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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]

Abstract: In this paper, we address some computational challenges arising in complex simulation-based design optimization problems. High computational cost, black-box formulation and stochasticity are some of the challenges related to optimization of design problems involving the simulation of complex mathematical models. Solving becomes even more challenging in case of multiple conflicting objectives that must be optimized simultaneously. In such cases, application of multiobjective optimization methods is necessary in order to gain an understanding of which design offers the best possible trade-off. We apply a three-stage solution process to meet the challenges mentioned above. As our case study, we consider the integrated design and control problem in paper mill design where the aim is to decrease the investment cost and enhance the quality of paper on the design level and, at the same time, guarantee the smooth performance of the production system on the operational level. In the first stage of the three-stage solution process, a set of solutions involving different trade-offs is generated with a method suited for computationally expensive multiobjective optimization problems using parallel computing. Then, based on the generated solutions an approximation method is applied to create a computationally inexpensive surrogate problem for the design problem and the surrogate problem is solved in the second stage with an interactive multiobjective optimization method. This stage involves a decision maker and her/his preferences to find the most preferred solution to the surrogate problem. In the third stage, the solution best corresponding that of stage two is found for the original problem. [Copyright &y& Elsevier]

Abstract: The systematic, transparent and accurate on-line energy information of the production process is quite the basis of mill energy management and conservation. An Energy Management System (EMS) was built according to the characteristics of the energy usage in a typical newsprint paper mill. The EMS first realized the process data acquisition and integration, second fulfilled the on-line energy information calculation from the integrated data. A water and steam properties query component based on IAPWS-IF97 was developed and plugged into the EMS to facilitate the energy calculation of water and steam. The practical performance in the paper mill showed that the integrated energy information of the whole mill was gained and the on-line energy supervisory was achieved. In the end a systematic, transparent and accurate energy usage profile is obtained and it would provide the valuable fundamental energy data for the further energy analysis and optimization in the EMS. [Copyright &y& Elsevier]

• 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]

With the rapidly evolving geo-political landscape and unceasing advancements in technology, sustainable energy security is a very important topic. Research indicates that electrochemical energy systems are quite promising to solve many of energy conversion, storage, and conservation challenges while offering high efficiencies and low pollution. The paper provides an overview of electrochemical energy devices and the various optimization techniques used to evaluate them. The optimization techniques include linear regression, factorial design, the Taguchi method, artificial neural networks, filters, and a combination of such methods to improve these systems. To support the growing interest in research, the bulk of this study focuses on a review of the most promising and highly researched electrochemical energy devices, such as fuel cells, batteries, and supercapacitors. The paper also provides modest commentary on hydrogen production technologies, electrochemical reactors, and membrane separation technologies, amongst other technologies. Building on a previous paper by the authors of this paper on artificial intelligence in hybrid renewable energy systems with fuel cells, this work provides a comparative review of optimization techniques for supercapacitors by highlighting key findings based on model accuracy. A summary of the advantages and disadvantages of the different major optimization techniques is presented. The paper concludes that a combination of optimization techniques is used to overcome the drawbacks of individual techniques, with adaptive filters being the most widely studied. This paper presents studies on the Design of Experiments (DoE) with the goal of building a better understanding of the relationships that exist between different operating variables in various electrochemical devices. • The electrochemical systems can be optimized for better performance with high accuracy. • The optimization techniques used are either model-based or data-driven. • Linear regression and artificial neural networks are the most common techniques studied. • Hybrid models could be applied to enhance systems' accuracy and minimize errors. [ABSTRACT FROM AUTHOR]

