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In the evolving field of solar energy, precise forecasting of Solar Irradiance (SI) stands as a pivotal challenge for the optimization of photovoltaic (PV) systems. Addressing the inadequacies in current forecasting techniques, we introduced advanced machine learning models, namely the Rectified Linear Unit Activation with Adaptive Moment Estimation Neural Network (RELAD-ANN) and the Linear Support Vector Machine with Individual Parameter Features (LSIPF). These models broke new ground by striking an unprecedented balance between computational efficiency and predictive accuracy, specifically engineered to overcome common pitfalls such as overfitting and data inconsistency. The RELAD-ANN model, with its multi-layer architecture, sets a new standard in detecting the nuanced dynamics between SI and meteorological variables. By integrating sophisticated regression methods like Support Vector Regression (SVR) and Lightweight Gradient Boosting Machines (Light GBM), our results illuminated the intricate relationship between SI and its influencing factors, marking a novel contribution to the domain of solar energy forecasting. With an R2 of 0.935, MAE of 8.20, and MAPE of 3.48%, the model outshone other models, signifying its potential for accurate and reliable SI forecasting, when compared with existing models like Multi-Layer Perceptron, Long Short-Term Memory (LSTM), Multilayer-LSTM, Gated Recurrent Unit, and 1-dimensional Convolutional Neural Network, while the LSIPF model showed limitations in its predictive ability. Light GBM emerged as a robust approach in evaluating environmental influences on SI, outperforming the SVR model. Our findings contributed significantly to the optimization of solar energy systems and could be applied globally, offering a promising direction for renewable energy management and real-time forecasting. [ABSTRACT FROM AUTHOR]

The crystalline material that is greenish-white and dissolves in water is iron chloride. It is utilized in sewage treatment, dyeing, and medicine. Graph entropy plays a significant role in measuring the complexity of atoms, molecules, and structures in nature. It has specific chemical applications in biology, neuroscience, and chemistry. A compound's molecular structure consists of many atoms. Particularly, hydrocarbons are a chemical combination of hydrogen and carbon atoms. In this article, we discuss the entropy of the chemical structure Iron (II) Chloride. Additionally, we discuss the idea of degree-based indices and compute the Shannon entropy(ENT) using these indices. The linear regression(LR) of various indices and entropies for iron chloride, FeCl2, is also discussed. Also, we link the degree-based indices and entropies via line fit. [ABSTRACT FROM AUTHOR]

Chemical Graph entropy plays a significant role to measure the complexity of chemical structures. It has explicit chemical uses in chemistry, biology, and information sciences. A molecular structure of a compound consists of many atoms. Especially, the hydrocarbons is a chemical compound that consists of carbon and hydrogen atoms. In this article, we discussed the concept of subdivision of chemical graphs and their corresponding line chemical graphs. More preciously, we discuss the properties of chemical graph entropies and then constructed the chemical structures namely triangular benzenoid, hexagonal parallelogram, and zigzag edge coronoid fused with starphene. Also, we estimated the degree-based entropies with the help of line graphs of the subdivision of above mentioned chemical graphs. [ABSTRACT FROM AUTHOR]

Due to its high chemical and thermal stability, tri-s-triazine (g − C 3 N 4) is a potential nanomaterial used in water purification and catalysis. Tri-s-triazine is released into the environment as a contaminant since it is occasionally utilized as a precursor or byproduct in industrial processes. Based on its chemical structure, we compute the new Zagreb-type indices to learn more about its connection and bonding patterns. We may construct the entropy measure using these indices to assess the material's stability and forecast its behavior under various circumstances. We establish mathematical links between the Zagreb-type indices and entropy by performing logarithmic regression, which helps optimize its use in particular applications. Also, we use the Pearson correlation coefficient. [Display omitted] Tri-s-triazine is released into the environment as a contaminant since it is occasionally utilized as a precursor or byproduct in industrial processes. Based on its chemical structure, we compute the new Zagreb-type indices to learn more about its connection and bonding patterns. We may construct the entropy measure using these indices to assess the material's stability and forecast its behavior under various circumstances. We establish mathematical links between the Zagreb-type indices and entropy by performing logarithmic regression, which helps optimize its use in particular applications. Also, we use the Pearson correlation coefficient. [ABSTRACT FROM AUTHOR]

A zero-sum flow is an assignment of nonzero integers to the edges such that the sum of the values of all edges incident with each vertex is zero, and we call it a zero-sum k-ow if the absolute values of edges are less than k. We define the zero-sum ow number of G as the least integer k for which G admitting a zero sum k-ow. In this paper we gave complete zero-sum ow and zero sum numbers for categorical and strong product of two graphs namely cycle and paths. [ABSTRACT FROM AUTHOR]