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Mathematical Modeling for Evaluating the Mechanical Properties of High Strength Concrete with Natural Zeolite and Additives
The cement manufacturing industry is a major contributor to atmospheric pollution, primarily due to carbon dioxide emissions. Consequently, there is a pressing need to develop eco-friendly concrete capable of mitigating air pollution by sequestering atmospheric carbon dioxide. In this context, the incorporation of Natural Zeolite in concrete has been investigated, as it can absorb environmental carbon dioxide. This study explored the effects of partial cement replacement with Natural Zeolite (5%) and varying percentages of Silica Fume, Metakaolin, and Fly Ash (5%, 10%, and 15%) on the mechanical properties and carbon sequestration potential of High Strength Concrete (HSC). Comprehensive testing was conducted to evaluate the split tensile, compressive, and flexural strengths of the modified HSC. Experimental results indicated that the addition of Natural Zeolite and Metakaolin enhanced the strength of HSC, with Mix 3 displaying a higher 90-day compressive strength compared to the reference mix. The findings suggest that incorporating Natural Zeolite and other supplementary cementitious materials in concrete has the potential to alleviate environmental pollution. The dataset, comprising 900 samples, exhibited no autocorrelation or multicollinearity issues, making it suitable for multiple regression analysis. The statistically significant regression models developed in this study can effectively predict concrete strength. (2023). All Rights Reserved. -
Mathematical Modeling of Concrete Fracture Energy of Notched Specimens Using Experimental Evidence
The tensile stiffness of concrete is an important parameter for crack initiation. The microcrack initiation and propagation regulate the stressstrain behavior and the failure mode of concrete. Therefore, fundamental awareness of the fracture mechanism in terms of fracture energy is a requisite to comprehend concrete behavior. There is research consensus that fracture energy alone does not suffice to characterize the ductility/brittleness and also the size dependency of concrete. Therefore, it is necessary to evaluate the fracture energy and the characteristic length for a realistic assessment of the fracture behavior of concrete. Towards this objective, this study examined the fracture energy of concrete by experimentation, and the fracture energy proposed by various models in the literature. Further, the characteristic length proposed by Hillerborg which depicted both the material influence and the size effect, has been computed. Based on the RILEM50 FM recommendations, 18 specimens with varying grades of concrete and notch depths have been tested and the fracture energy parameters have been evaluated. Also, two regression models with key fracture parameters as variables for two-notch ratios, have been formulated for the concrete fracture energy. The arguments have been supported by experimental evidence. The Author(s), under exclusive licence to Shiraz University 2024. -
Mathematical Modeling of Concrete Fracture Energy of Notched Specimens Using Experimental Evidence
The tensile stiffness of concrete is an important parameter for crack initiation. The microcrack initiation and propagation regulate the stressstrain behavior and the failure mode of concrete. Therefore, fundamental awareness of the fracture mechanism in terms of fracture energy is a requisite to comprehend concrete behavior. There is research consensus that fracture energy alone does not suffice to characterize the ductility/brittleness and also the size dependency of concrete. Therefore, it is necessary to evaluate the fracture energy and the characteristic length for a realistic assessment of the fracture behavior of concrete. Towards this objective, this study examined the fracture energy of concrete by experimentation, and the fracture energy proposed by various models in the literature. Further, the characteristic length proposed by Hillerborg which depicted both the material influence and the size effect, has been computed. Based on the RILEM50 FM recommendations, 18 specimens with varying grades of concrete and notch depths have been tested and the fracture energy parameters have been evaluated. Also, two regression models with key fracture parameters as variables for two-notch ratios, have been formulated for the concrete fracture energy. The arguments have been supported by experimental evidence. The Author(s), under exclusive licence to Shiraz University 2024. -
Mathematical modeling to investigate the influence of vaccination and booster doses on the spread of Omicron
The emergence of new variants, such as Omicron, has raised concerns regarding the transmission dynamics of COVID-19 and the effectiveness of vaccination strategies. This paper proposes a mathematical model to investigate the impact of vaccination and booster doses on Omicron transmission dynamics, considering various infection compartments. The model incorporates multiple compartments representing different stages of infection, including susceptible individuals, vaccinated individuals, boosted individuals, and those infected with Omicron. The infection dynamics are captured by parameters such as vaccine efficacy, vaccination with booster received efficacy, and infection rate. Using mathematical analysis and numerical simulations, we explore how different vaccination and booster strategies affect the spread of Omicron. The normalized sensitivity analysis method of R0 is investigated to understand the importance of parameters in disease transmission. Furthermore, we assess the influence of infection compartments, such as asymptomatic and symptomatic cases, on overall transmission dynamics. 2023 Elsevier B.V. -
