K. N. Toosi University of Technology

A Generalized Order-Reduction Method for Transfer Function and Pole–Zero Analysis of Linear Analog Circuits

Accurate pole–zero determination is essential for predicting the frequency response, stability, and bandwidth of analog circuits. Conventional transfer function derivations based on nodal or mesh analysis, however, often produce lengthy symbolic expressions and provide limited ph...

Design, modeling, and nonlinear model predictive control of a novel fully actuated uav for safe transport of hazardous liquids

Abstract Transporting hazardous liquids with unmanned aerial vehicles (UAVs) demands exceptionally high precision and stability in flight control to prevent catastrophic accidents caused by the destructive fluid sloshing phenomenon. This paper presents the design, mathematical mo...

Enhanced retail customer equity prediction using a hybrid machine learning and Markov chain model

Purpose This study aims to improve the accuracy of customer equity (CE) estimation in complex retail environments by introducing a hybrid framework that integrates machine learning (ML) with Markov Chain modeling, enabling dynamic prediction of CE in settings characterized by fre...

Durability Index and Microstructure Evolution of Magnesium Sulfate-Exposed One-Part and Two-Part Alkali-Activated Slag Concretes

Abstract Durability enhancement of low-carbon alkali-activated cements, as an emerging alternative to portland cement, is imperative for optimal performance in various conditions. This study examines the influence of one-part and two-part activators ( Na 2 SiO 3 · 5 H 2 O , Na 2...

Developing an epidemic-adjusted walkability index to analyze pandemic-resilient urban areas: a data driven GIS-based multi-criteria approach

The infectious disease pandemic highlighted the critical role of the spatial structure of cities in shaping infectious-disease transmission dynamics. This study develops a health-oriented framework to investigate the spatiotemporal association between urban walkability and the in...

Design of extremal reentrant auxetics using optimization algorithms

A comprehensive understanding of the optimal achievable ranges of their key mechanical properties and the tradeoffs that govern them, which is foundational for the targeted design of multifunctional, performance-optimized lattices, remains elusive. The purpose of this study is to...

Bridging efficiency and heat-generation analysis in perovskite solar cells through integrated opto–electro–thermal modeling

This study presents a comprehensive numerical opto-electro-thermal simulation framework for planar and half-tandem perovskite solar cells (PSCs), emphasizing the critical role of absorber-layer thickness in device optimization. Using CsPbI 3 and FASnI 3 as absorbers, we systemati...

A hybrid 3D-CNN and CGAN approach for bipolar disorder diagnosis using VBM and SBC features from multimodal MRI

Neuroimaging technologies like structural and functional MRI (sMRI/fMRI) have identified biomarkers for bipolar disorder (BD). Deep learning approaches have shown promising performance in analyzing such data; however, many multimodal models rely on complex architectures with limi...

Application of Medicare Part D data in Alzheimer’s disease and related dementias: a scoping review

Although Medicare Part D data are extensively used in pharmacoepidemiologic research, no previous synthesis has assessed their role in studies of Alzheimer’s disease and related dementias (ADRD). This scoping review aims to map how Medicare Part D data have been employed to exami...

Classification of sleep apnea based on EEG signals using ensemble learning methods

Sleep Apnea Syndrome (SAS) is a common disorder characterized by repeated cessation of airflow during sleep. Accurate classification of its main subtypes—Obstructive Sleep Apnea (OSA), Central Sleep Apnea (CSA), and Normal Breathing (NB)—is essential for effective clinical manage...

Safe and adaptive control of non-stationary stochastic systems via Lyapunov-constrained distributional reinforcement learning

Abstract Non-stationary environments pose significant challenges for reinforcement learning (RL), particularly in safety-critical applications like robotics and energy systems, where adaptability, stability, and robustness to uncertainty are essential. This paper introduces Lyapu...

Upcycling copper chips into Cu–Fe alloys via coating-assisted plasma sintering

Upcycling metal machining waste into functional engineering materials offers a sustainable pathway for value-added manufacturing. In this work, a new coated chip plasma sintering (CCPS) strategy was introduced to produce Cu–Fe alloys from discarded copper chips. Copper chips gene...

Extreme vulnerability to intruder attacks destabilizes network dynamics

Abstract Consensus, synchronization, formation control, and power grid balance are examples of desirable dynamical states that arise in networks. Here we investigate how such states can be destabilized by an intruder agent within an otherwise functioning network. We see that a si...

Performance Analysis of Q-Learning; DQN-Enhanced PID Controllers for Aircraft Trajectory Tracking Under Wind Disturbances

Traditional fixed-gain proportional–integral–derivative (PID) controllers often exhibit limited adaptability when regulating the longitudinal motion of passenger aircraft under varying flight conditions and external disturbances. This study proposes an adaptive control framework...