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In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]

In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]

Motivated by the growing significance of solar energy, the paper presents the findings of the maiden study on the long-term seasonal performance assessment of three PV module technologies—monocrystalline (m-Si), polycrystalline (p-Si), and amorphous silicon (a-Si) housed in a 1 MW solar plant installed at a local steppe climate in Gandhinagar, Gujarat, India using the key performance indicators (KPIs), daily power generation, final yield (Y f), reference yield (Y r), total energy loss (T EL) and performance ratio (PR). Notably, the am-Si PV modules were found to be the best performing technology, with an average PR of 71.26% and the lowest average total energy loss of 631 h during the monitored period. Moreover, PV systems have become more popular as a source of green, clean energy; yet, they have low base-load energy sustainability. Explainable AI (XAI) aids in understanding predictions made by the machine learning models. The importance of XAI lies in the comprehension and trust which is embedded into the results of machine learning algorithms. Therefore, the present work builds a comprehensive XAI model for predicting KPIs and assesses the performance of several learning algorithms using R-value, MAE, the number of iterations, and execution time. The Levenberg-Marquardt algorithm predicts the KPIs for p-Si, am-Si, and m-Si with prediction accuracies of 98.63%, 98.58%, and 90.09%, respectively. In summary, this study not only advances our understanding of PV module performance in a steppe climate context but also contributes to the broader field by integrating an XAI model for transparent and reliable predictions of the KPIs. [Display omitted] • Comparison of m-Si, p-Si, and a-Si PV modules in a 1 MW solar plant, Gandhinagar, India, showed long-term performance. • a-Si PV modules outperformed with a 71.26% PR and the lowest 631 hours energy loss, surpassing m-Si and p-Si technologies. • Study underscores XAI's role in ML predictions, developing a robust model to predict KPIs and evaluate learning algorithms. • Levenberg-Marquardt showed high accuracy: 98.63% for p-Si, 98.58% for a-Si, and 90.09% for m-Si PV modules. [ABSTRACT FROM AUTHOR]

Accurately forecasting energy demand can better predict future changes in energy demand. This study is aimed to develop more accurate clean energy prediction model from two perspectives of the modeling mechanisms and improvements on the model structure. For this purpose, a novel Riccati–Bernoulli differential equation is established through social practice theory, supply–demand relationship, and the preference of consumption in the energy economy. Considering the limited information in observation data, this differential equation is then transformed into a grey Riccati–Bernoulli model (GRBM(1,1)) according to the differential information principle. With the response function solved on the basis of polynomial equation theory and the power exponent optimized combining the modified flower pollination algorithm, the main process of GRBM(1,1) can be summarized. The four validation examples are provided for confirming the effectiveness and reliability of the new model by comparing with other existing models. Finally, the proposed model is employed to estimate and forecast the clean energy consumption in China and India. The results show that the proposed model demonstrates better estimation in all cases and efficiency in short-term clean energy consumption forecasting. Therefore, by using this optimum model, China's preference coefficient in total energy consumption is 0.7103, lower than that of India (0.8799), and China's preference coefficient in clean energy consumption is 0.9665, higher than that of Indian (0.7155), which means China has less interest to increase total energy consumption but more interest in popularizing the clean energy. • This paper proposed a novel grey Riccati–Bernoulli model for energy consumption prediction. • A specific solution algorithm for this Riccati–Bernoulli equation is discussed in this research. • We compared the proposed novel grey model with other 12 exiting prediction models. • The novel model also reveals China's effort in popularizing the use of clean energy. [ABSTRACT FROM AUTHOR]

In this article, a new optimization technique known as Kho-Kho optimization (KKO) algorithm is presented. This proposed technique is a population based meta-heuristic method which is inspired from the strategies used by players in a well known tag-team game played in India, i.e. Kho-Kho. The performance and superiority of the proposed method with respect to other existing methods is evaluated using twenty nine benchmark functions and real-time optimization problems related to power system i.e. combined emission economic dispatch and combined heat and power economic dispatch problem. • This paper presents a new Kho-Kho optimization algorithm (KKO). • KKO is inspired by a well-known tag-team game played in India known as Kho-Kho. • KKO mimics the strategies used by a chasing team to catch the runners of a running team. • Twenty-nine benchmark functions are used to test the performance of the proposed algorithm. • Further, KKO is used to solve real-time optimization problems of power system. [ABSTRACT FROM AUTHOR]