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• A review of waste management optimization models (WM-OMs). 2. • A novel characterization method for examining and benchmarking the features WM-OMs. 3. • A pioneering guide for the selection of proper uncertainty analysis approaches. 4. • A compilation of characteristics of the papers reviewed and their data (Appendix). 5. • A perspective on the design of WM-OMs for different applications and stakeholders. 6. The transition to a circular economy through sustainable waste management (WM) follows different paths in each region, depending on its socioeconomic conditions and existing infrastructure. Mathematical optimization models are rigorous tools for informing local decision-making and identifying WM policy levers based on a variety of configurations. This review explores the pathways taken when designing WM optimization models (WM-OMs) that establish a network of waste valorization technologies. To standardize the literature review process, we propose a novel characterization method for examining, relating, and benchmarking the features of WM-OMs. After a thorough review of 58 articles published between 2015 and 2022, we assembled a comprehensive database to document the characteristics of these papers and the type of data reported in their case studies. We aim to provide a solid foundation for streamlining and enhancing future WM-OMs. Our work identifies various opportunities to improve the accuracy and reliability of WM-OMs. They include modeling thermo-chemical reactions in WM processes; considering regulatory, environmental, and political constraints; recognizing the informal sector; exploring the impact of marketing mechanisms on waste prevention and recycling; improving the traceability of case study data; specifying the rationale for uncertainty analysis (UA); and indicating the mathematical model (type, optimization algorithm, and equations). As many WM-OM authors have implemented UA without justifying their method choices, our review provides a pioneering guide for selecting the UA approach. Finally, we discuss the need for a trade-off between performance and practicality as models become more complex, making it critical to consider the specific needs of stakeholders. [ABSTRACT FROM AUTHOR]

Determining the mining sequence is one of the main objectives in mine planning. However, depending on the size of the analyzed instances, such activity might become an extremely difficult task, despite current computational capacity. In addition, determining a feasible and operational mining sequence is also challenging, so practitioners usually employ strategies to segment and simplify the main problem, such as splitting it into distinct time horizons and aggregating blocks into clusters. This paper aims to perform a critical review about the different clustering methodologies and algorithms used for mining-block aggregation, with the purpose of understanding the proposed solutions and identifying the gaps found in the current literature. The reviewed aggregation strategies encompass the modelling of tabular deposits as sets of layers and grouping of blocks in benches, bench-phases, and mining cuts. Among the optimization techniques evaluated, one may find heuristics, artificial intelligence, and exact approaches, relying on deterministic or uncertainty-based methodologies, considering approximately six decades of studies and covering fifty-eight works published in journals and proceedings from 1967 to 2022. In addition to what is seen within the literature analyzed, we also propose future research directions, such as approaches and algorithms not yet implemented to solve the block aggregation problem, thus presenting opportunities for further research in this field. [ABSTRACT FROM AUTHOR]

Determining the mining sequence is one of the main objectives in mine planning. However, depending on the size of the analyzed instances, such activity might become an extremely difficult task, despite current computational capacity. In addition, determining a feasible and operational mining sequence is also challenging, so practitioners usually employ strategies to segment and simplify the main problem, such as splitting it into distinct time horizons and aggregating blocks into clusters. This paper aims to perform a critical review about the different clustering methodologies and algorithms used for mining-block aggregation, with the purpose of understanding the proposed solutions and identifying the gaps found in the current literature. The reviewed aggregation strategies encompass the modelling of tabular deposits as sets of layers and grouping of blocks in benches, bench-phases, and mining cuts. Among the optimization techniques evaluated, one may find heuristics, artificial intelligence, and exact approaches, relying on deterministic or uncertainty-based methodologies, considering approximately six decades of studies and covering fifty-eight works published in journals and proceedings from 1967 to 2022. In addition to what is seen within the literature analyzed, we also propose future research directions, such as approaches and algorithms not yet implemented to solve the block aggregation problem, thus presenting opportunities for further research in this field. [ABSTRACT FROM AUTHOR]

Intelligent Reflecting Surfaces (IRS) have revolutionized wireless communication systems through their adaptive reconfiguration of the radio propagation environment. This paper compares an IRS-assisted non-orthogonal multiple access (NOMA) system and a relay-assisted NOMA system. The investigation delves into the impact of the number of IRS elements on the performance of a two-user NOMA system. A dynamic power allocation scheme is proposed based on the number of IRS elements. Specifically, analytical investigations reveal the range, upper, and lower bounds of power allocation coefficients. The asymptotic values of these bounds are also determined. Furthermore, an optimization problem is formulated to maximize the sum-rate while considering individual minimum rate constraints. The minimum number of passive reflecting elements required by the IRS is determined to meet the minimum target data rates for both users. Analytical calculations determine the optimal number of IRS elements, minimizing total power consumption. Simulation results confirm the superiority of the IRS over the decode-and-forward relay approach. • Dynamic power allocation in IRS assisted NOMA system. • Performance comparison between IRS assisted and relay assisted systems. • Optimal number of IRS elements for energy efficient communication. • Design of a separate phase shift matrix for each user to maximize the system's overall sum-rate. • Optimal power allocation using Karush–Kuhn–Tucker (KKT) optimization technique. [ABSTRACT FROM AUTHOR]

Computational modeling plays an increasingly important role in neuroscience, highlighting the philosophical question of how computational models explain. In the particular case of neural network models, concerns have been raised about their intelligibility, and how these models relate (if at all) to what is found in the brain. We claim that what makes a system intelligible is an understanding of the dependencies between its behavior and the factors that are responsible for that behavior. In biology, many of these dependencies are naturally "top-down", as ethological imperatives interact with evolutionary and developmental constraints under natural selection to produce systems with capabilities and behaviors appropriate to their evolutionary needs. We describe how the optimization techniques used to construct neural network models capture some key aspects of these dependencies, and thus help explain why brain systems are as they are — because when a challenging ecologically-relevant goal is shared by a neural network and the brain, it places constraints on the possible mechanisms exhibited in both kinds of systems. The presence and strength of these constraints explain why some outcomes are more likely than others. By combining two familiar modes of explanation — one based on bottom-up mechanistic description (whose relation to neural network models we address in a companion paper) and the other based on top-down constraints, these models have the potential to illuminate brain function. [ABSTRACT FROM AUTHOR]

In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. The proposed AO-AOA algorithm follows two strategies to find a better optimal solution. The first strategy is to introduce an energy parameter (E) to balance the transition between the individuals' procedure of exploration and exploitation in AO-AOA swarms. Next, a piecewise linear map is employed to reduce the energy parameter's (E) randomness. To evaluate the performance of the proposed AO-AOA algorithm, it is tested on two well-known power systems i.e., IEEE 30-bus test network, and IEEE 118-bus test system. Moreover, to validate the effectiveness of the proposed (AO-AOA), it is compared with a famous optimization technique as a competitor i.e., Teaching-learning-based optimization (TLBO), and recently published works on solving OPF problems. Furthermore, a robustness analysis was executed to determine the reliability of the AO-AOA solver. The obtained result confirms that not only the AO-AOA is efficient in optimization with significant convergence speed, but also denotes the dominance and potential of the AO-AOA in comparison with other works. [ABSTRACT FROM AUTHOR]