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75 articles for “linear time”
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An Overview of Artificially Generated Neural Networks Inside the Brain’s Structure in an Alzheimer’s Disease Patient
Abstract: Alzheimer’s disease produces significant neuronal loss, while the precise mechanisms and timing are yet unknown. Other types of cell death, such necroptosis, parthanatosis, ferroptosis, and cuproptosis, need further investigation. Based on brain images of people with mild cognitive impairment, this study assesses artificial neural networks (ANNs) used to diagnose and predict Alzheimer’s disease (AD). This research was conducted considering growing recognition among researchers and medical professionals regarding the importance of …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 15, Issue 2, 2025 Read article
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Regression Analysis of Topological Indices and Physicochemical Properties of Polymer-Based Anticancer Drugs
Abstract: The research analyzed the quantitative correlation between topological indices and the physicochemical properties of selected anticancer compounds using regression analysis. Various topological descriptors—such as the First Zagreb Index, Second Zagreb Index, and Forgotten Index were calculated and regressed against key molecular features, including polar surface area, melting point, and molar refractivity. The regression analysis revealed linear correlations between these topological indices and the properties of anticancer drugs, with particularly strong …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 255–267 Read article
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LQR-Based Optimal Control of Inverted Pendulum System with State Estimation and Stability Analysis
Abstract: The inverted pendulum on a cart is a canonical benchmark problem in control systems engineering, capturing the essential challenges of stabilizing an inherently unstable, underactuated, and nonlinear plant. Classical Proportional-Integral-Derivative (PID) controllers, while widely employed in industrial practice, exhibit fundamental performance limitations when applied to such systems, primarily due to their inability to account for multivariable coupling, process noise, and the absence of a systematic optimization framework. This paper presents …
Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 1, 2026 · pp. 31–43 Read article
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SpecForesight: A Predictive Analytics Pipeline for Laptop Price Forecasting
Abstract: This paper frames laptop pricing as a supervised predictive analytics problem, transforming product specifications into feature-rich signals to forecast price with calibrated regression models and operational guardrails against drift. A structured pipeline ingests tabular listings, performs data cleaning, and engineers domain-informed features (e.g., central processing unit (CPU) family and clocks, graphics processing unit (GPU) tiering, memory/storage density, display, and touch capabilities), followed by encoding and normalization to optimize model learnability. …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 61–71 Read article
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A Geospatial Analysis of Correlation Between Built-up Area and Vegetation Coverage using Google Earth Engine in Saint Martin Island, Bangladesh
Abstract: Background: This study explores the complex interconnection between expanding human settlements and fluctuating plant life on the island of Saint Martin, Bangladesh, utilizing cutting-edge geospatial techniques through the open-source Google Earth Engine platform. The rapid population boom in Bangladesh and tourism-fueled development on Saint Martin Island stir serious worries about their ecological consequences. Methods: Remote sensing data sourced from Landsat 5 TM and Landsat 8 OLI/TIRS satellites between 1991 and …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 1, 2025 · pp. 21–29 Read article
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A study on IoT and AI for Predictive Modeling and Control of Infectious Disease Transmission
Abstract: Background: The global response to novel and recurring infectious diseases is frequently hindered by surveillance systems that are slow, siloed, and reactive. Traditional epidemiology relies on retrospective analysis of clinical reports, often missing the critical early phase of autocatalytic spread. The urgency of modern public health necessitates a shift toward real-time, predictive intelligence. Methods: This study investigates the development and deployment of a synergistic paradigm integrating the Internet of Things …
Published in International Journal of Pathogens · Vol. 2, Issue 2, 2025 Read article
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Animated Transient Response of High Voltage Power Line Towers Equipped with Nonlinear Footing Resistances
Abstract: This study focuses, among other objectives, on the use of animation techniques for visualizing the electromagnetic transients in high voltage power line towers. The model includes parameters such as the tower’s footing resistance and its location-dependent surge impedance. It describes the governing algebraic/partial differential equations expressed in terms of the current and voltage distributions as functions of time and location. The transients are assumed initiated by a lightning discharge of …
Published in Trends in Electrical Engineering · Vol. 14, Issue 1, 2024 · pp. 44–52 Read article
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Optimal PMU Placement in Power Systems Using Graph Theory and PSAT
