National Seminar On Mathematical Foundations and Computational Techniques for Emerging Technologies | International Journal of Science, Engineering and Technology

Sri Venkateshwara Government Arts & Science College(A),
Palem-509215, Nagarkurnool Dist., Telangana, India

5th March 2025

Sponsored by
RUSA/PM USHA, Hyderabad
Chairman/Convener:
Dr. P. Ramulu, Principal(FAC)
Associate Professor of Mathematics

Publish Partner IJSET Journal

ISSN(O): 2348-4098

ISSN(P): 2395-4752

Brocedure

Seminar Proceeding

“A Mathematical Biology Approach To Plant Systems: Integrating Growth, Ecology, And Genetics”

Authors: P. Ramulu, S.Suresh

Abstract: Mathematical biology has emerged as a powerful interdisciplinary approach for analyzing complex plant systems through quantitative modeling and computational techniques. This study presents an integrated framework that combines plant growth dynamics, ecological interactions, and genetic mechanisms to provide a comprehensive understanding of plant behavior. The research employs differential equation–based models, ecological competition models, and population genetics principles to examine how plants grow, interact within ecosystems, and evolve over time. The logistic growth model is utilized to describe plant growth under resource limitations, demonstrating the characteristic sigmoidal pattern and the role of carrying capacity in regulating biomass. Ecological interactions are analyzed using competition models, revealing conditions for species coexistence and competitive exclusion. Genetic variation is examined through population genetics models, highlighting how allele frequencies change under selection pressure and contribute to plant adaptation and evolution. The integration of these components illustrates the interdependence of growth, ecology, and genetics in shaping plant systems.The study relies on secondary data and computational simulations to validate the proposed models, with results showing strong agreement with observed biological patterns. Sensitivity analysis further emphasizes the importance of key parameters such as growth rate, competition coefficients, and genetic fitness in influencing system behavior. While the models provide valuable insights, certain limitations related to simplifying assumptions and environmental variability are acknowledged. Overall, this research underscores the significance of mathematical biology in advancing plant science by offering predictive and analytical tools for understanding complex biological processes.

DOI: https://doi.org/10.5281/zenodo.19436691

Reinforcing Graduate Students’ Problem-Solving Reasoning And Scholarly Writing Skills Through Case-Based Learning Frameworks.

Authors: P. Ramulu, S.Suresh

Abstract: This study examines the effectiveness of contextualized and case-based learning frameworks in enhancing graduate students’ mathematical reasoning, problem-solving abilities, and scholarly communication skills. Grounded in constructivist learning theory, these instructional approaches emphasize meaningful connections between mathematical concepts and real-world or research-oriented applications, fostering deeper understanding and active engagement. A quasi-experimental design with a nonequivalent control group was employed, involving 45 graduate students from a higher education institution. Participants were divided into experimental and control groups, where the experimental group was exposed to contextualized, case-based instructional modules, while the control group received conventional teaching. Data were collected using validated instruments measuring mathematical reasoning and academic communication skills through pretest and posttest assessments. Quantitative analysis was conducted using paired-sample t-tests to evaluate within-group improvements and Multivariate Analysis of Covariance (MANCOVA) to assess between-group differences while controlling for pretest scores. Additionally, qualitative data were obtained from writing samples, reflective journals, and student feedback to provide deeper insights into learning outcomes. The findings reveal that students exposed to contextualized and case-based learning approaches demonstrated significantly greater improvement in mathematical reasoning, problem-solving skills, and scholarly writing compared to those in traditional settings. The results further indicate enhanced abilities in logical analysis, structured argumentation, and discipline-specific academic expression. Moreover, these approaches promoted collaborative learning, critical thinking, and the integration of theoretical knowledge with practical applications.

