment model by minimizing transportation and carbon emission cost using mixed--integer linear programming for model formulation. The solution searching used branch and bound method. This research analyzed the environmental objective function and constrain effect in the transshipment model. The model was tested in a beef distribution case study in Bogor, Indonesia that has eight source points, three distribution centers, and six destination points. The model was experimented using carbon emission limitation scenarios. The optimum result in source allocation, distribution and destination were different between the two scenarios. The carbon emission limitation affected carbon emission production and total cost.
1. Joint Species Distribution models (JSDMs) explain spatial variation in community composition by contributions of the environment, biotic associations, and possibly spatially structured residual covariance. They show great promise as a general analytical framework for community ecology and macroecology, but current JSDMs, even when approximated by latent variables, scale poorly on large datasets, limiting their usefulness for currently emerging big (e.g., metabarcoding and metagenomics) community datasets. 2. Here, we present a novel, more scalable JSDM (sjSDM) that circumvents the need to use latent variables by using a Monte-Carlo integration of the joint JSDM likelihood and allows flexible elastic net regularization on all model components. We implemented sjSDM in PyTorch, a modern machine learning framework that can make use of CPU and GPU calculations. Using simulated communities with known species-species associations and different number of species and sites, we compare sjSDM with state-of-the-art JSDM implementations to determine computational runtimes and accuracy of the inferred species-species and species-environmental associations. 3. We find that sjSDM is orders of magnitude faster than existing JSDM algorithms (even when run on the CPU) and can be scaled to very large datasets. Despite the dramatically improved speed, sjSDM produces more accurate estimates of species association structures than alternative JSDM implementations. We demonstrate the applicability of sjSDM to big community data using eDNA case study with thousands of fungi operational taxonomic units (OTU). 4. Our sjSDM approach makes the analysis of JSDMs to large community datasets with hundreds or thousands of species possible, substantially extending the applicability of JSDMs in ecology. We provide our method in an R package to facilitate its applicability for practical data analysis.
The 6th problem of the 50th International Mathematical Olympiad (IMO), held in Germany, 2009, is called 'the grasshopper problem'. To this problem Kos developed theory from unique viewpoints by reference of Noga Alon's combinatorial Nullstellensatz. We have tried to solve this problem by an original method inspired by a polynomial function that Kos defined, then examined for n=3, 4 and 5. For almost cases the claim of this problem follows, but there remains imperfection due to 'singularity'.
Courses on the mathematics of gambling have been offered by a number of colleges and universities, and for a number of reasons. In the past 15 years, at least seven potential textbooks for such a course have been published. In this article we objectively compare these books for their probability content, their gambling content, and their mathematical level, to see which ones might be most suitable, depending on student interests and abilities. This is not a book review (e.g., none of the books is recommended over others) but rather an essay offering advice about which topics to include in a course on the mathematics of gambling.