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This paper presents an overview of pulp and paper mills (PPM) production processes, the resulting release of wastewater effluent loaded with wide range of pollutants and associated environmental impacts. The review highlighted the different types of functional materials and their modified forms employed as coagulants for pulp and paper mills industries effluent (PPME) treatment that have been intensively studied as a promising strategy for PPM to achieve cleaner and sustainable treatments in accordance with sustainable development goals (SDGs) "6-Clean water and sanitation", "9-Industry, innovation, and infrastructure", and "12-Responsible consumption and production". Standalone coagulation treatment processes are inherently ineffective towards meeting the increasingly stringent discharge requirements, coupled with their higher energy demand, and increased operational and maintenance costs. Owing to the recalcitrant nature of PPME contaminants, this review explored the effectiveness of the coagulation processes for decontamination of PPME. Furthermore, the review provides a state-of-the-art coagulation-based hybrid systems employed for enhanced PPME treatment. The process limitations, influencing factors and optimization techniques are highlighted. The review also highlights how sustained research in the subject area impacts on achieving cleaner production. The review also discusses coagulant classifications and the synergistic, antagonistic and shock load toxic effects of hybrid coagulants on toxicant biodegradation and their associated system efficiency. Moreover, it offers a guide for the development and application of sustainable hybrid-based coagulants for PPME treatment. The findings presented herein provide a vital theoretical foundation for sustainable solutions to improve coagulation-based hybrid systems efficiency and their scale-up towards potential commercialization. [Display omitted] • Pulp and paper mills produce effluents containing diverse and emerging contaminants. • Different materials and their modified forms are used as coagulants for PPME treatment. • The synergistic and shock load toxic effects of hybrid-based coagulants could be identified. • Hybrid systems are highly efficient techniques for PPME treatment. • The review highlights process weaknesses, influencing factors and optimization techniques. [ABSTRACT FROM AUTHOR]

To improve convergence speed of deep neural network trained by gradient descent methods (GDMs), this paper proposed a novel fractional order gradient descent optimizer (FOAdam) based on Caputo operator and Adam algorithm, and analyzed the impact of fractional order on convergence performance using optimization theory and simulation. Furthermore, to address the trade-off between convergence speed and precision under fixed-order conditions, a fractional order scheduler (FOS) was designed based on the Connections Cloud Model (CCM). Numerical experiments conducted on MNIST and CIFAR-10 datasets showed that FOS-FOAdam outperformed other mainstream GDMs in terms of convergence speed and precision. Finally, FOS-FOAdam was applied in recognizing and classifying image datasets of cuttings and cores at the logging site, demonstrating good prospects for practical engineering applications. [ABSTRACT FROM AUTHOR]

Partially reusable launch vehicle design has raised a great interest in order to minimize the costs of space transportation by recovering and refurbishing the first stage. Several configurations such as toss-back architectures are now operational. These concepts require additional rocket propellant in order to carry the boost-back and landing maneuvers, that induce several losses in terms of payload mass performance for the ascent mission. In order to limit these losses, this paper focuses on the design of two types of architectures to carry out the return-to-launch-site mission. The first, named glide-back, uses the main propulsion system of the first stage to perform a boost-back burn and returns to the landing site by a gliding mode using additional lifting surfaces. The second, named, fly-back, performs the return-to-launch-site mission using several air-breathing engines located in the nose of the first stage combined with lifting surfaces. In this paper, the design of a reusability kit, allowing to provide the first stage with both expendable and reusable capabilities, is investigated. This kit is composed of the lifting surfaces, the nose including the air-breathing propulsive system (for fly-back configuration), landing gears and additional avionics. This paper addresses the design of such reusability kits and presents the mission specifications, the design process relying on Multidisciplinary Design Optimization techniques, the vehicle performance and the optimal trajectories. Different analyses (aero-propulsive, optimal control, operational considerations, etc.) are detailed and trade-offs between the studied configurations are assessed. [Display omitted] • Reusable launch vehicles. • Multidisciplinary design and optimization of reusability kit. • Inclusion of aeronautical technologies in launch vehicle design. [ABSTRACT FROM AUTHOR]

• The negative stiffness inertial amplifier tuned mass dampers is introduced. • The new closed-form expressions for optimal design parameters of NSIA-TMDs. • The H 2 and H ∞ optimization based closed-form expressions for optimal design parameters. • NSIA-TMDs shows 50% better vibration reduction capacity than conventional TMD. The negative stiffness inertial amplifier tuned mass dampers (NSIA-TMD) are introduced in this paper. Another two novel tuned mass dampers such as negative stiffness tuned mass damper (NS-TMD) and inertial amplifier tuned mass damper (IA-TMD) are mathematically developed from the negative stiffness inertial amplifier tuned mass dampers (NSIA-TMD) and the static masses of these three novel dampers are retained constant. The exact closed-form expressions for optimized system parameters for these novel dampers are obtained using H 2 and H ∞ optimization techniques. The dynamic responses of the SDOF systems controlled by H 2 and H ∞ optimized novel tuned mass dampers subjected to base excitations are obtained analytically. The dynamic response reduction capacities of the novel tuned mass dampers are compared with the dynamic response reduction capacities of traditional tuned mass dampers (TMD). Therefore, the dynamic response reduction capacities of H 2 optimized NS-TMD, NSIA-TMD, and IA-TMD are significantly 45.51 % , 43.47 % , 41.08 % superior to the H 2 optimized traditional tuned mass dampers. Furthermore, the dynamic response reduction capacities of H ∞ optimized NS-TMD, NSIA-TMD, and IA-TMD are significantly 3.31 % , 8.98 % , 13.79 % superior to the H ∞ optimized traditional tuned mass dampers. The nonlinear negative stiffness inertial amplifier tuned mass dampers (NNSIA-TMD) are also introduced in this paper. As a result, the dynamic response reduction capacities of H 2 optimized nonlinear negative stiffness tuned mass damper (NNS-TMD), NNSIA-TMD, and nonlinear inertial amplifier tuned mass damper (NIA-TMD) are significantly 24.54 % , 21.92 % , 19.12 % superior to the H 2 optimized traditional tuned mass dampers. Furthermore, the dynamic response reduction capacities of H ∞ optimized NNS-TMD, NNSIA-TMD, and NIA-TMD are significantly 3.01 % , 9.04 % , 15.08 % superior to the H ∞ optimized traditional tuned mass dampers. The outcomes of this research are mathematically accurate and relevant to practical design applications. [ABSTRACT FROM AUTHOR]