DOI: https://doi.org/10.5281/zenodo.19436755

Mathematical Foundations And Computational Techniques For Emerging Technologies

Authors: P. Ramulu

Abstract: Mathematics forms the foundational framework for the development and advancement of emerging technologies in the modern digital era. This research paper explores the critical role of core mathematical disciplines—such as linear algebra, probability and statistics, calculus and optimization, graph theory, and number theory—in enabling technologies like artificial intelligence, quantum computing, blockchain, data science, and cybersecurity. These mathematical tools provide the structure for designing efficient algorithms, modeling complex systems, handling uncertainty, ensuring secure communication, and optimizing performance. The study highlights how linear algebra supports data representation and neural network computations, while probability and statistics enable predictive modeling and decision-making under uncertainty. Calculus and optimization techniques are essential for training machine learning models and improving system efficiency. Graph theory facilitates the analysis of networks and interconnected systems, and number theory forms the basis of cryptographic methods that secure digital communication and blockchain systems. In addition to examining these applications, the paper discusses key challenges such as managing large-scale data, developing quantum-resistant cryptographic systems, improving the explainability of artificial intelligence models, and bridging the gap between theoretical mathematics and practical implementation. Addressing these challenges requires continuous innovation and interdisciplinary collaboration. The paper concludes that mathematics is not merely supportive but central to technological progress. A strong understanding of mathematical foundations is essential for advancing emerging technologies and solving complex real-world problems. As technology continues to evolve, the integration of mathematical principles will remain crucial in driving innovation, ensuring security, and shaping the future of a data-driven and interconnected world.

DOI: https://doi.org/10.5281/zenodo.19437631

A Deterministic Construction Of Sensing Matrices From The Lattice Of Integers Under Divisibility

Authors: P. Anuradha, Co-Author: K.V.R.Kanaka Durga

Abstract: The construction of deterministic sensing matrices satisfying the Restricted Isometry Property (RIP) remains a fundamental challenge in compressed sensing. This paper introduces a novel deterministic framework for constructing sensing matrices by exploiting the algebraic structure of the lattice of integers under the divisibility partial order. We demonstrate that the Möbius function and incidence algebra associated with this lattice naturally give rise to matrices with low coherence and structured sparsity. The proposed construction yields matrices of size φ(N) × N, where φφis Euler’s totient function, with column coherence bounded by O (1/√(φ(N))), asymptotically achieving the Welch bound. Unlike random constructions, our approach guarantees perfect recovery for signals sparse in the standard basis without probabilistic arguments. Furthermore, we establish a connection between the divisibility lattice and Dirichlet convolution, enabling efficient signal reconstruction via number-theoretic transforms. Numerical experiments validate the theoretical guarantees and demonstrate competitive performance against existing deterministic constructions based on finite fields and algebraic geometry.

DOI: https://doi.org/10.5281/zenodo.19437704

Integrating Think–Talk–Write With Realistic Mathematics Education To Strengthen Critical Thinking And Adversity Quotient

Authors: P. Ramulu, M. Ramakrishna

Abstract: This study presents a conceptual analysis of the integration of the Think–Talk–Write (TTW) strategy within the framework of Realistic Mathematics Education (RME) as an approach to enhancing middle school students’ critical thinking skills and Adversity Quotient (AQ). In the context of twenty-first-century education, learners are expected not only to demonstrate strong cognitive abilities but also to exhibit resilience and adaptability when facing complex academic challenges. Therefore, the development of both higher-order thinking skills and psychological endurance becomes a fundamental goal of effective mathematics instruction. The present study is based on an extensive review of relevant scholarly literature focusing on instructional strategies, critical thinking development, and resilience in mathematics education. The TTW strategy promotes structured learning through three interconnected stages: individual reflection (think), collaborative dialogue (talk), and systematic written expression (write). These stages encourage students to actively process information, articulate their reasoning, and refine their understanding through communication and reflection. At the same time, the RME approach emphasizes the importance of contextualizing mathematical concepts by linking them to real-life situations that are meaningful and familiar to students. Through processes such as exploration, modeling, and progressive mathematization, RME enables learners to construct knowledge in a way that is both meaningful and applicable. The integration of TTW and RME is conceptualized as a complementary pedagogical framework that combines reflective learning with contextual problem-solving.