Abstract: The paper industry is, with about 6% of the total worldwide industrial energy use, an energy-intensive industry. The drying section is with approximately 50% the largest energy consumer in a paper mill, energy use in this section is mainly heat use. Several options to decrease heat use in conventional multi-cylinder drying sections are investigated, calculating the effect on energy use. Optimization measures include a) decreasing the amount of water evaporation by applying additives in higher consistencies and by lowering the water viscosity, b) decreasing the heat use of water evaporation by increasing the dew point temperature of the dryer and c) increasing the amount of heat recovery by using exhaust air to not only pre-heat the incoming air but also to increase process water temperatures. These could all be achieved by retrofitting and/or choosing different processing conditions in existing factories. The combined thermal heat saving potential due to the optimization actions is 1.3GJh/t paper (or 32% of the drying section’s heat use) as compared to the reference situation. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: The aim of this study was to validate an ultramicroassay with a reduced interference of hemoglobin for the enzymatic diagnosis of mucopolysaccharidosis I in dried blood spots on filter paper. Design and methods: A matrix of dried blood was incorporated within the calibration system. In addition, trichloroacetic acid was added to precipitate hemoglobin. Linearity, precision, accuracy and limits of detection and quantification were determined and α-l-iduronidase activity was obtained from 6 patients, 9 heterozygotes, 25 healthy adults and 500 neonates. Results: The ultramicroassay was linear, precise (coefficients of variation less than 10%) and accurate (recovery between 91 and 98%). The interference of hemoglobin was decreased within the hematocrit range of clinical interest: 35–55%. Conclusions: This ultramicroassay increases in 2.5 times the difference between healthy individuals and patients with respect to the reference assay; optimizing enzymatic quantification and confirmatory biochemical diagnosis for mucopolysaccharidosis I. [Copyright &y& Elsevier]

Abstract: Biological treatment of pulp and paper industry wastewater is made effective by giving a simple electrochemical pre-treatment. Biodegradability index is found to be improved from 0.11 to 0.46 by a short (6.9min) treatment in mild conditions (current density: 112.9Am−2, pH 7.3). The optimal operating point for maximum biodegradability has been found out using response surface methodology (RSM). It is also noticed that there exists an optimal operating point for maximum biodegradability. The specific energy consumption and current efficiency figures at the optimal condition are also promising. Moreover there is considerable reduction in the pollutant load (COD removal: 55% and color removal: 87%). Performance of the process at this operating point has been validated by running biological treatment. Considerable reduction in the requirement of biological reactor volume can be expected by giving such a simple electrochemical pre-treatment. [Copyright &y& Elsevier]

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]

Highlights: [•] A 23-fold enhancement in xylanase production was attained after statistical optimization. [•] The xylanase produced in SSF has higher thermostability than that produced in SmF. [•] The enzyme finds application in deinking of waste paper. [•] The time for deinking was reduced significantly at alkaline pH and elevated temperature. [Copyright &y& Elsevier]

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 paper entitled “Experimental investigation of machinability characteristics and multiresponse optimization of end milling in aluminium composites using response surface methodology (RSM) based grey relational analysis (GRA)”. The Authors adopted RSM based grey relational method to achieve high productivity in terms of maximum material removal rate MRR and least surface roughness, tool wear and cutting forces. Experimental design was made using RSM (central composite design) and responses were measured according to standard methods. In this study, authors had proposed the optimized Grey relational grade (GRG) ranking for machinability characteristics. However, there are some errors in their analysis, which are likely to affect the significance of their proposed ranking. So, this comment highlights the miscalculations and gives the corrected results. [ABSTRACT FROM AUTHOR]

Abstract: A detailed mathematical process integration model of a pulp and paper mill in the Northern Sweden has been developed. The main objective of this work has been set to describe the practical development of the model with particular emphasis on the development of the digester and evaporation plant sub-models. Actual plant measurements have been used to validate the model. By implementing the sub-models into the complete plant model, the influence of different operation parameters on the overall plant performance has been investigated. Furthermore, introductory studies with the main objective to minimize the plant energy cost have been carried out. [Copyright &y& Elsevier]

