Engineering College, Ajmer, generally referred to as ECA, (formerly known as Govt. Engineering College or GECA) is a public state technical college located in Ajmer, Rajasthan, India. It was established in 1997.
Traffic flow prediction is necessary for the successful functioning of the urban transport system, as it allows predicting traffic flow and managing it in advance, reducing congestion and increasing urban mobility. The paper presents an optimal Bird Swarm Graph Neural Network (BS-GNN) predictive model of traffic flow in urban road networks in both space and time. The proposed model represents intersections and road segments as graph nodes, captures spatial dependencies through graph neural network operations, and learns temporal dynamics via sequential propagation. Bird Swarm Optimization (BSO) is used to tune network parameters dynamically to achieve better prediction. Extensive simulations indicate that BS-GNN is more successful than normal GCN, GAT, and ST-GCN models, with a Mean Absolute Error (MAE) of 5.82 vehicles/hour, Root Mean Square Error (RMSE) of 9.12 vehicles/hour, Mean Absolute Percentage Error (MAPE) of 4.12, R2 score of 0.962, and congestion F1-score of 0.91 at a prediction horizon of 5 min. The model also performs well, with an MAE of 6.75 vehicles/hour, an RMSE of 10.32 vehicles/hour, an MAPE of 5.21, an R2 of 0.951, and an accuracy of 94.6. The model can also be easily scaled to networks of 1000 nodes, and the computation time per epoch is 28.4 s, which is impressive, as maintain reliable performance even under peak traffic, congestion, and incidents. These findings substantiate that BS-GNN is a valid, scalable, and high-accuracy solution for real-time traffic prediction and smart transportation systems.
This research develops a multi-objective optimization framework for thermal Zero Liquid Discharge (ZLD) systems integrating Multi-Effect Distillation (MED), Brine Concentrator (BC), and Brine Crystallizer (BCR) for sustainable seawater desalination in coastal regions. The work addresses water scarcity while ensuring complete elimination of liquid waste. A validated MATLAB thermodynamic model (2–6
Antennas play a crucial role in both high-frequency and multi-band wireless communication however, the traditional antenna design methods struggle in achieving multiple resonant frequencies within a single structure. Thus, a novel Omega-Phi Fractal Hexa-Band Square Planar Array Antenna (OFSP-Hex Antenna) is designed to enable robust operation across L, S, C, X, E, and UHF bands. Initially, the nonlinear interactions of surface waves cause resonance shadowing, while the anisotropic behavior of substrate materials induces impedance mismatching. To address this issue, a Fractal phi-shaped omega structure is modelled where the fractal geometry uses its self-similarity for multi-band operation, and the phi-omega structure act as a trap tuner for achieving impedance matching. Additionally, in modern antennas, phase control and beam steering complicate uniform phase distribution across the array whose dense packaging intensifies coupling effects. Hence, an Osprey-Walrus Integrated Roger Substrate (OWIRS-R04003C) is integrated, which enables uniform wave propagation with its dielectric loss and stable permittivity, and optimizes the phase-shifter configurations by tuning spatially variant phase delays. Meanwhile, the metamaterials in the structure induce Surface Plasmon Resonance (SPR) effects, which absorb electromagnetic energy, leading to reflection losses. These issues are mitigated by a Perovskite metasurface microwave absorber that strategically absorbs and dissipates the electromagnetic energy using its high-loss tangent and tunable permittivity. Experimental evaluations with higher efficiency, isolation level, higher gain, and bandwidth witness the antenna’s multiband operation over existing methods.
Accurate estimation of global solar radiation (GSR) is vital for integrating solar energy into power grids and optimizing photovoltaic performance. However, the availability of reliable solar radiation data is often limited in remote or rural areas due to the high cost and complexity of direct measurements. This study presents a comprehensive review of solar radiation prediction models, outlining empirical, statistical, and machine learning approaches such as artificial neural networks, fuzzy logic, and hybrid models. Their strengths and limitations in addressing atmospheric nonlinearity and data scarcity are discussed. Building upon the insights gained from the review, the random forest (RF) machine learning model is employed to predict GSR and assess the solar energy potential across 28 districts in Tamil Nadu, India. The RF model is developed using input parameters such as month number, latitude, longitude, and minimum and maximum temperature, while GSR serves as the output variable. RF model performance is validated using experimental India Meteorological Department data. The predicted and observed values show strong agreement, with a correlation coefficient of 0.9714 and an RMSE of 0.7464 for Chennai. The estimated annual GSR ranges from 17 to 21 MJ/m2/day, highlighting the region’s significant potential for solar energy development.