Abstract: Phasor measurement units (PMUs) play a vital role in modern power systems by delivering synchronized, real-time measurements. These devices enhance system reliability by supporting functions such as monitoring, protection, and control. By accurately capturing voltage and current phasors across different locations, PMUs enable better situational awareness and more effective decision-making in grid operations and management. Determining the optimal location of PMUs is essential to ensure system observability, reduce installation costs, …
Published in Trends in Electrical Engineering · Vol. 15, Issue 2, 2025 · pp. 26–32 Read article
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From Differential Equations to Data Science: A Survey on Analytical Methods in Contemporary Problems
Abstract: The integration of differential equations and data science methods represents a dynamic and evolving approach to solving contemporary challenges across a wide range of disciplines, including engineering, physics, biology, economics, and finance. Differential equations have long served as fundamental tools for modeling continuous systems and processes, offering powerful insights into the behavior of natural and man-made phenomena. For example, they describe how heat diffuses through materials, how populations grow in …
Published in Recent Trends in Mathematics · Vol. 2, Issue 2, 2025 · pp. 1–6 Read article
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Intercropping Legumes Covers with Maize on Soil Moisture Improvement in Selected Dry Land Areas of Basketo Special Woreda’s, Ethiopia
Abstract: In order to increase land productivity, intercropping offers enough flexibility to grow two or more crops at the same time on the same plot of land. The advantages of intercropping systems have not been thoroughly investigated and are supported by available experimental data. The purpose of this study was to assess how intercropping affects soil moisture conservation in places under moisture stress, which affects land productivity. In this study, experimental …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 13, Issue 3, 2024 · pp. 09–18 Read article
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Topology and Geometry in Data Science: Persistent Homology and Beyond
Abstract: In recent years, the interplay between topology, geometry, and data science has gained substantial momentum, offering powerful frameworks to analyze and interpret complex datasets. Traditional statistical and machine learning methods often rely on linear or metric- based assumptions, which may fail to capture the intrinsic structure of high-dimensional or nonlinear data. In contrast, topological and geometric methods provide shape-oriented, scale- invariant tools that focus on the continuity, connectivity, and global …
Published in Recent Trends in Mathematics · Vol. 1, Issue 2, 2024 · pp. 21–27 Read article
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Synthesizing Policy Gaps, Sector-Specific Challenges, and Emerging Opportunities to Develop Novel Research Themes in India’s Circular Economy
Abstract: The concept of the Circular Economy represents an important shift away from the linear & take-make-dispose" model toward a regenerative system, with quite a number of aspects in which circularity is needed in India, due to recent decades of rapid industrialization and rising consumption. Despite supportive policy initiatives such as the National Resource Efficiency Policy and Extended Producer Responsibility frameworks, India's transition to CE remains fragmented. This synthesis aims to …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 16, Issue 1, 2026 · pp. 32–39 Read article
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Fluid-Structure Interaction Simulation of Parachute by Eulerian-Lagrangian penalty method
Abstract: In general, modeling and simulation of a model consisting of fluid and solid combinations has been considered difficult, and it has become impossible in terms of computer dependencies and accuracy to be analyzed by Fluent or other programs. The parachute evaluation process, which is historically based on a large amount of experimental data, necessitates many falling experiments. These tests can be costly and time-consuming, and they don't always allow for …
Published in Recent Trends in Fluid Mechanics · Vol. 11, Issue 2, 2024 · pp. 15–22 Read article
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Advances in Non-Invasive NIR-Techniques for Gout Detection and Measurement
Abstract: Recent advancements in non-invasive near-infrared (NIR) spectroscopy techniques have significantly improved the detection and measurement of gout, a common form of inflammatory arthritis. Traditional diagnostic methods for gout, such as joint aspiration and crystal analysis, are invasive and can be uncomfortable for patients. In contrast, NIR spectroscopy offers a promising alternative by enabling real-time, non-invasive assessment of uric acid levels and related biomarkers associated with gout. The integration of portable …
Published in International Journal of Toxins and Toxics · Vol. 2, Issue 1, 2025 · pp. 1–8 Read article
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Predicting Dielectric Constants of Polymers Using Molecular Structural Descriptors and Explainable Machine Learning: A Data-Driven Approach
Abstract: Accurate prediction of dielectric constants in polymeric materials is fundamental to the rational design of advanced electronic components, energy storage capacitors, flexible substrates, and high-frequency communication circuits. Conventional approaches to identifying suitable polymer dielectrics rely on extensive experimental synthesis and characterisation, which are both time-consuming and resource-intensive. In this work, an explainable machine learning framework is developed to predict the dielectric constant of polymers directly from molecular structural descriptors derived …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 297–304 Read article