• An efficient mid-range approximation method for large-scale problems is proposed. • The improved trust-region strategy has a flexible and controllable performance. • A metamodel assembly technique is developed to alleviate the efficiency issues. • Optimization problems of 512 and 232 design variables are considered respectively. • The method successfully obtains the optimum at a reasonable computational cost. This paper presents an efficient solution for high-fidelity large-scale aerodynamic shape optimization problems based on several developments in the mid-range approximation method within a trust region optimization framework. The mid-range approximation method is an iterative optimization technique that utilizes mid-range approximations to replace the physical experiments or simulations during the optimization based on the selected trust region strategy. It could transform the original optimization problem into a sequence of approximate sub-optimization problems. In this work, an improved trust region strategy is proposed to contain more optimization states with a flexible and controllable performance to suit different types of problems. A metamodel assembly technique and its gradient-enhanced version are developed to further relax the requirements of computational costs in the mid-range approximation method. Its performance is discussed through a detailed comparison of metamodel performance using a mathematical benchmark case named Vanderplaats scalable beam. The wing only of the Common Research Model is offered to the proposed method for aerodynamic shape optimization. The optimization has 1 design objective, 232 design variables, and 135 design constraints. With all constraints satisfied, the optimized configuration has a 4.85 % improvement in wing drag performance. The shock region is greatly reduced and the wing pressure distribution is smooth and nearly parallel. These results show that the proposed method could achieve the design goal successfully within a reasonable computational cost for large-scale problems. [ABSTRACT FROM AUTHOR]

The increased application of Information and Communications Technology in automation and digitization is the driving concept towards the actualization of the Industrial Revolution 4.0. Many aspects of industrial design and operations including offshore engineering have been automated. However, the selection procedure of mooring design parameters including azimuth angle, pretension, diameter, fairlead slope, and mooring radius, which is a critical aspect of mooring system design has remained heavily based on a manual approach despite the availability of sophisticated hydrodynamic analysis software. Up to date, there is no proper review on optimisation techniques that have the potential of automating these procedures. In the past, some of the few available mooring optimisation procedures consider only the mooring lines for the prediction of optimal platform offset without due consideration to the integrity of the risers. Thus, the significance of adopting an integrated riser-mooring design methodology in the optimisation of mooring design variables is discussed in this paper where the integrity of the riser is adequately represented using a Safe Operation (SAFOP) zone polar diagram. We also review the developmental transition from the use of classical to Evolutionary Algorithms (EA) in the optimisation procedure. EA has been acknowledged as a practical alternative for solving mooring optimisation problems. Hence, some of the EA techniques are presented and their computational capabilities explored. In the last part, a concise review of the application of optimisation techniques for mooring optimisation of the different offshore floating platforms is presented based on their capabilities in minimizing platform offset and efficiency in terms of simulation time. So far, EA techniques like the Differential Evolution (DE), variants of Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO) have been used to optimize mooring line design variables. Based on the discussions, the potentials of DE, RegPSO and other variants of PSO can be further explored for better efficiency. • Previous studies addressing differential evolution (DE), genetic algorithm (GE) and particle swarm optimisation (PSO) techniques used for optimisation of mooring parameters have been critically reviewed. • The integrated riser -mooring design methodology is effective in accounting for integrity of riser and mooring systems. • The application of evolutionary algorithms, particularly the variants and hybrids of DE ,PSO and GA, have proven to be efficient optimisation of mooring systems. • Some variants of PSO like the RegPSO have demonstrated improved capability in terms of escaping premature convergence , thereby resulting in better computational efficiency. [ABSTRACT FROM AUTHOR]

Abstract: We show that the main results in the recent paper [4] by Sadiq Basha (2012) are not real generalizations but particular cases of existing fixed point theorems in the literature. [Copyright &y& Elsevier]

Oilfield wastewater with high moisture content can be efficiently treated by supercritical water gasification (SCWG) technology to produce hydrogen. This paper conducted a thermodynamic and environmental analysis of the oilfield wastewater SCWG system to provide optimization strategies. The waste heat utilization technology can enhance the heat integration of the system to reduce the exergy destruction and increase the H 2 yield. Sensitivity analysis showed that a ratio of preheating water to emulsion 3 can promote water-sensitive reactions to produce H 2 and save the H 2 needed to maintain the energetically self-sufficient system. Low system pressures, high feedstock preheating temperatures, and low oxidation reactor temperatures can reduce system energy destruction to obtain high H 2 yield and system efficiency. Meantime, the developed SCWG system has the potential to achieve natural enrichment of greenhouse gases after the H 2 separation has been completed. The use of waste heat utilization technology and carbon capture and storage (CCS) technology is expected to increase system efficiency to 53.43 % and reduce global warming potential (GWP) of 5.52 kg CO 2 -eq/kg H 2. This work would be of great value to the process design and optimization for the SCWG system of oilfield wastewater. [Display omitted] • The process of supercritical water gasification of oilfield wastewater was simulated. • Optimization strategies were given by thermodynamic and environmental analysis. • Operational details were revealed by comprehensive sensitivity analysis. • The exergy efficiency of 53.43 % were obtained. • Global Warming Potential decreased to 5.52 kg CO 2 -eq/kg H 2 with CCS. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy neural network predictive control scheme for studying the coagulation process of wastewater treatment in a paper mill. An adaptive fuzzy neural network is employed to model the nonlinear relationships between the removal rate of pollutants and the chemical dosages, in order to adapt the system to a variety of operating conditions and acquire a more flexible learning ability. The system includes a fuzzy neural network emulator of the reaction process, a fuzzy neural network controller, and an optimization procedure based on a performance function that is used to identify desired control inputs. The gradient descent algorithm method is used to realize the optimization procedure. The results indicate that reasonable forecasting and control performances have been achieved through the developed system. [Copyright &y& Elsevier]