Mathematical modelling and mechanics of acoustic waves in piezoelectric layers between n-type semiconductor plates: an irreducible Cardano method coupled with a functional iteration scheme
This study presents a comprehensive analyticalnumerical investigation of acoustic wave dispersion, attenuation, and energy dissipation in piezoelectricsemiconductor heterostructures composed of SiPZTSi and GePZTGe layers. The governing electromechanicaldiffusive equations for the coupled media are formulated with full continuity conditions, leading to a cubic characteristic equation solved using a hybrid irreducible Cardano method and functional iteration scheme. A detailed convergence analysis demonstrates stable, monotonic residual decay for both symmetric and asymmetric modes, confirming the robustness of the adopted solver. Numerical results reveal strong sensitivity of phase velocity, attenuation, and specific loss to wave number, semiconductor mobility, convergence and carrier concentration. GePZTGe consistently exhibits higher phase velocity, reduced attenuation, and lower dissipative losses than SiPZTSi, primarily due to the higher carrier mobility and weaker acoustoelectric drag in Ge. Additional parametric plots highlight the influence of semiconductor quality and PZT layer thickness on acoustic energy confinement. The findings provide actionable design guidelines for optimizing SAW-based filters, delay lines, sensors, and signal-processing devices, where low loss, high velocity, and efficient energy trapping are critical. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2026. -
Mathematical Modelling of Love-Type Wave Transmission in Magnetostrictive Smart Materials with Imperfect Interface
Abstract: The purpose of this study is to investigate the propagation of Love-type surface acoustic waves through two magnetostrictive materials, NiFe2O4 (Nickel Ferrite) and Terfenol-D, embedded in a plate-substrate configuration with an imperfect interface. The study aims to investigate the effect of plate thickness, imperfect parameter, and heterogeneity parameter on both the materials under magnetically open and short scenarios. Methodology: To accomplish this, the study uses a variable-separable approach with the Direct Sturm-Liouville technique along with suitable boundary conditions, to construct frequency relations for both magnetically open and short-circuit conditions. Numerical simulations are carried out to explore the impacts of plate thickness, imperfect parameter, and heterogeneity parameter on Terfenol-D and NiFe2O4 materials under magnetically open and short circumstances. These results have been discussed through graphs that are plotted with the help of Mathematica software. Findings: The results of the study show that the phase velocity increases greater in Terfenol-D than in NiFe2O4, regardless of whether the case is magnetically open or closed. Graphical comparisons clearly demonstrate the impact of width plates, faulty parameters, and heterogeneity parameters on wave propagation characteristics. Research Limitations: The study is limited to linear wave propagation and excludes non-linear effects. Furthermore, the research is based on the attributes of the idealized material and the contact conditions. Practical implications: The findings of this study can help with the design and optimization of sensors, energy harvesters, and wave manipulation devices that use piezomagnetic materials. Understanding the behaviour of surface waves in these structures is critical for their proper use. Originality: This paper provides a complete investigation of surface wave propagation in two types of piezomagnetic composite structures, taking into account heterogeneity and interface circumstances. The comparison of several piezomagnetic models, as well as the addition of heterogeneity and contact circumstances, contribute to the researchs originality. Pleiades Publishing, Ltd. 2025. -
Mathematical models in waste management
Waste management is a major issue faced by municipalities all over the world. The major problem associated with the waste managements newlineis its high cost and main part of the cost comes from its collection and transportation. This problem can be effectively overcome by the application of mathematical models. newlineAn important aspect of waste management is locating facilities like truck locations, transfer stations, compost units etc. The location of facilities when collection of waste happens at multiple time periods is newlineconsidered for cost minimization. The rapid increase in the population of cities as a result of vast urbanization and the corresponding shrinking of land has given rise to increase in apartment complexes in newlineall the cities. The waste management practices here have to be planned carefully as they are sources of large quantities of waste. They are also potential sites for recycling and composting as waste management newlinepractices can be introduced at apartment level itself, so that transportation burden is less. Components like fixed cost /maintenance cost and operational costs are considered for the study in cities as well newlineas in apartments. Testing the mathematical model is done using different scenarios and the results are used to draw conclusions. These results showed that the model works best when processing facilities are nearer to the transfer stations so that there is no additional cost incurred at that point for transportation. In addition, it was clear that the cost of the transportation is brought down using the model, as the newlineamount transported to landfills decreases. newlineScheduling a set of resources to a set of jobs can be done using resource calendar, which shows the availability of resources and the various time periods at which it a particular resource is available. There are different types of jobs and various types of resources. -
