Maharaja Ranjit Singh Punjab Technical University

A novel reduction approach is introduced to design the controller and simplify the complexity of large-scale continuous dynamic systems. This technique involves a generalized adaptation of the standard pole clustering method, which is used to derive the reduced denominator coefficients for the simplified model. The numerator polynomial coefficients are then determined using the Cauer second form. The generalized pole clustering (GPC) algorithm ensures that the key characteristics, such as stability and dominant poles, are preserved in the reduced system. To validate the effectiveness of the proposed method and gauge the closeness of the reduced model to the original system, various performance error indices are calculated. The technique has been applied to several benchmark systems, consistently yielding minimal error indices. After obtaining the reduced-order model, its transfer function is used to design the PID and lead/lag compensators via a moment matching algorithm. When the controller designed from the reduced model is applied to the original dynamical system, the closed-loop plant gives approximately the same response as required. Additionally, unit step responses and time domain specifications of the closed-loop plants are evaluated to demonstrate the usefulness of the proposed algorithm.

The present study investigates the effect of Strontium (Sr) doping in a series of materials having nominal composition Y1-xSrxFe0.5Co0.5O3 (x = 0.1,0.2), synthesized from high-purity nitrates using the solid-state reaction method. X-ray diffractometry (XRD) studies confirms that the material exhibits a single-phase orthorhombic perovskite structure with Pnma space group symmetry exhibiting a consistent peak shift (Δ2θ = 0.15–0.35°) and increased lattice strain, indicating Sr-induced structural deformation. Furthermore, Rietveld refinement of the x-ray data verifies that the Sr2+ ions replace the Y3+ ions at the A-site of perovskite lattice in YFe0.5Co0.5O3. The charge imbalance caused by Sr2⁺ substitution at the Y3⁺ site is compensated through the creation of oxygen vacancies and by creation of mixed valence states of Fe3⁺/Fe2⁺ and Co3⁺/Co2⁺. The presence of these defects increases the concentration of charge carriers, concurrently leading to lattice disorder and the localization of carriers. Additionally, Field Effect Scanning Microscopy (FESEM) investigations reveal that the synthesized materials have porous microstructure with crystallite sizes ranging from 160 to 240 nm. Moreover, the dielectric response exhibits negative permittivity (ε′ < 0) within the frequency range of 104–105 Hz. This behavior can be explained by defect-mediated polarization mechanisms, specifically Maxwell–Wagner interfacial polarization and localized hopping conduction, instead of true metallic transport. Impedance analysis demonstrates a predominant semicircular response in Nyquist plots, whereas the Z″ versus frequency spectra display two relaxation peaks that correspond to contributions from grain and grain boundary effects. The relaxation time is approximately in the range of 10⁻⁸ to 10⁻⁷ seconds. The relaxation peak frequency exhibits a nearly constant value across the temperature range of 383–1083 K, suggesting a non-thermally activated relaxation behavior. An explanation of the observed negative permittivity behavior has been provided using the Drude-Lorentz model. Overall, lattice strain and oxygen vacancies have a significant impact on the small polaron hopping between Fe3⁺/Fe2⁺ and Co3⁺/Co2⁺ states that governs the conduction process. The metamaterial-like dielectric response shown at low frequencies shows that this system might be useful for electromagnetic shielding and adjustable dielectric devices.

Neurodegenerative disorders and age-related cognitive decline remain significant global health challenges, with current therapies offering only symptomatic relief. Flavonoids have emerged as a promising alternative due to their multi-target actions and safety. One such is wogonin (5,7-dihydroxy-8-methoxyflavone), a root-specific flavone from Scutellaria species, which has shown strong neuroprotective potential. This review provides a comprehensive overview of biosynthesis, pharmacokinetics, and mechanistic insights into wogonin, which confer neuroprotection and cognitive resilience. Evidence from cellular and animal models highlights its ability to cross the blood-brain barrier and modulate key signaling pathways, including NF-κB, MAPK, PI3K/Akt, Nrf2/ARE, AMPK, and CREB/BDNF, thereby attenuating oxidative stress, neuroinflammation, mitochondrial dysfunction, and synaptic loss. Furthermore, we summarize preclinical and emerging clinical evidence supporting its role in aging-associated neurodegenerative disorders.

Artificial Intelligence (AI) is rapidly reshaping agricultural systems by enabling data-driven decision-making, predictive analytics, and automated farm management. This review critically synthesizes recent advances in computational models and algorithms applied to sustainable and climate-smart agriculture. It examines the role of machine learning techniques (e.g. Random Forest, Support Vector Machines, XGBoost), deep learning architectures (e.g. Convolutional Neural Networks, recurrent models, transformer-based frameworks), and computer vision systems for crop monitoring, disease detection, yield prediction, livestock management, and precision irrigation. The review further analyzes the integration of Internet of Things (IoT) sensor networks with AI models for real-time data acquisition and adaptive control. Emerging approaches, including generative AI and large language models, are evaluated for their potential to provide context-aware advisory systems and decision support. Across applications, AI demonstrates measurable improvements in prediction accuracy, input optimization, early stress detection, and climate risk forecasting. However, challenges related to model generalization, data heterogeneity, computational scalability, and deployment in resource-constrained environments remain significant barriers. The study identifies immediate research priorities in robust model design and explainable AI, as well as long-term directions toward autonomous, self-learning agricultural ecosystems. Overall, this review highlights how advanced computational methods are driving the transition toward intelligent, resilient, and sustainable agricultural systems.

The growing demand for real-time, low-power image analytics in applications such as remote sensing, environmental monitoring, and precision agriculture has significantly increased the importance of hyperspectral image classification (HSIC). Hyperspectral imaging captures rich spectral–spatial information; however, the high dimensionality and substantial computational requirements of conventional deep learning models limit their suitability for deployment on edge and resource-constrained devices. This review analyzes a range of efficient and lightweight architectures, including state-space Mamba models, lightweight transformers, and convolutional neural network (CNN)–based approaches, which represent notable advancements in lightweight deep learning for HSIC. Particular emphasis is placed on key methodologies, such as residual learning, spectral–spatial feature fusion, and attention-based mechanisms. Furthermore, this review explores current trends related to performance–efficiency trade-offs, evaluation metrics, model compactness, and real-world deployment challenges relevant to HSIC. By systematically analyzing and summarizing recent lightweight deep learning models and techniques, this paper provides a comprehensive overview of the challenges, available tools, and future research directions for implementing deep learning–based hyperspectral imaging solutions in resource-limited environments.