Abstract: In this paper, we focus on the proposed algorithms to solve a linear programming problem with the convex combination of the max–min and the max–average composition and the max–star composition, respectively. They have been proposed by Ghodousian and Khorram [A. Ghodousian, E. Khorram, Solving a linear programming problem with the convex combination of the max–min and the max–average fuzzy relation equations, Appl. Math. Comput. 180 (2006) 411–418] and Khorram et al. [E. Khorram, A. Ghodousian, A. Abbasi Molai, Solving linear optimization problems with max–star composition equation constraints, Appl. Math. Comput. 179 (2006) 654–661], respectively. Firstly, we show that the “Tabular method algorithm” in the first paper and the “First procedure” in the second paper may not lead to the optimal solutions of the two models in some cases. Secondly, we generalize the proposed algorithm by Abbasi Molai and Khorram [A. Abbasi Molai, E. Khorram, A modified algorithm for solving the proposed models by Ghodousian and Khorram and Khorram and Ghodousian, Appl. Math. Comput. 190 (2007) 1161–1167] to solve the two models. In fact, it modifies the presented algorithms in the two papers. Finally, some numerical examples are given to illustrate the purposes. [Copyright &y& Elsevier]

Abstract: In this paper, we determine the modularities of asymptotically optimal towers found by Maharaj and Wulftange in 2005. This is also a continuation of the work of Elkies in 2002. [Copyright &y& Elsevier]

Abstract: A generalization of two sharp inequalities in a recent paper by N. Ujević is established. Applications in numerical integration are also given and the results of N. Ujević are revised and improved. [Copyright &y& Elsevier]

Abstract: In this paper a maintenance policy is optimised for a two-unit system with a parallel structure and stochastic dependences. Monitoring problems are taken into account in the optimisation scheme: the failure time of each unit can be not detected with a given probability. Conditions on the system parameters (unit failure rates) and on the non-detection probabilities must be verified to make the optimisation scheme valid. These conditions are clearly identified. Numerical experiments allow to show the relevance of taking into account monitoring problems in the maintenance model. [Copyright &y& Elsevier]

• Variational Quantum Circuits (VQCs) play a key role in several applications. • VQCs are parameterized quantum circuits trained by using classical optimizers. • The paper proposes to apply memetic algorithms to train VQCs. • The designed memetic algorithm outperforms the state-of-the-art classical optimizers. Among the ready-to-implement quantum algorithms, Variational Quantum Circuits (VQCs) play a key role in several applications, including machine learning. Their strength lies in the use of a parameterized quantum circuit that is trained by means of an optimization algorithm run on a classical computer. In such a scenario, there is a strong need to design appropriate classical optimization schemes that deal efficiently with VQCs and pave the way for quantum advantage in machine learning. Among possible optimization schemes, those based on evolutionary computation are finding increasing interest, given the unconventional and nonanalytical nature of the problem to be solved. This paper proposes to apply memetic algorithms to train VQCs used as quantum classifiers and shows the benefits of exploiting this evolutionary optimization technique through a comparative experimental session. [ABSTRACT FROM AUTHOR]