Mathematics as an agent of dialogue in the society
Though Mathematics is mostly considered as a subject of the intelligent, it is used by everybody for daily activities. It acts as an efficient agent of dialogue in the society. Its role in transferring abstract knowledge to concrete experience, in interpreting the unknown and as a tool for problem-solving are discussed in this paper. Mathematics also helps human beings to transcend from concrete experience to abstract knowledge. This paper showcases various elements of Mathematics over a wide spectrum, from those useful in everyday life of human beings to the discussions on potential and actual infinity. Mathematics is an integral part of human life and an essential tool in knowing the universe. We do not deliberately side with any of the schools in Mathematics or that of Philosophy. 2017 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore). -
Mathematics Self-Efficacy, Utility Value and Well-Being Among School Students in India: Mediating Role of Student Engagement
Teaching and learning mathematics has many challenges, including student engagement, attitudes and beliefs toward mathematics. Students experience stress and anxiety while learning mathematics. Mathematics is perceived as a complex subject. Student self-efficacy and a sense of utility value of mathematics topics can impact student learning and well-being. The current study aims to examine the mediating role of student engagement on the relationship between mathematics self-efficacy, utility value and well-being among students. A cross-sectional survey of 774 eighth-grade students (491 male and 283 female) from India was carried out using standardized scales to measure the study variables. The mediation analysis tested two conceptual models. The findings indicate that student engagement mediates the relationship between self-efficacy and student well-being (model 1), and student engagement mediates the relationship between utility value and student well-being (model 2). The structural equation model results indicate an acceptable fit of the tested conceptual models. The study findings call for focusing on socio-emotional aspects of mathematics learning to improve the well-being of students. 2023 Research Council on Mathematics Learning. -
Matrix-Based Apriori Methods for Frequent Pattern Mining: An In-Depth Survey
Data Mining identifies intriguing, useful, and previously unknown patterns and correlations between data stored in databases or warehouses. Frequent Pattern Mining (FPM) is one of the vital methods in the prospering arena of data mining (DM), and it describes the relationship between the items in the datasets. In the last two decades, many studies were carried out in FPM using the Apriori algorithm. The Apriori algorithm requires many database scans and produces numerous candidate itemsets, increasing I/O cost and decreasing computational efficiency. To address these issues, researchers contributed many improved versions of Apriori and proved that those algorithms scan the database only once and identify the frequent itemsets quickly, especially when the itemsets are higher, and provide higher efficiency and feasibility. This research article summarizes matrix-based Apriori algorithms in the literature used for identifying frequent itemsets. 2025 IEEE. -
Maulana Abul Kalam Azad
Maulana Abul Kalam Azad was a profound and rare Islamic scholar, writer, thinker, freedom fighter who promoted the idea of 'universal humanism'. He was well versed in poetry, art and music besides having a flair for writing. He was a multi-faceted personality with a progressive outlook. Though he had a rationalist outlook, he was very well versed in Islamic lore and history. His view of Islam did not necessarily come into conflict with territorial nationalism, Pan-Islamism and anti-imperialism. In this sense, he had interpreted Islam from a rationalist perspective. Maulana Azad had given a clarion call to the Muslims to join hands with the Hindus to achieve the common goal of ending British rule and domination in India. In fact, he considered this as the duty of the Indian Muslims, because according to him Muslims were created not for despondency but for 'hope'. As a revolutionary journalist the Maulana heralded a new era in Urdu journalism. His weekly 'Al-Hilal' grew in readership to such an extent that ultimately the British Government had to ban it. This chapter will analyse the thoughts, ideas and contribution of Maulana Azad to the freedom movement and nation-building in the post-independent India. Springer Nature Singapore Pte Ltd. 2022. All rights reserved. -
Maximal matching cover pebbling number for variants of hypercube