• A novel full nonlinear mathematical model of asymmetric hydro turbine governing system established. • The multi-stability characteristics of as well as its generation mechanism are revealed. • The effect mechanism of water diversion system topology on the system stability is revealed. • The recommended setting values of system parameters under different topologies are provided. This paper aims to study the stability and nonlinear dynamics of hydro-turbine governing system with asymmetric water diversion system, i.e. asymmetric hydro turbine governing system, by using Hopf bifurcation theory. Firstly, the full nonlinear mathematical model of asymmetric hydro turbine governing system is established by all system components and nonlinear head loss. This model contains two units with different capacities which share a common pipeline. Based on the nonlinear mathematical model, the multi-stability characteristics of asymmetric hydro turbine governing system under load disturbance is studied by using stable domain and verified by numerical simulation. Moreover, the multi-time scale oscillation is revealed and its relationship to system multi-stability is investigated. Furthermore, the effect of system parameters and topological parameters on system stability is analyzed. Results indicate that: two stable domains are emerged under load disturbance, the overall stability of asymmetric hydro turbine governing system is determined by the intersection area of the two stable domains. In addition, the system parameters and topological parameters both have obvious effect on system stability, which can be significantly improved by reasonable tuning of system parameters and optimization of water diversion system layout. [ABSTRACT FROM AUTHOR]

Over the past few decades, a considerable increase in the number of space debris has become a serious threat to the safety of the operating satellites. So, collision avoidance maneuvers have become indispensable in providing immediate and cost-effective solutions. The unavoidable presence of uncertainty associated with any physical system adds to the challenge. In this paper, a method is presented to determine the fuel-optimal collision avoidance trajectory using a convex optimization technique while incorporating the evolution of uncertainty associated with the space objects. The safety of the satellite is ensured by the collision avoidance constraints which involve calculating the instantaneous collision probability and using the Mahalanobis distance. An avoidance region is also demarcated around a space object with uncertain states to prevent any collision. Posing the problem as a convex optimization problem helps to eliminate the problem caused by dependency of the optimization variables on the initial guesses used. However, convexification of this problem with evolving uncertainty and collision probability calculation presents a set of challenges. The evolution of the positional error covariance ellipsoid leads to a feasible non-convex space surrounding the avoidance region. Moreover, implementation of a convex optimization formulation here requires convexification of the expression for collision probability, which is originally not purely convex in nature when a Gaussian probability density function is involved. This paper presents an approach to tackle the above-mentioned challenges. The satellite is returned to its nominal orbit in a timely manner, as time spent off the nominal trajectory can often lead to disruption of the space mission. The effects of various parameters on the required Δv are also explored. • Collision avoidance maneuver is formulated as a convex optimization problem. • Collision probability calculation and nonconvex feasible region are convexified. • The optimization includes evolution of uncertainty in the states of space objects. • An approximated Mahalanobis distance leads to a more conservative approach. • The effects of orbital, control and uncertainty parameters are explored. [ABSTRACT FROM AUTHOR]

The partial denitrification (PD) coupled with anaerobic ammonium oxidation (Anammox) (PD/A) process is a unique biological denitrification method for sewage that concurrently removes nitrate (NO 3 −-N) and ammonium (NH 4 +-N) in sewage. Comparing PD/A to conventional nitrification and denitrification technologies, noticeable improvements are shown in energy consumption, carbon source demand, sludge generation and emissions of greenhouse gasses. The PD is vital to obtaining nitrites (NO 2 −-N) in the Anammox process. This paper provided valuable insight by introduced the basic principles and characteristics of the process and then summarized the strengthening strategies. The functional microorganisms and microbial competition have been discussed in details, the S-dependent denitrification-anammox has been analyzed in this review paper. Important factors affecting the PD/A process were examined from different aspects, and finally, the paper pointed out the shortcomings of the coupling process in experimental research and engineering applications. Thus, this research provided insightful information for the PD/A process's optimization technique in later treating many types of real and nitrate-based wastewater. The review paper also provided the prospective economic and environmental position for the actual design implementation of the PD/A process in the years to come. [Display omitted] • PD/A was considered the most economically and environmentally beneficial new nitrogen removal process. • Principles and recent advances of the PD/A process are discussed. • The functional microorganisms and their competition in PD/A process are analyzed. • Influencing factors control and reinforcement strategies of PD/A was classified and conceptualized. • Knowledge gaps and prospects are presented to improve the future of PD/A process. [ABSTRACT FROM AUTHOR]

• This paper presents a novel application of AHA to tune the controllers parameters. • The super twisting sliding mode controller is applied to microgrids. • The results of proposed model are compared with other recent models. • The proposed model is verified by comparing its results with the measured results. It is essential to improve optimal control for an autonomous microgrid operation. This paper presents a novel application of an artificial hummingbird algorithm for achieving optimal control of an autonomous microgrid. It also suggests a comparative analysis for the optimal super-twisting sliding mode control with both optimal fuzzy logic control and optimal proportional-integral control. The proposed super-twisting sliding mode controller parameters are adequately designed by using the AHA optimization method; meanwhile, the FLC and PI controllers are designed by using the African vultures and the Gorilla troops optimization algorithms, respectively. A MATLAB/Simulink environment is used to verify the effectiveness of the suggested microgrid control methods. The overall system detailed approaches for modeling, designing, and controlling are introduced. The investigation of the ST-SMC approach is also tested in a real-time setting. OPAL-RT 4510 rapid control prototyping and OPAL-RT 8660 data acquisition are used to implement the suggested controller. The verification results prove the superiority of the ST-SMC based on the AHA optimization technique compared with other optimal control techniques. [ABSTRACT FROM AUTHOR]