DOI: https://doi.org/10.5281/zenodo.19437805

Multidisciplinary Uses of Computational Mathematics along with Artificial Intelligence in Sustainable Engineering: Intelligent Systems, Optimization, and Modelling for Global Resilience

Authors: P. Sobha Latha, Y. Jnapika

Abstract: The growing complexity of global sustainability challenges demands advanced analytical and computational approaches. Issues such as climate change, energy transition, urban resilience, and the circular economy require integrated scientific methods for effective solutions. Computational mathematics encompassing mathematical modelling, numerical analysis, optimization theory, uncertainty quantification, and scientific machine learning—plays a crucial role in advancing sustainable engineering innovations. This paper presents a multidisciplinary overview of computational mathematics techniques with machine learning models applied to environmental and climate modelling, circular manufacturing systems, green infrastructure, smart grids, sustainable transportation, and renewable energy technologies. The study critically evaluates several computational approaches, including graph-theoretic models, multi-scale simulations, stochastic systems, partial differential equation (PDE) frameworks, and evolutionary optimization methods in sustainability engineering. It also explores emerging concepts such as digital twins, quantum-inspired optimization, and climate simulations supported by high-performance computing (HPC). Furthermore, the research addresses challenges related to algorithm scalability, model interpretability, uncertainty propagation, and ethical considerations in AI-driven sustainability systems. By integrating mathematics, computer science, and engineering principles, this paper highlights how computational mathematics enables carbon-neutral system design, predictive analytics, and efficient resource management. It also identifies future research directions, including distributed computing architectures, hybrid AI–physics models, and quantum-enhanced optimization to support global Sustainable Development Goals.

DOI: https://zenodo.org/records/19439399

Artificial Intelligence In Library Science

Authors: P. Ramulu, V. Srinivasulu

DOI: http://doi.org/10.5281/zenodo.19439497

Network Intrusion Detection With The Assistance Of Artificial Intelligence

Authors: J.Bindhu Bhargavi

Abstract: The relevance of resolving network intrusion issues in AI applications has increased due to the integration of artificial intelligence (AI) into vital infrastructure and daily life. While AI systems have many advantages, like improved productivity, automation, and decision-making, they also present new risks and weaknesses. It is essential to guarantee these systems’ dependability and security. The main cybersecurity issues related to AI are examined in this study, including data privacy, integrity, adversarial attacks, and the moral ramifications of AI in security. Artificial Intelligence (AI) has revolutionized several industries in the digital age, providing previously unheard-of chances for efficiency and creativity. Nevertheless, these developments provide intricate cybersecurity issues that affect people, businesses, and society as a whole. It is crucial to protect these systems from malicious assaults, unauthorized access, and unforeseen repercussions as AI becomes more and more integrated into everyday life and vital infrastructure. We examine important cybersecurity concerns in AI applications, including adversarial assaults, data privacy violations, ethical challenges, and AI-driven cyberthreats. It also looks at how Shapley Additive explainable AI contributes to transparency by making AI models easier to understand and providing insights into how decisions are made.

DOI: http://doi.org/10.5281/zenodo.19439582

AI Tools for Calculus: Enhancing Algorithmic Discovery and Mathematical Understanding

Authors: Mr. M. Naveen Raj

Abstract: The integration of artificial intelligence (AI) into calculus education and research represents a paradigm shift in how mathematical concepts are taught, learned, and discovered. This article presents a comprehensive review of contemporary AI tools designed for calculus applications, categorizing them into three primary domains: learning and tutoring platforms, automated assessment systems, and research assistants for mathematical discovery. Through systematic analysis of twelve representative tools—including AxiomProver, AlphaEvolve, CalcTutor, and various interactive learning platforms—we examine their architectural foundations, pedagogical approaches, and empirical performance metrics. The review synthesizes recent benchmark studies evaluating large language models on calculus tasks, revealing that while current systems achieve up to 94.71% accuracy on procedural differentiation problems, significant limitations persist in conceptual understanding and complex problem-solving contexts. We conclude by proposing a framework for tool selection based on user objectives and discussing implications for the future of mathematical pedagogy and research methodology.