An edge pebbling move is defined as the removal of two pebbles from one edge and placing one on the adjacent edge. The maximal matching cover pebbling number, fmmcp(G), of a graph G, is the minimum number of pebbles that must be placed on E(G), such that after a sequence of pebbling moves the set of edges with pebbles forms a maximal matching regardless of the initial configuration. In this paper, we find the maximal matching cover pebbling number for variants of hypercube. (2023), (SciELO-Scientific Electronic Library Online). All Rights Reserved. -
Maximised bioethanol extraction from bamboo biomass through alkali pretreatment and enzymatic saccharification by application of ANN-NSGA-II-based optimisation method
The demand for alternative fuels is growing due to the depletion of fossil fuel resources. Non-edible resources are explored as alternatives, and a bamboo is an up-and-coming option for producing ethanol. The extraction process for bioethanol from bamboo involves alkali pretreatment, enzymatic saccharification, and ethanol production. The bamboo biomass is treated with alkali at high temperatures and pressure. This treatment helps break the lignin bonds that hinder the reaction between cellulose and enzymes. As a result, the pretreated biomass contains 40% less lignin than its raw form. Next, the air-dried pretreated biomass undergoes saccharification using Supercut Acid Cellulose. The saccharification process is optimised to achieve the shortest possible time, determined through prediction models based on artificial neural networks and optimisation techniques like Non-dominated Sorting Genetic Algorithm-II. The optimised process involves specific biomass and enzyme loading, producing reducing sugars estimated using the DNS method. Following enzymatic Saccharification, the hydrolysate is fermented using Saccharomyces cerevisiae, a type of yeast. This fermentation process yields ethanol with a 1614.26mg/kg concentration. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Maximised bioethanol extraction from bamboo biomass through alkali pretreatment and enzymatic saccharification by application of ANN-NSGA-II-based optimisation method
The demand for alternative fuels is growing due to the depletion of fossil fuel resources. Non-edible resources are explored as alternatives, and a bamboo is an up-and-coming option for producing ethanol. The extraction process for bioethanol from bamboo involves alkali pretreatment, enzymatic saccharification, and ethanol production. The bamboo biomass is treated with alkali at high temperatures and pressure. This treatment helps break the lignin bonds that hinder the reaction between cellulose and enzymes. As a result, the pretreated biomass contains 40% less lignin than its raw form. Next, the air-dried pretreated biomass undergoes saccharification using Supercut Acid Cellulose. The saccharification process is optimised to achieve the shortest possible time, determined through prediction models based on artificial neural networks and optimisation techniques like Non-dominated Sorting Genetic Algorithm-II. The optimised process involves specific biomass and enzyme loading, producing reducing sugars estimated using the DNS method. Following enzymatic Saccharification, the hydrolysate is fermented using Saccharomyces cerevisiae, a type of yeast. This fermentation process yields ethanol with a 1614.26mg/kg concentration. The Author(s), under exclusive licence to Springer Nature B.V. 2023. -
Maximizing Bifunctionality for Overall Water Splitting by Integrating H2 Spillover and Oxygen Vacancies in CoPBO/Co3O4 Composite Catalyst
In the pursuit of utilizing renewable energy sources for green hydrogen (H2) production, alkaline water electrolysis has emerged as a key technology. To improve the reaction rates of overall water electrolysis and simplify electrode manufacturing, development of bifunctional electrocatalysts is of great relevance. Herein, CoPBO/Co3O4 is reported as a binary composite catalyst comprising amorphous (CoPBO) and crystalline (Co3O4) phases as a high-performing bifunctional electrocatalyst for alkaline water electrolysis. Owing to the peculiar properties of CoPBO and Co3O4, such as complementing Gibbs free energy values for H-adsorption (?GH) and relatively smaller difference in their work functions (??), the composite exhibits H2 spillover (HS) mechanism to facilitate the hydrogen evolution reaction (HER). The outcome is manifested in the form of a low HER overpotential of 65 mV (at 10 mA cm?2). Moreover, an abundant amount of surface oxygen vacancies (Ov) are observed in the same CoPBO/Co3O4 composite that facilitates oxygen evolution reaction (OER) as well, leading to a mere 270 mV OER overpotential (at 10 mA cm?2). The present work showcases the possibilities to strategically design non-noble composite catalysts that combine the advantages of HS phenomenon as well as Ov to achieve new record performances in alkaline water electrolysis. 2024 The Author(s). Small Science published by Wiley-VCH GmbH. -
Maximizing Efficiency: Unveiling thePotential ofKubernetes Metrics