• A method for extending polarimetric optimization of PS-InSAR and SBAS techniques on dual polarized Sentinel-1 data is proposed. • The phase quality and density of coherent pixels in both techniques are improved with the proposed method. • The potentials of the proposed method for differential land covers are investigated. The rationale of combining polarimetric SAR data with multi-temporal InSAR techniques, which is based on polarimetric optimization, is to improve their performance in identifying coherent pixels and increasing the accuracy of deformation monitoring. This paper extended the polarimetric optimization of Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques on dual-polarized Sentinel-1 data. For this purpose, 20 dual-polarization (VV/VH) SAR images acquired by the Sentinel-1A sensor were used. The results indicated that by applying polarimetric optimization on dual-polarized data in the SBAS technique, the number of candidate and final coherent pixels increased 2.95 and 1.42 times, respectively. Similarly, the number of candidate and final coherent pixels (CP) increased 3.12 and 1.45 times in the PSI technique, respectively. Also, the effect of the polarimetric optimization on increasing the density of coherent points in different regions was investigated. In this framework, Sentinel-2 time-series images were classified into four classes (bare soil, vegetation, build-up, and others) using Google Earth Engine by applying remote sensing indices. This demonstrated that polarimetric optimization of the SBAS technique was more effective in bare soil and vegetated regions than in urban areas. However, polarimetric optimization of the PSI technique was more successful in non-urban regions than in urban areas. An external comparative analysis was also performed between the deformation results derived by PSI and SBAS techniques and GPS observation. The evaluation demonstrated that polarimetric optimization coupled with multi-temporal InSAR analysis yielded more reliable deformation results than conventional single-polarimetric data. [ABSTRACT FROM AUTHOR]

• Determining the urban critical infrastructures in the event of an earthquake. • Designing Location-Routing-Inventory decisions in humanitarian relief chain. • Simulation of interaction of urban infrastructure in earthquake conditions. • Using a cooperative game to examine the manner of cooperation of disaster relief supply chain players. • Application of the model to a real-world disaster relief logistic case study. This paper describes a new simulation based mathematical model for locating distribution centres, vehicle routing, and inventory problems under earthquake conditions. In this paper, the proposed network includes affected areas, suppliers, distribution centres, and hospitals. Additionally, the basic infrastructures of the city, which are very fragile at the time of the earthquake, are identified. Then, the demand of each relief commodity is calculated depending on the different earthquake scenarios using simulation. The estimated demand is incorporated into the mathematical model as an uncertain parameter. The proposed methodology is designed as a two-stage model so that in the first stage the location and inventory of distribution centres are addressed. Thereafter, in the second stage the routing decisions are taken for the distribution of relief commodities from the distribution centres and suppliers to the affected areas. Due to the NP-hardness of the second stage model, this model is solved using multi-objective stochastic fractal search. This algorithm is one of the population-based and stochastic optimization techniques and inspired by the natural phenomenon of fractal growth. It should be noted that in the second stage, a cooperative game theory of coalition type is considered, which resulted in synergies that minimize the relief golden time. The kind of cooperation is the use of potential of co-operators' vehicle. In this stage, the possibility for the players to cooperate by sharing people and commodities transportation requests is considered. Also, to validate the model, a real case study is provided for a possible earthquake in Tehran. Finally, the comparison between the simulation results and the values obtained from the real system evaluates the performance of the implemented model. Considering normality and a 95% confidence interval, it can be concluded that the proposed model provides a precise representation of the real system's performance. [ABSTRACT FROM AUTHOR]

• In-silico framework for the simultaneous optimal control and design of shape-morphing composite magnetic sheets. • The externally applied magnetic flux density and the remnant magnetisation of hard particles are simultaneously optimized. • The existence of optimal solutions for both the underlying state equation and the control problem is proved. • Explicit expressions of the continuous gradients are obtained by using the formal Lagrangian method. • An alternative to intuition based approaches to obtain complex magnetically induced configurations is provided. This paper presents a novel in-silico framework for the simultaneous optimal control and design of complex magnetic responsive polymer composite materials. State-of-the-art optimisation techniques are used in conjunction with the latest developments in the numerical solution of hard-magnetic soft materials undergoing large (potentially extreme) deformations, in order to address the challenging task of designing shape-morphing two-dimensional composite magnetic sheets. This paper introduces the following key novelties: (i) an optimisation-driven method for the simultaneous optimal control and design of the externally applied magnetic flux density as well as the remnant magnetisation of hard particles within the elastomer matrix, (ii) the well-posedness character of the optimisation problem is established by proving existence of solutions for both the underlying state equation and the control problem itself, (iii) a gradient-based optimisation algorithm is proposed for the numerical approximation of the problem, where explicit expressions of the continuous gradients are obtained by using the formal Lagrangian method. Furthermore, a series of numerical examples are presented in order to demonstrate the capability of the proposal as an alternative to intuition or experimentally-based approaches, representing an optimisation-driven method that facilitates the design of smart materials yielding complex magnetically induced shape morphing configurations. [ABSTRACT FROM AUTHOR]