DOI: http://doi.org/10.5281/zenodo.19439750

The Dawn Of Self-Improving AI: Reflexion And Evolution In LLMs

Authors: L Babitarani, A. Manoj Kumar

Abstract: The rapid advancement of large language models (LLMs) has marked a significant milestone in the field of artificial intelligence, enabling machines to perform complex reasoning, natural language understanding, and generative tasks with remarkable proficiency. However, traditional LLMs operate in a static manner, lacking the ability to dynamically learn from their own outputs during inference. The concept of self-improving AI introduces a transformative paradigm in which models iteratively refine their responses through mechanisms such as Reflexion and evolutionary strategies. Reflexion-based approaches allow LLMs to evaluate their own outputs, identify errors, and generate improved responses by incorporating feedback loops within the reasoning process. This self-corrective capability enhances accuracy, reliability, and interpretability without requiring external retraining. In parallel, evolutionary methods inspired by biological processes—such as mutation, selection, and adaptation—enable models to explore diverse solution spaces and progressively optimize performance over multiple iterations. This paper explores the integration of Reflexion and evolutionary frameworks in LLMs, highlighting their potential to bridge the gap between static intelligence and adaptive learning systems. It also examines practical applications, including problem-solving, code generation, and decision-making, while addressing key challenges such as computational cost, alignment, and evaluation metrics. The emergence of self-improving AI represents a crucial step toward more autonomous, efficient, and robust intelligent systems, paving the way for next-generation artificial intelligence.

DOI: http://doi.org/10.5281/zenodo.19439951

A Class Of BCI-Algebras Defined By The Non-Existence Of Singular Elements

Authors: P. Ramulu, J. Niranjana Goud

Abstract: BCI-algebras constitute an important class of algebraic structures arising from non-classical logic and implicational systems. They generalize BCK-algebras and play a significant role in the study of algebraic logic, order theory, and abstract algebraic systems. In this paper, we introduce and investigate a special class of BCI-algebras characterized by the non-existence of singular elements. An element of a BCI-algebra is said to be singular if it satisfies certain restrictive operational conditions that lead to structural degeneracy. By eliminating such elements, we obtain a refined algebraic framework that preserves the core axioms of BCI-algebras while ensuring improved structural regularity.We establish fundamental properties of non-singular BCI-algebras and examine their relationships with ideals, sub algebras, and holomorphic images. Several equivalent conditions characterizing the absence of singular elements are derived. Furthermore, we study the behaviour of these algebras under standard constructions and provide illustrative examples to distinguish them from general BCI-algebras. It is shown that every non-singular BCI-algebra exhibits enhanced cancellation-like properties and stronger order-theoretic behaviour.The results contribute to a deeper structural understanding of BCI-algebras and suggest potential applications in classification theory and logical algebraic modelling.

DOI: https://doi.org/10.5281/zenodo.19441040

Mathematical Foundations And Computational Techniques In Diophantine Equations

Authors: E.Ramaraju Yadav, G.Yuvaroopa Lakshmi, K. Rahul

Abstract: Diophantine equations constitute a fundamental area of number theory characterized by the requirement that solutions must be integers. These equations have played a central role in mathematical research from classical antiquity to modern computational science. This paper presents a systematic study of the mathematical foundations and computational techniques associated with Diophantine equations. Core theoretical concepts—including divisibility theory, greatest common divisors, modular arithmetic, and prime factorization—are examined to establish existence and structure conditions for integer solutions. Classical solution methods such as the Euclidean and Extended Euclidean algorithms are discussed alongside contemporary computational approaches that utilize symbolic computation and algorithmic search strategies. The study further explores diverse applications of Diophantine equations in cryptography, coding theory, optimization, computer science, and geometric modeling. By integrating theoretical analysis with modern computational tools, this work highlights the continued relevance of Diophantine methods in both pure mathematics and applied technological domains. The findings emphasize the importance of efficient algorithm design and suggest future research directions in higher-degree equations, artificial intelligence–assisted number theory, and large-scale computational analysis.