• A novel global optimization algorithm, rime optimization algorithm (RIME), is proposed. • RIME simulates the growth and crossover behavior of the rime-particle population. • The performance of RIME is adequately discussed based on benchmark functions. • RIME has better convergence accuracy and speed compared to other methods. • RIME is applied to solving five practical engineering optimization problems. This paper proposes an efficient optimization algorithm based on the physical phenomenon of rime-ice, called the RIME. The RIME algorithm implements the exploration and exploitation behaviors in the optimization methods by simulating the soft-rime and hard-rime growth process of rime-ice and constructing a soft-rime search strategy and a hard-rime puncture mechanism. Meanwhile, the greedy selection mechanism in the algorithm is improved, and the population is updated in the stage of selecting the optimal solution to enhance the exploitation capability of the RIME. In the experimental, this paper conducts qualitative analysis experiments on the RIME to clarify the characteristics of the algorithm in the process of finding the optimal solution. The performance of RIME is then tested on a total of 42 functions in the classic IEEE CEC2017 and the latest IEEE CEC2022 test sets. The proposed algorithm is compared with 10 well-established algorithms and 10 latest improved algorithms to verify its performance advantage. In addition, this paper designs experiments for the parametric analysis of RIME to discuss the potential of the algorithm in running different parameters and handling different problems. Finally, this paper applies RIME to five practical engineering problems to verify its effectiveness and superiority in real-world problems. The statistical and comparison results show that the RIME is a strong and competitive algorithm. The source code of the RIME 1 1 Source codes of RIME algorithm are also available at: https://codeocean.com/capsule/4472937/tree and https://www.mathworks.com/matlabcentral/fileexchange/124610-rime-a-physics-based-optimization and https://github.com/aliasgharheidaricom/RIME-A-physics-based-optimization. algorithm and associated files are publicly accessible at https://aliasgharheidari.com/RIME.html. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this note is to show that the optimization algorithm proposed in may not lead to the optimal solution in some cases. In fact, the optimization problem remains open if we do not apply the branch-and-bound method to solve it or compute all the objective values of minimal solutions of the feasible domain. [Copyright &y& Elsevier]

Abstract: Plating bath concentrations of Pt(IV) and Ir(III) have been optimized as well as the total catalytic loading of bimetallic Pt–Ir alloy for the electro-oxidation of ammonia in alkaline media at standard conditions. This was accomplished using cyclic voltammetry, scanning electron microscopy (SEM), energy dispersive X-ray (EDX), and statistical optimization tools. Concentrations of Pt(IV) and Ir(III) of the plating bath strongly influence electrode surface atomic compositions of the Pt–Ir alloy directly affecting the electro-oxidation behavior of ammonia. Several anode materials were studied using cyclic voltammetry, which demonstrated that Pt–Ir was the most active catalyst for the electro-oxidation of ammonia. Criteria for optimization were minimizing the climatic oxidation overpotential for ammonia and maximizing the exchange current density. Optimized bath composition was found to be 8.844±0.001gL−1 Pt(IV) and 4.112±0.001gL−1 Ir(III) based on electrochemical techniques. Physical characterization of the electrodes by SEM indicates that the plating bath concentrations of Pt and Ir influence the growth and deposition behavior of the alloy. [Copyright &y& Elsevier]

Abstract: A counterexample is provided in this short note to show that some of local convergence theorems established in [D.K.R. Babajee, M.Z. Dauhoo, An analysis of the properties of the variants of Newton’s method with third order convergence, Appl. Math. Comput. 183 (2006) 659–684] are not always true. Some mistakes in the proofs of these theorems are pointed out. [Copyright &y& Elsevier]

• 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]

Space-filling designs are useful for exploring the relationship between the response and factors, especially when the true model is unknown. The wrap-around L 2 -discrepancy is an important measure of the uniformity, and has often been used as a type of space-filling criterion. However, most obtained designs are generated through stochastic optimization algorithms, and cannot achieve the lower bound of the discrepancies and are only nearly uniform. Then deterministic construction methods for uniform designs are desired. This paper constructs uniform designs under the wrap-around L 2 -discrepancy by generator matrices of linear codes. Several requirements on the generator matrices, such as a necessary and sufficient condition for generating uniform designs, are derived. Based on these, two simple deterministic constructions for uniform designs are given. Some examples illustrate the effectiveness of them. Moreover, the resulting designs can be regarded as a generalization of good lattice point sets, and also enjoy good orthogonality. • The deterministic construction methods for uniform designs under the wrap-around L 2 -discrepancy are given. • A necessary and sufficient condition for generating uniform designs are derived. • The resulting designs can be regarded as a generalization of good lattice point sets, and also enjoy good orthogonality. [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]