• A robust method is proposed to alleviate the effect of traffic data with outliers. • A comprehensive traffic flow prediction indicator system is established in this paper. • We hybridize polynomial and Gaussian kernel to build nonlinear version of our method. • Parameters of the proposed method are optimized by Fruit Fly Optimization Algorithm. A novel least squares twin support vector regression method is proposed based on the robust L 1 -norm distance to alleviate the negative effect of traffic data with outliers. Although there is some known work for the short-term traffic flow prediction problems, their efficacy depends heavily on the collected traffic data, which are often affected by various external factors (e.g. weather, traffic jam or accident), leading to errors and missing data. This makes it difficult to pick an effective method that accurately predicts the traffic state. As a contribution of this paper, an iterative algorithm is designed to solve the non-smooth L 1 -norm terms of our method; its convergence also proved. Further, a comprehensive traffic flow indicator system based on speed, traffic flow, occupancy and ample degree is utilized in this paper. We also extend the proposed method to a nonlinear version by hybridizing the polynomial kernel and radial basis function kernel, where the weight coefficient of hybrid kernel is determined by the change tendency of traffic data. To promote the prediction performance, the parameters of our nonlinear method are optimized by adaptive fruit fly optimization algorithm. Extensive experiments on real traffic data are performed to evaluate our model. The results indicate that the newly constructed model yields better prediction performance and robustness than other models in various experimental settings. [ABSTRACT FROM AUTHOR]

• The visual difference between noise and image structure is described and used for denoising. • A dual-constrained Retinex decomposition model is proposed for low-light image enhancement and removing imaging noise. • A patch-aware low-rank prior is proposed for denoising, which can measure noise intensity for each image patch. For low-light image enhancement, Retinex-based models have been acknowledged as a representative technique, yet they frequently amplify noise hidden in dark image. There have been some attempts to noise reduction. Because they assume a uniform level of noise, the background seems blurry. In this paper, a dual constraint is developed to perform Retinex decomposition and de-noising. First, a patch-aware low-rank model (PALR) is proposed to reject noise. In contrast to the global noise intensity assumption, PALR can measure noise intensity for each image patch, and flexibly control the regularization extent to achieve a trade-off between noise removal and details preservation. Second, noise looks like image background texture instead of image structure. Relative total variation (RTV) is used to simulate the visual difference and works well in enlarging image gradients for highlighting details. By imposing PALR constraint on clean scene image, enforcing visual difference constraint on the illumination and reflectance, a dual-constrained Retinex decomposition model (DCRD) is proposed, which can remove noise during decomposition. DCRD can be solved by an alternating optimization algorithm. Experiments on commonly tested low-light image datasets demonstrate the competing performance of our proposed model in comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A combined probability prediction system based on quantile prediction is proposed. • The quantile prediction component based on deep learning is constructed. • An innovation improved multi-objective optimization algorithm is proposed. • Opposition learning and elite level strategy are introduced in optimization algorithm. Wind speed prediction accuracy is critical for grid connection safety and intelligent wind farm management. However, most wind speed prediction studies mainly focus on the deterministic prediction, and are rarely discussed in wind speed uncertain prediction. Therefore, this paper proposes a wind speed combined probability prediction system that integrates data denoising technology and creatively introduces the concept of quantile into the deep learning model to construct the wind speed quantile prediction component. To ensemble the prediction components effectively, a novel multi-objective marine predator combination strategy is developed that circumvents the limitations of the traditional multi-objective optimization algorithm. The experimental results based on two wind speed datasets show that the proposed system can improve wind speed prediction accuracy, build a more appropriate wind speed prediction interval, efficiently measure and minimize the uncertainty of the forecast process. [ABSTRACT FROM AUTHOR]

• An interval sequence prediction method for power generation is proposed. • The new matrixed grey model can be applied to different interval sequences. • Parameters are estimated by combining Particle swarm optimization algorithm and Ordinary Least Square method. • The new model has higher prediction accuracy compared with four competing models. • Intervals of different types of power generation in China are predicted. Development trends of power generation sequences have nonlinearity and uncertainty. The nonlinearity can be reflected by the nonlinear grey Bernoulli model. The uncertainty can be reflected by the interval sequence. A matrixed nonlinear grey Bernoulli model based on interval sequences is proposed in this paper. In order to make the model directly applicable to interval sequence, the traditional grey Bernoulli model is linearized by power function transformation, then the parameters are matrixized, and finally the recursive prediction formula is obtained by Cramer's law. Particle swarm optimization algorithm is used to estimate the nonlinear parameter. By adjusting the parameter, the new model can be used to predict exponential, saturated and parabolic interval sequences. The new model is used to predict the intervals of China's thermal power, hydro power, nuclear power, wind power, national power generation and power generation in east China. The prediction results show that the new model has higher prediction accuracy than the competing models. Finally, the power generation of four modes and national power generation from 2022 to 2025 are predicted and analyzed. [ABSTRACT FROM AUTHOR]

• A novel discrete multivariable grey prediction model considering the spatial proximity effect is proposed. • The complete spatial distance index is designed to quantify the intensity of the spatial proximity effect. • The spatial proximity effect term is constructed and incorporated into the grey prediction framework. • The proposed method extends the application field of the grey forecasting technology. • The novel forecasting method significantly outperforms a range of benchmark models. The temporal and spatial characteristics of the collected data typically play an essential role in time series forecasting. However, traditional grey models pay more attention to temporal information, meanwhile disregarding the presence of spatial features critical to producing accurate forecasts. Moreover, the grey theory has few attempts to overcome this problem. Given this situation, this paper proposes a novel multivariable grey prediction model considering the spatial proximity effect for time series forecasting. More specifically, the complete spatial distance index is defined initially based on the geographical and economic distance to quantify the intensity of the spatial proximity impact. On this basis, the spatial proximity effect term is constructed and incorporated into the conventional discrete multivariable grey model, establishing the novel model. Subsequently, the particle swarm optimization algorithm determines the optimal weight coefficient in the developed index. For illustration and verification purposes, the proposed model performs experiments predicting Jiangsu's economic outputs compared to five prevalent benchmarks. Furthermore, three statistical metrics, the Diebold-Mariano test, the Monte Carlo experiments, and the probability density analysis are employed to validate the model's efficacy and robustness. Empirical results demonstrate that the proposed method outperforms all five competitors with strong robustness and broad generalization. Therefore, this new model is used for forecasting Jiangsu's future economic outputs from 2021 to 2025. [ABSTRACT FROM AUTHOR]