DOI: https://doi.org/10.5281/zenodo.19441822

Mathematical Model For Plant Height Growth And Resource Optimization Under Climate Uncertainty Using Computational Techniques

Authors: Dr.M.Archana

Abstract: Plant growth is a complex biological process influenced by environmental conditions and resource availability. This study develops a mathematical framework to model plant height using linear, exponential, and logistic growth models. A regression model is used to study the effect of water, fertilizer, and sunlight, and a constrained optimization problem is formulated to maximize plant height. To incorporate real-world variability, the model is extended to a stochastic differential equation under climate uncertainty. A case study based on ICAR growth-stage data is used to validate the model. Graphical analysis and optimization results demonstrate that optimal resource allocation significantly improves plant growth. The study highlights the importance of mathematical foundations and computational techniques in emerging technologies such as precision agriculture and smart farming.

DOI: https://doi.org/10.5281/zenodo.19441936

Optimization, Stochastic Modeling, And Computational Frameworks In Emerging Technologies

Authors: Dr. K. Madhavi

Abstract: Emerging technologies such as Artificial Intelligence, Quantum Computing, Blockchain, and the Internet of Things (IoT) are rapidly transforming modern scientific and industrial landscapes. At the core of these technological advancements lie powerful mathematical principles and computational methodologies. This paper explores the fundamental role of optimization theory, stochastic modeling, and advanced computational frameworks in the development and deployment of emerging technological systems. Optimization techniques, including gradient-based methods and convex programming, provide efficient solutions to large-scale learning and decision-making problems in Artificial Intelligence. Stochastic modeling offers robust tools for handling uncertainty, randomness, and dynamic behavior in complex systems, particularly in data-driven environments. The study highlights how linear algebra, probability theory, differential equations, and algorithmic design collectively form the backbone of modern technological innovations. By synthesizing theoretical foundations with practical computational strategies, this paper demonstrates that mathematics is not merely supportive but foundational to emerging technologies. The discussion also outlines future research directions emphasizing interdisciplinary integration and the growing need for mathematically trained professionals in technological domains.

DOI: https://doi.org/10.5281/zenodo.19442291

Relative Efficiency Analysis Of Green Technology Innovation In Global Decarbonization: An Application Of The CCR Data Envelopment Analysis Model

Authors: Dr. D. Pavan Kumar

Abstract: The transition toward a low-carbon global economy requires not only increased investment in green technologies but also improvements in the efficiency with which such innovations contribute to decarbonization. This study evaluates the efficiency of green technology innovation in promoting global decarbonization using a Data Envelopment Analysis (DEA) framework based on the Charnes–Cooper–Rhodes (CCR) model, which assumes constant returns to scale. Drawing on cross-country panel data, the study constructs an efficiency model incorporating green innovation inputs—such as research and development (R&D) expenditure, renewable energy patents, and clean energy investment—and desirable outputs including carbon emission reductions and renewable energy generation. The empirical analysis assesses relative efficiency across countries and identifies best-performing frontiers in transforming green technological inputs into decarbonization outcomes. The results reveal substantial heterogeneity in efficiency levels, with several economies operating below the optimal frontier, indicating untapped potential in leveraging green innovation for climate mitigation. Furthermore, scale efficiency decomposition highlights that both technological capability gaps and suboptimal innovation scale contribute to observed inefficiencies. The findings offer important policy implications. First, increasing investment in green innovation alone does not guarantee proportional decarbonization gains; improving innovation efficiency is equally critical. Second, countries can benefit from benchmarking against frontier economies to optimize resource allocation and institutional support mechanisms. Overall, this study contributes to the literature on climate policy and sustainable development by providing an efficiency-based perspective on the role of green technological innovation in achieving global decarbonization targets.