Hydropower plants play an integral role in energy supply and are one of the main renewable energy sources. The efficiency of hydropower reservoir systems is controlled by operating rules that need to be optimized to maximize energy production. Here, a novel optimization algorithm, influential flower pollination algorithm (IFPA), is developed in this paper. IFPA has three enhancements: an efficient exploration mechanism based on an adaptive coefficient, a new exploitation mechanism around the best solution, and a rank-based strategy to smoothly transition from exploration to exploitation. IFPA is tested in two benchmark multi-reservoir systems and a real-world five-reservoir hydropower system in Iran. The results show IFPA outperforms other advanced methods in terms of convergence speed and preciseness to achieve the global solution. The tested algorithms include adaptive guided differential evolution (AGDE), composite DE (CODE), hybridizing sum-local search optimizer (HSLSO), improved teaching learning-based optimization (ITLBO), self-adapting control parameters in DE (jDE), self-adaptive TLBO (SATLBO), and a multi-strategy hybrid of DE and particle swarm optimization (MS-DEPSO). IFPA also achieved better total power production in the real-world case study compared to other algorithms. Overall, IFPA can provide better solutions with less power deficit and shows as a promising method for optimizing hydropower reservoir problems. [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]

This paper considers the completion problem of a partially observed high-order streaming data, which is cast as an online low-rank tensor completion problem. Though the online low-rank tensor completion problem has drawn lots of attention in recent years, most of them are designed based on the traditional decomposition method, such as CP and Tucker. Inspired by the advantages of Tensor Ring decomposition over the traditional decompositions in expressing high-order data and its superiority in missing values estimation, this paper proposes two online subspace learning and imputation methods based on Tensor Ring decomposition. Specifically, we first propose an online Tensor Ring subspace learning and imputation model by formulating an exponentially weighted least squares with Frobenium norm regularization of TR-cores. Then, two commonly used optimization algorithms, i.e. alternating recursive least squares and stochastic-gradient algorithms, are developed to solve the proposed model. Numerical experiments show that the proposed methods are more effective to exploit the time-varying subspace in comparison with the conventional Tensor Ring completion methods. Besides, the proposed methods are demonstrated to be superior to obtain better results than state-of-the-art online methods in streaming data completion under varying missing ratios and noise. [ABSTRACT FROM AUTHOR]

Accurate and reliable wind speed forecasting is of great significance to the management and utilization of wind energy. An improved deep learning model for wind speed forecasting, abbreviated as MODWT-RF-IGWO-LSTM, is presented in this paper. Firstly, the maximum overlap discrete wavelet transform (MODWT) is applied to denoise the original wind speed series. Secondly, the random forest (RF) algorithm is used for feature selection. Thirdly, the improved grey wolf optimization algorithm (IGWO) is applied to optimize the parameters of the long short-term memory (LSTM) model. Finally, the denoised wind speed data is entered into the well-trained LSTM model to obtain the final wind speed forecasting result. The performance of the proposed model is assessed by actual wind speed data for three different months of the year. The experimental results show that the proposed deep learning model for wind speed forecasting has good predictive ability. And the proposed model performs better than other benchmark models in this paper. [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]