Multi-view subspace clustering aims to discover the hidden subspace structures from multiple views for robust clustering, and has been attracting considerable attention in recent years. Despite significant progress, most of the previous multi-view subspace clustering algorithms are still faced with two limitations. First, they usually focus on the consistency (or commonness) of multiple views, yet often lack the ability to capture the cross-view inconsistencies in subspace representations. Second, many of them overlook the local structures of multiple views and cannot jointly leverage multiple local structures to enhance the subspace representation learning. To address these two limitations, in this paper, we propose a j ointly s moothed m ulti-view subspace c lustering (JSMC) approach. Specifically, we simultaneously incorporate the cross-view commonness and inconsistencies into the subspace representation learning. The view-consensus grouping effect is presented to jointly exploit the local structures of multiple views to regularize the view-commonness representation, which is further associated with the low-rank constraint via the nuclear norm to strengthen its cluster structure. Thus the cross-view commonness and inconsistencies, the view-consensus grouping effect, and the low-rank representation are seamlessly incorporated into a unified objective function, upon which an alternating optimization algorithm is performed to achieve a robust subspace representation for clustering. Experimental results on a variety of real-world multi-view datasets confirm the superiority of our approach. Code available:. • We jointly model multi-view commonness and inconsistencies for subspace learning. • We present the view-consensus grouping effect to explore multi-view local structures. • A jointly smoothed multi-view subspace clustering approach is proposed. • Experiments on a variety of multi-view datasets show the superiority of our approach. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) are not only used in terrestrial applications, but also in Low-Earth orbit satellites and in higher altitude missions. NASA's Magnetospheric Multiscale (MMS) mission has demonstrated the capabilities of existing GNSS systems to provide positioning, navigation, and timing (PNT) services in the Cis-lunar space. The resurgence in plans by national space agencies for Lunar exploration presents a need for accurate, precise, and reliable navigation systems to ensure the safety and success of future missions. Moreover, the increased amount of Moon missions over recent years, shows the requirement of navigation capabilities for Low Lunar orbiters, Moon landers, Moon rovers, and manned missions. The success of Global Navigation Satellite Systems (GNSS) on Earth, presents an opportunity for the study of a potential design requirements and expected performance of a Lunar GNSS constellation. We have approached this problem through the methodology of multi-objective optimization; numerically simulating the orbits, and using the Position Dilution of Precision (PDoP) as the figure of merit to optimize a set of 200 constellation designs and improving them gradually over 1864 generations. Over 12,000 unique constellation designs were generated with the best 10 constellations presented in this paper for consideration and further study. Compared to the literature, these 10 constellations achieved a 44% improvement in PDoP (2.73) using the same number of satellites in each constellation, and meeting the performance requirements of planned Lunar missions. • There is an increase of Moon missions over the recent years • These missions will require reliable navigation systems to ensure their success • Earth GNSS systems can provide position, navigation, and timing in the Cislunar space • Augmentation satellite systems will be required to improve PNT accuracy on the Moon • Multi-objective optimization has been used to optimize satellite constellation design • This methodology can also be used to optimize a Lunar GNSS constellation design [ABSTRACT FROM AUTHOR]

This paper considers an active distribution network (ADN) that can benefit from price-responsive demands, inverter-based photovoltaic (PV) generating units, and battery energy storage systems (BESSs) to participate in the real-time energy market. However, a challenging issue arises due to the coupling of energy offerings in the market and Volt-Var schedules of system devices. This paper develops an integrated framework of energy management system and Volt-Var optimization (EMS-VVO) in the ADN. This application is in bidirectional communication with the energy market and electricity assets. In the first stage and some minutes before each hour, the EMS-VVO unit makes the offering decisions in the energy market, as well as the schedules of demands and slow-acting devices (i.e., on-load tap changers (OLTCs) and capacitors) under uncertainties in the market prices, PV power production, and power loads. In the second stage, when the actual values of available PV productions and the updated market prices are known during the power delivery, this application controls the voltage in minutes by dispatching the fast-acting devices (i.e., PV generating units and BESSs) throughout the distribution network in minutes. A number of comparative out-of-sample simulations through a 69-node test system demonstrate how the offering and controlling decisions of the ADN can be modified under different PV penetration levels, voltage violation penalties, demand flexibility levels, and reactive power capabilities of fast-acting devices. • Scheduling model couples energy management system and Volt-Var optimization. • ADN profitably offers in real-time energy market through the scheduling model. • Voltage violation decreases by a higher penalty factor at the expense of lower profit. • Voltage violation is more crucial without reactive power support of fast-acting devices. • More flexible demands can result in a higher profit from offering in the energy market. [ABSTRACT FROM AUTHOR]

• By considering empty and laden containers, this paper investigates the LSHLPECR. • A MILP (mixed integer linear program) is presented for formulating the LSHLPECR. • In terms of the solution of our MILP, this paper further investigates the CLPP. • PCLPs at various ports are obtained by using inverse optimization. Considering the influence of economies of scale and ship capacity on empty container repositioning, using foldable containers and container leasing may be effective ways to save the operating costs of liner shipping companies. Considering the minimization of the transportation costs of empty and laden containers, this paper studies a liner shipping hub location problem with empty container repositioning (LSHLPECR) considering foldable containers. We propose a mixed integer linear program (MILP) for the formulation of the LSHLPECR. In terms of the optimal solution of our MILP, this paper further explores the container leasing pricing problem (CLPP), where our aim is to obtain perceived container leasing prices (PCLPs) utilizing the inverse optimization technique. Numerical experiments verify the effectiveness of the studied problems. [ABSTRACT FROM AUTHOR]