In the realm of Kubernetes cluster management, the importance of metrics cannot be overstated. Metrics serve as a powerful lens, providing a quantitative perspective into a clusters performance, behavior, and resource utilization. In the ever-evolving landscape of cloud-native computing, metrics are the key to informed decision-making. They empower administrators to navigate scaling, resource allocation, and the holistic optimization of Kubernetes clusters with a data-driven confidence. This paper stands as a vital contribution, placing metrics at the forefront of the discussion. It underscores their transformative potential by shedding light on how they drive administrators decisions, enable the identification of performance bottlenecks, and enhance application responsiveness. Moreover, metrics play a pivotal role in proactive capacity planning, ensuring resources are allocated with precision to meet both current and future workload demands. In essence, this papers core contribution lies in providing a comprehensive overview of Kubernetes metrics and highlighting their profound impact on Autoscaling strategies. By revealing the constraints that metrics may impose on the efficient scaling of application resources, it equips administrators with a navigational tool for building dynamic and resilient computing environments within Kubernetes clusters. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Maximum Decision Support Regression-Based Advance Secure Data Encrypt Transmission for Healthcare Data Sharing in the Cloud Computing
The recent growth of cloud computing has led to most companies storing their data in the cloud and sharing it efficiently with authorized users. Health care is one of the initiatives to adopt cloud computing for services. Both patients and healthcare providers need to have access to patient health information. Healthcare data must be shared and maintained more securely. While transmitting health data from sender to receiver through intermediate nodes, intruders can create falsified data at intermediate nodes. Therefore, security is a primary concern when sharing sensitive medical data. It is thus challenging to share sensitive data in the cloud because of limitations in resource availability and concerns about data privacy. Healthcare records struggle to meet the needs of security, privacy, and other regulatory constraints. To address these difficulties, this novel proposes a machine learning-based Maximum Decision Support Regression (MDSR)-based Advanced Secure Data Encrypt Transmission (ASDET) approach for efficient data communication in cloud storage. Initially, the proposed method analyzed the node's trust, energy, delay, and mobility using Node Efficiency Hit Rate (NEHR) method. Then identify the efficient route using an Efficient Spider Optimization Scheme (ESOS) for healthcare data sharing. After that, MDSR analyzes the malicious node for efficient data transmission in the cloud. The proposed Advanced Secure Data Encrypt Transmission (ASDET) algorithm is used to encrypt the data. ASDET achieved 92% in security performance. The proposed simulation result produces better performance compared with PPDT and FAHP methods. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Mayfly Algorithm for Optimal Integration of Hybrid Photovoltaic / Battery Energy Storage / D-STATCOM System for Islanding Operation
In today's power system design studies, autonomous and self-healing capabilities are becoming increasingly important. Renewable energy (RE) integration, on the other hand, is geared at long-term sustainability. In this regard, a hybrid energy system consisting of a photovoltaic (PV) source, battery energy storage (BESS), and distribution-static synchronous compensator (D-STATCOM) is proposed for optimal design and integration in the electrical distribution network (EDN) when short-term islanding operational requirements are taken into account. When considering grid-connected mode, the PV system is initially optimally allocated towards loss minimization. Following that, the capacities of BESS and D-STATCOM are assessed in the context of a short-term islanding scenario. The optimization problem is tackled utilising a recent meta-heuristic mayfly optimization algorithm (MOA) in both stages. The simulations are run on an IEEE 33-bus EDN network. By having optimal PV system in grid-connected mode, it is observed that real power losses are reduced to 111.03 kW from 210.998 kW and reactive power losses are reduced to 81.684 kVAr from 143.033 kVAr. In addition, the minimum voltage in the network is raised to 0.9424 p.u. from 0.9038 p.u. On the other hand, by designing hybrid energy systems using PV, BESS, and D-STATCOM, the network is able to serve the entire load even under islanding conditions. MOA's competitiveness in solving difficult non-linear multivariable optimization problems was demonstrated in comparative research with literature publications. In addition, the proposed hybrid energy system can cope with the uncertainties and other requirements of current grids. 2022. All Rights Reserved. -
MCCLDP: Multi Class Cotton Leaf Diseases Prediction and Classification using Deep Learning Model
Cotton plant disease detection is critical for sustainable agriculture and reducing crop losses. This paper proposes a novel Multi-Stream Attention-Guided Hybrid CNN (MAH-CNN) for accurate classification of cotton leaf diseases. The model leverages pre-trained ResNet152v2 and DenseNet-121 backbones for hierarchical feature extraction, complemented by a shallow CNN for localized texture analysis. A spatial attention mechanism enhances focus on disease-relevant regions, mitigating background noise. Features from the global and local streams are fused and passed through a lightweight classification head. The model achieves superior performance in terms of accuracy 97.32%, F1 score 98%, and specificity 100% on benchmark datasets which are available in open access, outperforming existing state-of-the-art methods. The integration of Grad-CAM provides interpretability, fostering trust in automated disease detection systems. 2025 IEEE.