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This article develops two metaheuristics optimization techniques, Grey Wolf Optimizer (GWO) and Harris Hawks Optimization (HHO), to handle multi-objective optimal power flow (MOOPF) issues. Multi Objective GWO (MOGWO) and Multi Objective HHO (MOHHO) are the names of the developed techniques. By combining these optimization techniques with Pareto techniques, the non-dominated solution set can be obtained. These developed approaches are characterized by simplicity and have few control parameters. Fuel cost, emissions, real power losses, and voltage deviation were the four objective functions considered. The theories used to determine the best compromise solution and organize the Pareto front options are the fuzzy membership equation and the crowding distance approach, respectively. To validate and evaluate the performance of the presented techniques, two standard IEEE bus systems—30-bus and 57-bus power systems—were proposed. Bi, Tri, and Quad objective functions with 21 case studies are the types of objective functions and the scenarios that were applied in this paper. As compared to the results of the most recent optimization techniques documented in the literature, the comparative analysis results for the proposed methodologies demonstrated the superiority and robustness of MOGWO and MOHHO. [ABSTRACT FROM AUTHOR]

Unmanned engineering systems that execute various operations are becoming increasingly complex relying on a large number of components and their interactions. The reliability, maintainability, and performance optimization of these systems are critical due to their intricate nature and inaccessibility during operations. This paper introduces a new reliability-based optimization framework for planning operational profiles for unmanned systems. The proposed method employs deep learning techniques for subsystem health monitoring, dynamic Bayesian networks for system reliability analysis, and multi-objective optimization schemes for optimizing system performance. The proposed framework systematically integrates these schemes to enable their application to a wide range of tasks, including offline reliability-based optimization of system operational profiles. This framework is the first in the literature that incorporates health monitoring of multi-component systems with causal relationships. Using this hybrid scheme on unmanned systems can improve their reliability, extend their lifespan, and enable them to execute more challenging missions. The proposed framework is implemented and executed using a simulation model for the engine cooling and control system of an unmanned surface vessel. [ABSTRACT FROM AUTHOR]

Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the suggested framework is an intelligent Internet of Things (IoT) system designed to reduce interference. By using adaptive communication protocols and sophisticated interference management algorithms, the framework minimizes interference caused by overlapping transmissions and guarantees effective data sharing. This can be accomplished by creating an optimization model that takes into account the dynamic nature of the smart home environment and intelligently allocates resources. By maximizing the signal quality at the destination and optimizing the distribution of frequency channels and transmission power levels, the model seeks to minimize interference. A deep learning technique is used to augment the optimization model by adaptively learning and predicting interference patterns from real-time observations and historical data. The experimental results show how effective the suggested hybrid strategy is. While the deep learning model adjusts to shifting interference dynamics, the optimization model efficiently controls resource allocation, leading to better data reception performance at the destination. The system's robustness is assessed in various kinds of situations to demonstrate its flexibility in responding to changing smart home settings. This work not only offers a thorough framework for interference reduction but also clarifies how deep learning and mathematical optimization can work together to improve the dependability of data reception in smart homes. [ABSTRACT FROM AUTHOR]

Recently, low THz radar-based measurement and classification for archaeology emerged as a new imaging modality. In this paper, we investigate the classification of pottery shards, a key enabler to understand how the agriculture was introduced from the Fertile Crescent to Europe. Our purpose is to jointly design the measuring radar system and the classification neural network, seeking the maximal compactness and the minimal cost, both directly related to the number of sensors. We aim to select the least possible number of sensors and place them adequately, while minimizing the false recognition rate. For this, we propose a novel version of the Binary Grey Wolf Optimizer, designed to reduce the number of sensors, and a Ternary Grey Wolf Optimizer. Together with the Continuous Grey Wolf Optimizer, they yield the CBTGWO (Continuous Binary Ternary Grey Wolf Optimizer). Working with 7 frequencies and starting with 37 sensors, the CBTGWO selects a single sensor and yields a 0-valued false recognition rate. In a single-frequency scenario, starting with 217 sensors, the CBTGWO selects 2 sensors. The false recognition rate is 2%. The acquisition time is 3.2 s, outperforming the GWO and adaptive mixed GWO, which yield 86.4 and 396.6 s. [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]

This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and local exploration well. Therefore, the accuracy and convergence speed of the algorithm cannot be guaranteed. To improve the performance of AOA, this paper reconstructed a nonlinear MOA function, which is expected to balance the exploitation and the exploration of AOA. Furthermore, four hybrid elite pool strategies are integrated to enhance the ability to escape local optima. The proposed algorithm inherits the fast convergence of AOA and develops the performance of escaping local optima. Numerical experiment results on benchmark functions and engineering problems show that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