The fault management process is facilitated by equipping power distribution systems with automated devices, especially remote-controlled switches (RCSs). Although RCS plays a key role in improving the system's reliability, it imposes significant investment, installation, and maintenance costs. Hence, RCSs should be optimally located in distribution feeders for the highest profit. In previous works, reliability-oriented mathematical optimization models have been formulated to reach this goal. However, the number of test solutions exponentially grows with the problem size to find the globally optimal solution. This paper uses machine learning to propose a scalable and easy-to-implement model for optimal switch placement in real power distribution systems. At first, the features of candidate points for installing RCSs are introduced. Then, a learning model is applied to deeply explore the relationship between these features and optimal locations for installing RCSs. After training, the learning-based surrogate model directly determines the optimal RCS placement strategy in real power distribution systems by leveraging knowledge gained from past experiences. Simulation results demonstrate that the proposed surrogate model is approximately 29 times faster than the integer programming-based mathematical model, without a significant loss of accuracy, when implemented on a modified 11 kV network connected to Bus 4 of the Roy Billiton test system. This paper also employs explainable artificial intelligence (XAI) tools to select the most important features, where the Hamming loss is decreased by approximately 5%. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to the strategic planning of the supply chain for lime cultivation. The uncertainty associated with lime production due to factors such as transportation, crime, and water availability, among others, represents different challenges. To address these challenges, the authors propose the use of machine learning tools coupled with deterministic optimization to obtain optimal solutions for the supply chain for future years. This paper highlights the use of justice schemes to ensure an equitable distribution of lime production between farmers and learning machines to quantify the insecurity impact on lime production in Michoacan. The problem can be solved using a Mixed-Integer Linear Programming (MILP) model that focuses on maximizing economic benefits, minimizing the use of irrigation water in crops, and minimizing Carbon Dioxide Equivalent (CO 2eq) emissions within the supply chain. The results demonstrate that the proposed approach can effectively address the challenges facing the lime production supply chain, resulting in an optimized and equitable lime production and distribution between farmers using the Social Welfare Justice scheme. Scenario A presents a utopian viewpoint with maximum benefits and minimum CO 2eq generated emissions, corresponding to $ 4.08 × 109 and 1.94 × 106 tons of CO 2eq , respectively. This paper contributes to the growing body of literature on the use of Artificial Intelligence (AI) in supply chain management, specifically in the optimization of supply chain processes. [Display omitted] [ABSTRACT FROM AUTHOR]

In recent years, the maturation of modern multicore processor technology and its increasing adoption in critical industrial domains have posed significant challenges for real-time systems, primarily due to contention for shared cache resources and the resulting uncertainty. To address this issue, contemporary processors employ cache partitioning techniques, enhancing temporal predictability by isolating cache access among processor cores. However, this isolation technique may lead to real-time tasks missing their deadlines due to an insufficient number of cache partitions. Consequently, this paper investigates the schedulability of preemptive global Earliest Deadline First (EDF) real-time scheduling algorithms that support dynamic cache allocation. We propose an innovative LAG-based schedulability analysis method for these algorithms and present a utilization-based schedulability condition that reduces analysis time complexity while improving analysis accuracy. Building upon this foundation, and incorporating task characteristics into our analysis, this paper further introduces an optimization technique aimed at minimizing the pessimism inherent in the schedulability test. Lastly, the performance and efficiency of the proposed schedulability determination method are validated through simulation experiments with randomly generated tasks. [ABSTRACT FROM AUTHOR]

With the development of intelligent logistics technology, some companies began to use robots instead of humans to load cargo. This paper studies a novel container loading problem based on robotic loader system (CLP-RLS). Different from the existing robot-packable pattern in the literature, the robotic loader system in this paper consists of a depalletizing robot, an automatic telescopic roller line and a loading robot, in which the loading robot will enter the carriage along with the automatic telescopic roller line. In CLP-RLS, it is necessary to consider not only many practical constraints already in the literature, including load balancing, orientation, stability, and multi-drop but also two new constraints related to robotic loader system: pallet continuity constraint (the loading sequence of cargo on the same pallet is continuous) and robot position constraint (the robot can only load cargo incrementally from the front to the back of the truck). Due to the difficulty in modeling CLP-RLS and the large scale of the real-case instances, we present a tree search approach based on wall-building to solve CLP-RLS, intending to find a feasible loading scheme for the RLS to minimize the length required to load all cargo. The effectiveness of the proposed approach is verified through both real-case instances and numerical instances. • A novel container loading problem based on robotic loader system is studied. • A tree search approach based on wall-building is proposed to solve this problem. • The effectiveness of the proposed approach is verified through real-case instances. • The proposed approach can support the decision-making of the truck delivery route. [ABSTRACT FROM AUTHOR]

The number of space debris has rapidly increased in the recent times with the rising number of space missions. Thus, to ensure the safety of any operating satellite, collision avoidance maneuvers must be performed. It is also practically impossible to make the state measurements and dynamic models completely error-free. So, this study deals with fuel-optimal collision avoidance maneuvers while also incorporating the effects of uncertainty on them. In this paper, one formulation for convex optimization and another formulation for non-convex optimization have been presented and compared. Effects that the choice of the optimization algorithms and the number of discretization points used have on the optimal Δv as well as on the computational speed have also been discussed. • Convex and non-convex optimization for collision avoidance maneuvers are compared. • Choice of algorithms and number of discretization points affect the optimal result. • The more conservative convex formulation leads to slightly higher Δv requirement. • The non-convex formulation can implement more accurate state dynamics. • If suitable algorithms are used, non-convex formulation can be a better choice. [ABSTRACT FROM AUTHOR]