DOI: https://doi.org/10.5281/zenodo.19442356

Exploring Algorithmic Bias In Language Generation Frameworks

Authors: D. Sharavaiah, A. Manoj Kumar

Abstract: The rapid advancement of Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), has transformed the landscape of automated text generation across domains such as recruitment, education, media, and decision-support systems. Despite their remarkable capabilities, these models may inadvertently encode, reproduce, and amplify existing societal biases present in training data. This study explores algorithmic bias within language generation frameworks, with a particular emphasis on identifying and quantifying gender-related disparities in AI-generated content. The research proposes a systematic bias evaluation framework grounded in statistical and probabilistic methods. Key metrics include mean bias, mean absolute bias, sentiment distribution analysis, and divergence-based measures such as Kullback–Leibler divergence to assess distributional imbalances across demographic attributes. By analyzing large-scale AI-generated textual datasets, the study aims to detect measurable patterns of bias and evaluate how prompt construction, model architecture, and training data influence output disparities. Furthermore, the work examines cross-model behavior among leading proprietary and open-source LLMs and integrates interpretable embedding techniques to enhance transparency in bias detection and mitigation. The expected contribution of this research lies in developing a mathematically rigorous bias quantification pipeline and offering practical strategies for fairness-aware language generation. Ultimately, the study seeks to provide a scalable framework for evaluating and reducing algorithmic bias in generative AI systems, contributing to more equitable and responsible AI deployment.

DOI: https://doi.org/10.5281/zenodo.19442412

On The Existence Of Common Fixed Points In Complete Rectangular S-Metric Spaces

Authors: P. Ramulu, D. Sattemma, T. Rajeshwari

Abstract: Fixed point theory has witnessed substantial development through the introduction of generalized metric structures. Among these, S-metric spaces and their rectangular variants have emerged as powerful frameworks for extending classical contraction principles. In this paper, we investigate common fixed point results in complete rectangular D-metric spaces, obtained through a structural reformulation of S-metric spaces. We establish a new contraction-type condition for a pair of self-mappings and prove the existence and uniqueness of a coincidence point under suitable range inclusion and completeness assumptions. Further, by employing weak compatibility of mappings, we derive the existence of a unique common fixed point. The presented theorem generalizes several known results in rectangular S-metric spaces and broadens the applicability of fixed point theory in generalized nonlinear structures. The results contribute to the ongoing development of metric-type spaces and their applications in nonlinear functional analysis.

DOI: https://doi.org/10.5281/zenodo.19442480

Computational Frameworks Underpinning Emerging Technologies: A Mathematical Perspective

Authors: Ittedi Shravani

Abstract: This paper explores the critical role of mathematical structures and computational techniques in advancing emerging technologies, such as artificial intelligence, quantum computing, blockchain, the Internet of Things (IoT), robotics, and data science. It provides a comprehensive overview of algorithms, numerical methods, machine learning, optimization, simulation, and high-performance computing frameworks that underpin these technologies. Through the use of case studies and application examples, the paper illustrates how mathematical models are integrated with computational tools to address complex real-world challenges, with an emphasis on performance evaluation and impact analysis. It examines key challenges, including computational complexity, scalability, and interpretability, as well as the potential for new mathematical theories and the importance of interdisciplinary collaboration. The paper concludes by underscoring the necessity for ongoing research in mathematical and computational methods to sustain innovation, robustness, and scalability in emerging technological domains.

DOI: https://doi.org/10.5281/zenodo.19467664

A Novel Linear Programming Framework For Multi-Objective Multi-Commodity Transportation Optimization