The paper investigates a nonlinear vibration mitigation strategy of a variable length pendulum subjected to a harmonic external excitation. A nonlinear absorber in a form of a tri-pendulum system is used to reduce the response of the primary pendulum. Thus, the paper investigates a non-stationary problem of nonlinear vibration mitigation of the primary pendulum using another nonlinear passive pendulum absorber. Due to genuine interest in capturing the nonlinear dynamic interaction, the paper numerically studies the performance of the primary mass and absorber, first, by constructing 2D maps in the unrestrained parametric space, which demonstrate the qualitative behavior of the system. Then, the surrogate optimization technique is used to tune the absorber's parameters within a given bounded set of parameters' values. The optimization is conducted based on a priori known reeling speed or acceleration/deceleration of the primary pendulum, thereby completely removing the need for acquiring a current system states essential for active feedback control. The obtained numerical results validate the proposed strategy and demonstrate high performance of the nonlinear passive absorber when it is properly tuned. [ABSTRACT FROM AUTHOR]

With the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants' benefits, poor computational efficiency, and data privacy concerns. This paper introduces the concept of a virtual power plant (VPP) as a solution for energy integration and management. To strike a balance between operational safety and the interests of market participants, a dual‐layer model is proposed. This model considers the benefits of both Distribution System Operators (DSO) and VPP, while also enhancing the consideration of distribution network constraints. The DSO considers AC optimal power flow and utilizes penalty functions to ensure network security in case of violations. To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This approach allows multiple intelligent agents to share a neural network model, effectively reducing the computational load. During the training process, only essential data is exchanged among the agents, ensuring the privacy of sensitive information. The effectiveness of the proposed model and the algorithm is validated through a case study on an IEEE 33‐node system. [ABSTRACT FROM AUTHOR]

This document provides a summary of research papers presented at a conference on mathematical optimization for fair social decisions. The conference honored Michel Balinski, a mathematician and economist known for his work in transportation problems, matching algorithms, voting theory, and fair representation. The papers covered a range of topics including optimal partisan gerrymandering, approval-based apportionment, and strategy-proof voting rules. Additionally, the document includes summaries of papers in the fields of network formation problems, game theory, and social choice theory, covering topics such as the median problem, minimum cut problems, network stability, splitting games, price of anarchy, online learning algorithms, cooperative games, and fair division. These summaries provide a quick overview of each paper's main contributions, helping library patrons determine their relevance to their research interests. [Extracted from the article]

The present paper provides a comparison of different optimization techniques applied to the model-based design of a Buck Zero Voltage Switching (ZVS) Quasi-Resonant DC-DC Converter. The comparison was made both on the basis of the duration of the optimization procedures and in terms of guaranteeing the performance of the power electronic device. The main task of the paper is to present various techniques based on the use of mathematical software for the optimal design of Quasi-Resonant DC-DC converters. These topologies were chosen because in them, the design is carried out according to computational procedures, in which several iterations are often necessary for the successful completion of the process. An optimization procedure with a target function reference curve of the output voltage was used. In this way, the optimization is performed without the need for a complete design of the device but only by using base ratios, design constraints, and past experience to determine initial values and intervals of change in circuit parameters. This is also the main advantage of the used optimization of the reference curve type of the output, compared to applying other objective functions, such as achieving minimum losses or maximum efficiency of the device. [ABSTRACT FROM AUTHOR]

Over the years, several achievements have been made in power generation and optimising hybrid renewable energy systems (HRES) to achieve nature conservation, achieve energy security, and reduce carbon emissions. However, there are many complexities in Renewable energy (RE) conversion, sizing, design, and implementation, that require optimisation techniques to achieve optimal results in terms of reliability, cost, and environmental protection over time. This paper presents an overview of research trends in Optimization methods in HRES which are classified into modern and conventional methods. These two classifications are further divided into control methods, Artificial intelligence, Iterative and mathematical operations. However, all mentioned techniques have inherent advantages and disadvantages which will be discussed in this survey. In addition, the review paper explored different types of research in computing intelligence (CI), an aspect of Artificial Intelligence (AI) that involves the development of nature-inspired algorithms for optimisation. Finally, general optimisation criteria, system sizing methods used in RES, Mathematical modelling of RES, and gaps for future work to achieve sustainability were also presented. [ABSTRACT FROM AUTHOR]

Additive manufacturing continues to see increased growth and adoption across various industries owing to technology improvements and increasing material options. This paper presents a method to optimise the infill structure placement in fused deposition modelling parts. The method uses finite element analysis and the bi-directional evolutionary structural optimisation technique to determine the optimal placement based on stress and validated through testing 3D-printed samples. The results show that multi-infill geometry outperforms traditional rectilinear infill in loading capacity and stiffness. The paper concludes with a real-world application, using this optimisation method on a railway-related part. [ABSTRACT FROM AUTHOR]