The detection of gravitational waves (GWs) from space is a hot topic in astronomy and space science, as the Space Gravitational Wave Observatory can detect low-frequency GWs from various sources. The long-term stability of the configuration of GW observatories is the key to the success of GWs detection, which challenges the configuration design and optimization. This paper innovatively proposes an semi-analytical double-layer iterative optimization algorithm (DLI) to solve the geocentric configuration optimization problem. First, an iterative method with gradient information of breathing angle, which is compensated by its standard deviation, is introduced as the inner iteration layer. Then, the outer iterative layer executes the initial value update process via the selection of the constructed candidates with smallest arm length variation rate, until the maximum breathing angle variation converges to the preset accuracy. The proposed method is applied to optimize the nominal configuration of TianQin mission. The results shows that DLI can reduce about 99% computation duration compared with the numerical optimization method. Furthermore, the influence of initial configuration elements on stability parameters are analyzed, providing better alternation for geocentric GWs detection missions. • A semi-analytical configuration optimization of the GWs observatory is proposed. The efficiency is improved more than 100 times. • The inner iteration is realized with the compensated breathing angles as the gradient. • The effects of orbital elements on the configuration performance are analyzed. [ABSTRACT FROM AUTHOR]

The method used and results found by the team "ASTL-NUAA" from NUAA for the problem of GTOC 11 are presented in the paper. The problem is mainly decomposed into three parts: multiple targets flyby sequence optimization for the Mother Ships, station-building database construction problem of asteroid transfer arcs, and station-building mission planning. Therefore, a three-step optimization framework is proposed to solve the problem. After analyzing the data of all the asteroids and selecting according to the orbital elements and mass of the asteroids, an asteroid candidate set RS1 is chosen as candidate targets for the Mother Ships. A two-level optimization algorithm based on a genetic principle is applied to solve the two-impulse asteroid flyby problem and rapidly determine the asteroid sequence of each Mother Ship. Subsequently, a method of filtering input data for the time-optimal rendezvous problem by the optimal flight time and the corresponding optimal relative phase is proposed to construct a station-building database with less computation burden. The optimal relative phase is obtained by solving free final-phase continuous low-thrust time-optimal problem. The station-building database is composed of the results of continuous low-thrust time-optimal rendezvous problems. At last, a branch-and-bound algorithm based on the greedy strategy is adopted to rapidly get the result by maximizing the minimal mass among the stations. The final score of our solution is 4315.6663, which is obtained after the competition. • The method used and results found by NUAA for the GTOC 11 is presented. • The flyby sequence of asteroids is obtained by the two-level optimization algorithm. • For less computation burden, a filter strategy is proposed to filter the input of time-optimal problem. • To assign asteroids, the branch-and-bound algorithm is applied. [ABSTRACT FROM AUTHOR]

A periodic close encounter orbit is an orbit that periodically surveys a target spacecraft. The design of such orbits is important for both military and civil missions. As an example, it can be used for a preliminary mission design to better plan the repair of damaged, expensive, strategic, or extremely critical space assets. Indeed, this paper proposes an optimization approach and algorithm to reach periodic close encounter orbits through n -impulse maneuvers within the framework of Keplerian motion. The optimization is constrained to several bound requirements, such as a) distance bounds at encounter, b) total Δ v tot cost, c) single impulse cost Δ v k , d) encounter illumination angle, e) daily observation time, f) minimum perigee radius, g) maximum repetition time, h) maximum eccentricity, and i) maximum encounter time. The optimization, performed using Genetic Algorithm, has been validated by several numerical examples using the TLEs of existing satellites. The simulations showed good results and the reliability of the proposed approach via the Genetic Algorithm has been validated by a comparison with the Particle Swarm Optimization algorithm. [ABSTRACT FROM AUTHOR]

The network-on-chip (NoC) is an intra-communication on the chip extended from system-on-chip (SoC). The NoC design suffers from high failure rates due to the problem of routing in the traffic conditioning. In this paper, a realistic traffic pattern is used to verify the proposed routing scheme of the NoC with the novel design topology. Here a topology named mixed-torus topology is designed with the combination of processing block and the non-processing block that has the information about the processing block, which are associated with its torus block. The designed topology is presented with the encoder application model, and the set of tasks are mapped and scheduled to the processing block by the spider monkey optimization algorithm. Then the routing of data through the non-processing block is done by the pathfinder based traffic and thermal aware adaptive routing protocol (PFTTAR), which will perform the path diversity phase and the path selection phase for better routing. The designed NoC is then treated with the statistical traffic pattern generation for the analytical study of the proposed work and is implemented in Xilinx. • Based on execution time the SMO map and schedule the task of each processing block on mixed-torus topology. • Concerning the deadlock and the throttling nodes the adaptive routing is carried out by PFTTAR protocol at path diversity phase. • The topology design on the proposed protocol is influenced by the statistical traffic pattern for the analysis of the proposed methodology. [ABSTRACT FROM AUTHOR]

• A novel nonlinear impulsive time-delay system in fed-batch process is proposed. • The robust parameter identification problem is presented to determine the optimal parameters. • A parallel particle swarm optimization algorithm is developed to solve the identification problem. In this paper, taking the feeding process as an impulsive form and considering the time-delay in fermentation process, we propose a nonlinear impulsive time-delay system to formulate the fed-batch culture in microbial 1,3-propanediol production. For this system, we discuss some important properties of the solution. To determine the optimal time-delay and kinetic parameters in the system, we then present a robust parameter identification model subject to continuous state inequality constraints. By the constraint transcription technique and a penalty method, we transform the robust parameter identification problem to a sequence of optimization problems with only box constraints. On this basis, we propose a parallel Particle Swarm Optimization algorithm to solve the resulting problem. Finally, the numerical results show that the impulsive time-delay system can well describe the fed-batch process and the developed algorithm is highly effective. [ABSTRACT FROM AUTHOR]