This review provides a deep analysis of the different methodologies to improve the operation of solar thermal plants based on mathematical optimization. The various schemes found in the literature to determine the optimal operational strategy are classified depending on two criteria: time dependence (static or dynamic) and with feedback or not from the plant (real-time or offline). This review shows that offline dynamic optimization is performed on solar thermal plants in research papers but highlights the lack of real-time optimization studies. The analysis work conducted in this review, based on studies of the operation of solar systems but also on process engineering research articles, shows that dynamic real-time optimization seems capable of handling the intermittency of the solar radiation and well suited to improve the operation of a solar thermal plant. Indeed, the daily and seasonal variations of weather and heat demand associated with the uncertainty of their forecasts make the operation of such systems very challenging. This paper details the different ways to implement Dynamic Real-Time Optimization, and the possible improvements to the classical scheme. Perspectives on the application of Dynamic Real-Time Optimization in association with a planning phase to plan a smart use of storage are described. Although it has not been studied in depth in the literature, the Dynamic Real-Time Optimization of a solar thermal plant including storage should be investigated in order to maximize the benefits from the heat sold, extend the time period where the heat demand is met and reduce the consumption of back up fossil fuels. • A literature review of control and optimization of solar thermal plants is provided. • There are challenges associated with weather and demand variations and forecasts. • Dynamic Real-Time Optimization schemes are presented with examples. • Dynamic Real-Time Optimization is well-suited to optimize the solar plant operation. • A planning phase should be added for a better storage management. [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]

[Display omitted] • A comprehensive framework for analysing embodied carbon in TBM tunnelling. • Evaluate module-based carbon outputs with functional units via BIM-driven platform. • Baseline surveys for segmental lining designs and carbon reduction strategies. • Establish correlations between geotechnical parameters and TBM carbon emissions. • Identify limitations of current assessment methods suggesting a holistic framework. Alongside the transition towards sustainable and renewable infrastructure and energy production, tunnelling projects that adopt tunnel boring machines (TBM) continue to increase in size and complexity. Life Cycle Assessment (LCA) methodologies have a significant impact on the Architecture, Engineering and Construction (AEC) industry, with a shift in focus to cost and carbon management across the infrastructure lifecycle through the use of digitalisation techniques and sustainability rating systems. This paper aims to develop a systematic framework to enhance the sustainability of underground infrastructure in the early design and construction stages, aiming for "build clever and efficiently". This framework integrates digitalisation, carbon assessment standards, numerical modelling, and optimisation techniques to assess carbon emissions and establish benchmarks for construction products and processes. The approach uses parametric models to define tunnelling-related entities such as geological formations, TBM, and segments, designed with dimensional constraints that allow for adaptive modelling and interactions. Enabled by building information modelling (BIM) through parametric models enriched with domain properties, module-based carbon accounting is adopted to evaluate the footprint of material- and asset-level key emitters with functional units. Leveraging established databases, baselines for segmental lining designs and carbon reduction strategies are established. Through project-driven case studies, the correlations among geotechnical conditions, tunnel dimensions, and TBM operating parameters are developed to quantify TBM carbon emissions. A prototype programme demonstrates the framework's application on typical metro and road tunnels at different levels of details (LoD). This paper serves as a guideline for conducting embodied carbon assessments specifically tailored to TBM tunnelling, acknowledging the substantial contribution of the TBM construction process, which is controlled by geotechnical conditions. [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]

Underwater images serve as crucial mediums for conveying marine information. Nevertheless, due to the inherent complexity of the underwater environment, underwater images often suffer from various quality degradation phenomena such as color deviation, low contrast, and non-uniform illumination. These degraded underwater images fail to meet the requirements of underwater computer vision applications. Consequently, effective quality optimization of underwater images is of paramount research and analytical value. Based on whether they rely on underwater physical imaging models, underwater image quality optimization techniques can be categorized into underwater image enhancement and underwater image restoration methods. This paper provides a comprehensive review of underwater image enhancement and restoration algorithms, accompanied by a brief introduction to underwater imaging model. Then, we systematically analyze publicly available underwater image datasets and commonly-used quality assessment methodologies. Furthermore, extensive experimental comparisons are carried out to assess the performance of underwater image optimization algorithms and their practical impact on high-level vision tasks. Finally, the challenges and future development trends in this field are discussed. We hope that the efforts made in this paper will provide valuable references for future research and contribute to the innovative advancement of underwater image optimization. • A comprehensive review of underwater image enhancement and restoration methods is provided. • The publicly available underwater image datasets and underwater image quality metrics are introduced. • The extensive experiments on underwater image enhancement and restoration methods are conducted. • The challenges in the field are discussed and the promising insights for future directions are provided. [ABSTRACT FROM AUTHOR]