Authors: P. Ramulu, Ch. Janaiah, E. Rama Raju Yadav, B. Saidi Reddy

Abstract: The multi-objective multi-commodity transportation problem (MOMCTP) is a complex and significant challenge in modern supply chain management, requiring the simultaneous optimization of multiple conflicting objectives while distributing various products from multiple sources to diverse destinations. This study presents a novel and structured mathematical framework based on linear programming to effectively address this problem. A Multi-Objective Linear Programming (MOLP) model is developed by incorporating three critical performance criteria: minimization of transportation cost, reduction of delivery time, and mitigation of environmental impact through reduced CO₂ emissions. The proposed model integrates realistic operational constraints, including source capacity limitations, destination demand requirements, vehicle capacity restrictions, and multi-commodity allocation policies within a unified optimization framework. To handle the inherent trade-offs among competing objectives, the weighted sum method is employed to generate Pareto-efficient solutions, enabling a systematic analysis of objective interactions and decision priorities. To validate the applicability and effectiveness of the model, a real-world case study based on operational data from a regional distribution system is examined. The problem is solved using WinQSB software to determine optimal shipment plans and routing decisions. Computational results demonstrate that the proposed approach successfully balances multiple objectives, leading to significant improvements in overall system performance, including reductions in transportation cost, delivery time, and carbon emissions. This study contributes to the field of transportation optimization by providing a robust, flexible, and practical decision-support framework for sustainable and efficient multi-commodity logistics planning.

DOI: http://doi.org/10.5281/zenodo.19467812

Comparative Statistical Analysis Of IPL 2025: Team Performance, Individual Impact, And Auction Economics

Authors: P. Ramulu, M Mallikarjun

Abstract: The Indian Premier League (IPL) 2025 season exhibited unprecedented scoring trends, record-breaking team totals, and aggressive batting performances. This study presents a comprehensive comparative statistical analysis integrating team standings, individual batting achievements, highest team innings totals, and auction economics. Using descriptive statistics, regression modeling, and performance comparison metrics, the research evaluates the relationship between auction investment, individual brilliance, and overall team success. The findings indicate that while explosive batting defined IPL 2025, balanced team composition and bowling efficiency were stronger predictors of league success.

DOI: https://doi.org/10.5281/zenodo.19467955

2-D Bio-convective Nanofluid Flow Past A Stretching Sheet In Presence Of Motile Microorganisms With Cattaneo-Christov Heat And Mass Flux

Authors: Ch. Janaiah, B. Saidi Reddy

Abstract: This research examines the two-dimensional flow of a continuous bio-convective magnetohydrodynamic (MHD) nanofluid. This flow is driven by a porous substance stretched across a sheet and by movable gyrotactic microorganisms. Mass fluxes and Cattaneo-Christov heat are among the physical variables examined in order to understand the system’s flow dynamics. Finding out how different things influence microorganisms, concentration, speed, and temperature is our main goal. Discovering this kind of flow issue is necessary for controlling the movement of heat and liquids around a stretched sheet. A variety of bioengineering applications, including microfluidics, cooling systems, and bioreactors (including microbial fuel cells), may make use of this material. There were practical reasons for starting the investigation, and there were also scientific grounds. The nonlinear ordinary differential equations derived from the governing equations may be solved using the MATLAB bvp4c tool. The effects on velocity, concentration, temperature, and microbial growth may be shown graphically. Moreover, the impacts on the Motile density, Sherwood number, skin friction, and Nusselt number parameters are shown in a table. Because it addresses the concerns raised by Rashidi et al. [21], this study stands out. This study has some common ground with Rashidi et al. [21].

DOI: https://doi.org/10.5281/zenodo.19468102

Integrating Machine Learning And Mathematical Optimization For Skill-Centric Talent Acquisition