This paper aims to determine a time-varying dilution rate of the feed medium which maximizes mean productivity while minimizing both system sensitivity and control cost in glycerol continuous culture. A multi-objective optimal control problem subjected to a nonlinear control system and its auxiliary system is proposed and transformed into a finite-dimensional large-scale multi-objective optimization problem via discretization and time-scaling transformation. A multi-objective competitive swarm optimization algorithm is constructed to solve this transformed problem based on pairwise competition, mutation operation and environment selection. The Pareto front obtained shows the distribution of the optimal target value which indicates the rationality and feasibility of the control system under the approximate optimal strategy. Numerical simulations verify the robustness of the optimal control system and the effectiveness of our numerical solution method. The value of IGD shows the good accuracy and distribution of the proposed multi-objective optimization algorithm. • We propose a multi-objective optimal control problem of glycerol continuous culture. • The multi-objective optimal control problem is approximated by a parameter optimization problem. • A multi-objective competitive swarm optimization algorithm is developed. • Numerical experiments show the effectiveness and applicability of the approach proposed. [ABSTRACT FROM AUTHOR]

• A variable order fractional accumulation generating operator is proposed. • A variable order derivative is introduced into a grey model. • The effect of the new model is better than that of similar competitive models. • The new model can be used to predict problems with variable order characteristics. The use of constant order differential equations to describe the evolution of complex systems is often unable to describe some of the changing characteristics of the systems accurately. Variable order fractional derivatives provide us with new tools to solve such problems. In this paper, the accumulation and derivative orders of the classic grey model are expanded from constants to functions, and a variable order fractional grey model is established to describe the evolution process of complex systems. Firstly, this paper defines the variable order fractional accumulation generation sequence. On the basis of this sequence, a variable order fractional derivative grey model is established, the parameters of the model are estimated using the least square method, and the quantum particle swarm optimization algorithm is used to solve the order of fractional derivative and accumulation. Sadik transform and Laplace transform are adopted to obtain the analytical solution of the new model. Lastly, the effectiveness of the new model is verified through four cases. Compared with other models, the variable order fractional model can describe the development process of complex systems more accurately. [ABSTRACT FROM AUTHOR]

The development of fast and reliable optimization algorithms is required in order to obtain real-time optimal trajectory on-board spacecraft. In addition, the wide spread of small satellites, due to their low costs, is leading to a greater number of satellite formations in space. This paper presents an Improved version of the Magnetic Charged System Search (IMCSS) metaheuristic algorithm to compute time-suboptimal manoeuvres for satellite formation flying. The proposed algorithm exploits some strategies aimed at improving the convergence to the optimum, such as the chaotic local search and the boundary handling technique, and it is able to self-tune its internal parameters and coefficients. Moreover, the inverse dynamics technique and the differential flatness approach, through the B-splines curves, are used to approximate the trajectory. The optimization procedure is applied to the circular J2 relative model developed by Schweighart and Sedwick and to the elliptical relative motion model developed by Yamanaka and Ankersen. The results of this paper show that the convergence is better achieved by using the proposed tools, thus proving the efficiency and reliability of the algorithm in solving some space engineering problems. [ABSTRACT FROM AUTHOR]

• The studied problem in this paper is completely novel. • Generalized shifted Legendre polynomials (GSLP) are introduced. • GSLP is the novel and powerful computational tool for solving fractional optimal control problems. • We derive a new fractional operational matrix in the Caputo sense for the GSLP. • Applying the optimization technique to obtain the best possible approximate solution. This paper proposes an optimization method for solving a general form of nonlinear fractional optimal control problems (NFOCP) governed by nonlinear fractional dynamical systems and mixed conditions. A model for cancer treatment based on the NFOCP is developed, by including the population of immune cells, tumor cells, normal cells, fat cells, chemotherapeutic and immunotherapeutic drug concentrations. The aim behind the NFOCP is to find the optimal doses of chemotherapeutic and immunotherapeutic drugs which minimize the difference between the numbers of tumor and normal cells. The proposed approach includes inserting weight constants for the tumor and normal cells in the cost functional so that the normal cell population is large minimizing, therefore, the tumor burden. The method approximates the solutions of the NFOCP using the generalized shifted Legendre polynomials (GSLP). Operational matrices of integer and fractional derivatives are derived for the GSLP. The Gaussian quadrature rule and the Lagrange multipliers method reduce the NFOCP to a system of algebraic equations. Additionally, the error analysis of the algorithm is considered. Numerical examples demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

A parametric design approach to the petal-type solid surface deployable reflector is presented, in which the generic parametric model of the reflector is established and its division parameters are optimized to simplify the structure, increase deployment reliability and improve electrical performance. For this goal, the estimated expression of the division number of petals is first derived as the initial value of subsequent optimization algorithm. Then the estimation-trial algorithm is developed, its corresponding three-level nested optimization model is established, and the reference circle model for improving the efficiency in solving the aforementioned model is proposed. Furthermore, the deployment scheme of this kind of reflectors is implemented by Euler's rotation theorem combined with rigid-body registration. In addition, the parameter analysis is made to determine which parameters dominate the division number of petals. Finally, the effectiveness of this method in configuration, deployment and optimality together with the improvement in deployment reliability and electrical performance are verified. It is revealed that reflectors designed with this method need fewer petals compared with the existing reflectors of this class, deploy smoothly, have lower deployment failure probability, as well as have higher antenna gains and lower relative sidelobe levels. The results indicate the petal-type solid surface deployable reflector characterized by compact structure, high reliability and good electrical performance can be obtained with the approach proposed in this paper. [Display omitted] • A parametric model for describing the configuration, size, shape, and petal pose. • A novel algorithm and its efficient optimization model for minimizing petal number. • A deployment scheme realized by Euler's theorem coupled with rigid-body registration. • The reflector is more compact, reliable, and has better electrical performance. [ABSTRACT FROM AUTHOR]