Electric vehicles’ (EVs’) accelerated expansion is changing transportation systems and making managing the charging infrastructure and power grid much more difficult. In order to handle issues like demand variations, charging congestion, grid instability, intermittent renewable energy, and cybersecurity risks, intelligent coordination techniques are needed. Through data-driven forecasting, predictive control, and automated decision-making, artificial intelligence (AI) has become a crucial enabling technology for optimizing EV charging networks. An organized and thorough analysis of AI methods used in intelligent infrastructure for EV charging is presented in this research. System architecture, operational difficulties, optimization techniques, deployment obstacles, and new research avenues are all methodically examined in this paper. To guarantee openness and repeatability, a PRISMA-based approach for choosing literature is used. The scalability and practical viability of major AI technologies are compared, including deep learning for predictive maintenance, optimization-based scheduling, machine learning for demand forecasting, and reinforcement learning in order for real-time charging control. Additionally, this assessment highlights important implementation gaps in the areas of economic modeling, cybersecurity integration, distributed intelligence, and standards. Future research avenues for secure, self-optimizing, and autonomous charging ecosystems are explored. Academics, system operators, and politicians working toward the widespread implementation of AI-enabled EV infrastructure can use the findings as technical assistance.
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.
Helium–oxygen mixtures (HeliOx) have been proposed to enhance aerosol drug delivery by reducing airflow turbulence and facilitating deeper penetration of medications into the respiratory tract. However, previous studies on HeliOx efficacy have yielded mixed results, often limited by unrealistic anatomical models and steady-state assumptions, and focus on restricted sections of the airway. This study aims to address these limitations by investigating the transport and deposition of pharmaceutical aerosols in HeliOx compared to atmospheric air using a realistic, computed tomography-based nose-to-lung respiratory tract model that extends from a face mask to the 13th generation of tracheobronchial airways. Experimentally measured, dynamic breathing profiles were incorporated to simulate realistic inhalation conditions. Pharmaceutical aerosols with diameters ranging from 1 to 100 µm were released under two conditions: ambient air and HeliOx. Computational fluid–particle dynamics simulations were performed to model and visualize the transport and deposition patterns of aerosols throughout the respiratory tract during inhalation. The simulation results indicate that HeliOx promotes more steady airflow with reduced turbulence, facilitating a deeper delivery of drug aerosols compared to ambient air. Specifically, HeliOx reduced turbulence intensity in the nasal cavity, larynx, and upper trachea regions, and enhanced aerosol penetration to the lower respiratory tract, increasing the deposition fraction in the deeper lung regions. These findings suggest that utilizing HeliOx as a carrier gas can improve deep lung-targeted drug delivery, potentially enhancing therapeutic outcomes for pulmonary diseases.
Reproductive phenology provides insights into plant adaptation strategies under changing climates, and thus requires extensive studies on intraspecific variations across climate gradients. In this study, we examined the reproductive phenology of the Himalayan wild cherry (Prunus cerasoides Buch. -Ham. Ex D. Don) at two climatically contrasting sites – the tropical Mizoram, part of the Indo-Burma region, and the temperate Uttarakhand, part of the western Himalayas, between 2019 and 2023. Monthly climatic variations in temperature, rainfall, humidity, and wind speed, as well as the reproductive phenological observations of the flowering and the fruiting phases, were recorded. Comparative analyses revealed an earlier and shorter flowering period in Uttarakhand compared to Mizoram, suggesting site-specific adaptive responses of the Himalayan wild cherry. We developed and implemented a staggered machine learning pipeline using regularised regression models (Lasso, Elastic Net and Ridge) to predict four key events: first flowering day, peak flowering day, last flowering day and the fruit drop day, using site-specific monthly climatic data. Temperature, rainfall, and their interaction were the major determinants of reproductive timing, contributing to nearly 90
The growing demand for sustainable energy solutions has driven the development of domestic biogas cookstoves as an eco-friendly alternative to conventional cooking methods. With biogas, emerging as a key renewable fuel, optimizing cookstove performance has become essential. This study investigates the design and optimization of a domestic biogas cookstove burner mixing tube assembly using a numerical methodology to assess its flow characteristics and combustion performance. The aim is to enhance the uniformity of the fuel flow, port exit velocity and quality of mixture at the ports to improve the combustion performance and flame stability. Additionally, the impact of fuel flow rate, loading height, and vessel size on the overall heat transfer rate and thermal efficiency has been examined. The influence of key design parameters including mixing tube geometry, fuel nozzle size, fuel pressure at nozzle inlet and primary air entrainment through annular and side ports, was systematically investigated. By incorporating a 100