Authors: P. Ramulu, Ch. Janaiah, A. Manoj Kumar

Abstract: The increasing demand for skilled professionals in dynamic digital labor markets has exposed significant limitations in traditional recruitment systems, which often rely on resumes and subjective evaluation criteria. These methods frequently fail to capture candidates’ true competencies, resulting in inefficient hiring decisions and suboptimal talent utilization. This study proposes an integrated framework that combines machine learning techniques with mathematical optimization to enable skill-centric talent acquisition. The objective is to develop a robust, data-driven hiring model that emphasizes measurable performance and objective evaluation. The proposed system leverages machine learning algorithms to analyze job requirements, extract relevant skill features, and generate customized evaluation tasks aligned with real-world problem scenarios. Candidates are assessed based on their ability to solve these tasks, ensuring that recruitment decisions are grounded in practical competency rather than self-reported qualifications. The evaluation process incorporates multiple performance indicators, including solution accuracy, logical reasoning, efficiency, and quality of implementation. These parameters are quantified and used as inputs to a mathematical optimization model designed to rank candidates and identify the best match for a given role. A key contribution of this work is the formulation of an optimization framework that maximizes skill compatibility between candidates and job requirements while minimizing hiring time and evaluation bias. By integrating predictive analytics with optimization techniques, the system enhances decision-making accuracy and ensures consistency in candidate selection. Furthermore, the framework supports scalability and adaptability, making it suitable for diverse recruitment scenarios, particularly in freelance and project-based environments. The results demonstrate that the integration of machine learning and mathematical optimization significantly improves recruitment efficiency, transparency, and fairness. The proposed approach reduces reliance on subjective judgment, streamlines the screening process, and promotes merit-based hiring practices. leading to improved project outcomes and workforce productivity. Additionally, it facilitates better alignment between organizational needs and candidate capabilities, leading to improved project outcomes and workforce productivity. In conclusion, this study establishes a comprehensive methodology for intelligent, skill-focused recruitment by bridging the gap between artificial intelligence and mathematical modeling. The proposed framework provides a scalable and objective solution for modern talent acquisition challenges, with potential applications in online hiring platforms, corporate recruitment systems, and digital freelancing ecosystems.

DOI: https://doi.org/10.5281/zenodo.19468185

Properties of an Unbounded Solutions of a Fourth Order Neutral Dynamic Equations

Authors: Ramireddy Pasupula

Abstract: In this paper, the author studied, the properties of an unbounded solutions of a class of fourth order dynamic equation of the form (r(t) (x(t)+p(t)x(α(t)))^(∆^2 ) )^(∆^2 )+g(t)G(x(β(t)))-h(t)H(x(γ(t)))=0 (H) for 〖t∈[t_0,┤ ├ ∞)〗_T where 𝕋 is a time scale with⁡sup⁡〖T=+∞〗,t_0 (≥0)∈T are studied under the assumption H_0 ∫_(t_0)^∞▒〖t/(r(t)) ∆t=∞ 〗and ((〖H_0〗^’ ∫_(t_0)^∞▒〖t/r(t) ∆t<∞〗 for various ranges of p(t).

DOI: https://doi.org/10.5281/zenodo.19468299

Learning-Augmented Robust Dynamic Network Flow With Theoretical Guarantees

Authors: G.Yuvaroopa Lakshmi, K.Saraswati, D.Sattemma

Abstract: Dynamic network flow problems are central to applications such as transportation systems, communication networks, and real-time logistics, where edge capacities and demands evolve over time under uncertainty. Traditional approaches either adopt worst-case assumptions, leading to overly conservative solutions, or rely on stochastic models that may lack strong robustness guarantees. This gap motivates the integration of predictive, data-driven insights into algorithmic frameworks while preserving rigorous performance assurances. In this paper, we present a learning-augmented robust framework for dynamic minimum cost flow problems. We consider a time-indexed networkG_t = (V,E,c_t,w_t), where capacities and costs vary dynamically, and incorporate partial forecasts provided by a machine-learned oracle. Our approach combines online primal-dual optimization techniques with robust correction mechanisms to effectively handle inaccuracies in predictions. The proposed framework treats predictions as advisory inputs while ensuring feasibility under adversarial deviations. A tuneable robustness parameter is introduced to balance efficiency and resilience, enabling improved performance when predictions are accurate and controlled degradation when they are not. We establish strong theoretical guarantees, including consistency, robustness bounds, regret analysis, and competitive ratios. These results demonstrate that the algorithm achieves near-optimal performance under accurate predictions while maintaining bounded worst-case losses under prediction errors. Experimental evaluations on dynamic transportation and communication network scenarios show that our method significantly outperforms classical robust and stochastic approaches. The results indicate improved efficiency, reduced congestion, and enhanced adaptability to real-time changes. Overall, this work highlights the effectiveness of integrating machine learning with robust optimization to address complex, time-evolving network flow challenges with both practical and theoretical reliability.

DOI: https://doi.org/10.5281/zenodo.19468932