Micro-irrigation pipe network systems are commonly utilized for water transmission and distribution in agricultural irrigation. They effectively transport and distribute water to crops, aiming to achieve water and energy conservation, increased yield, and improved quality. This paper presents a model for the scaled micro-irrigation pipeline network system and provides a comprehensive review of the fundamental concepts and practical applications of optimization techniques in the field of pipeline network design. This paper is divided into four main sections: Firstly, it covers the background and theoretical foundations of optimal design for scaled micro-irrigation pipeline network systems. Secondly, the paper presents an optimal design model specifically tailored for scaled micro-irrigation pipeline networks. And then, it discusses various optimization solution techniques employed for addressing the design challenges of scaled micro-irrigation pipeline networks, along with real-world case studies. Finally, this paper concludes with an outlook on the ongoing research and development efforts in the field of scaled micro-irrigation pipeline network systems. In addition, this paper establishes a fundamental model for optimizing pipeline networks, to achieve minimum safe operation and total cost reduction. It considers constraints such as pipeline pressure-bearing capacity, maximum flow rate, and diameter. The decision-making variables include pipeline diameter, length, internal roughness, node pressure, future demand, and valve placement. Additionally, this paper provides an extensive overview of deterministic methods and heuristic algorithms utilized in the optimal design of micro-irrigation pipeline networks. Finally, this paper presents future research directions for pipeline network optimization and explores the potential for algorithmic improvements, integration of machine learning techniques, and wider adoption of EPANET 2.0 software. These endeavors aim to lay a strong foundation for effectively solving complex and challenging optimization problems in micro-irrigation pipeline network systems in the future. [ABSTRACT FROM AUTHOR]

Conventional generation maintenance scheduling (GMS) is a solution to increase the reliability of power systems and minimize the operation and maintenance costs paid by generation companies (GenCos). Nonetheless, environmental aspects, such as zero carbon emissions, have attracted global attention, leading to emission costs being paid by electricity generators. Therefore, to obtain GMS plans that consider these factors, this paper proposes multi-objective GMS models to minimize operation, maintenance, and emission costs by using lexicographic optimization as a mathematical tool. A demand response program (DRP) is also adapted to decrease emission generation and operational expenditures. The probability that no generation unit (GU) fails unexpectedly and the average net reserve value, comprising the system reliability with and without considering the GU failure rate, are demonstrated. Numerical examples are implemented for the IEEE 24-bus reliability test system. A GMS algorithm presented in a published work is run and compared to verify the robustness of the proposed GMS models. Our results indicate that this paper provides comprehensive approaches to the multi-objective GMS problem focusing on operation, maintenance, carbon, and DRP costs in consideration of technical and environmental aspects. The use of lexicographic optimization allows for the systematic and hierarchical consideration of these objectives, leading to significant benefits for GenCos. [ABSTRACT FROM AUTHOR]

As space resources become increasingly constrained, the major space-faring nations are establishing large space target monitoring systems. There is a demand for both the number and the detection capability of space-based optical monitoring equipment. The detection range (i.e., field of view) and parasitic capability (lightweight and small size) of a single optical payload will largely reduce the scale and cost of the monitoring system. Therefore, in this paper, the optic-mechanical system of an ultra-lightweight and ultra-compact space camera and the optical alignment method are investigated around a fully freeform off-axis triple-reversal large field of view (FOV) optical system. The optic-mechanical system optimisation design is completed by adopting the optic-mechanical integration analysis method, and the weight of the whole camera is less than 10 kg. In addition, to address the mounting problems caused by the special characteristics of the freeform surface optical system, a dual CGH coreference alignment method is innovatively proposed. The feasibility of the method is verified by the mounting and testing test, and the test results show that the system wavefront difference is better than 1/10 λ. The imaging test of the space camera and the magnitude test results meet the design requirements of the optical system. The optic-mechanical system design method and alignment method proposed in this paper are instructive for the design and engineering of large field of view full freeform optical loads. [ABSTRACT FROM AUTHOR]

With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs' automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis. [ABSTRACT FROM AUTHOR]

An amalgamation of Green Distributed Generation (GDG) with Distribution Networks (DNs) was developed because its performance became more efficient and sustainable. It increased the challenges in the design and operation of the protection scheme and changed the short circuit current (SCC), voltage profile, power losses, and power flow direction after the GDG penetration. These changes rely on the number, size, location, and environmental influence according to the GDG type. Therefore, many researchers have discussed protection system challenges and presented types of protection approaches to find a robust protection layout for DNs integrated with GDGs to prevent the electrical equipment from being destroyed during abnormal conditions. This paper represents an exhaustive survey of GDG integration with DNs and its effects on protection design challenges. Furthermore, this paper summarizes the modern protection methods and detection technologies, along with their important aspects that have been accessed. One of the important and reliable methods is resetting and coordinating between protection devices (PDs) that operate in the same distribution feeder. This methodology focuses on restricting the main variables and parameters used in the PDs setting after the GDG is embedded to recalculate the suitable setting and coordination. Optimization techniques should be used to find the best setting or location of the protection system in the DNs, in addition to calculating the optimal GDG scale and location. However, international standards are used to specify the suitable equations that satisfy high protection system characteristics to ensure the DNs' reliability. [ABSTRACT FROM AUTHOR]

Changes are emerging that will significantly alter structure and operation of this century's distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical loads and incorporating them into the optimization problem within an appropriate methodology is becoming increasingly important in reshaping distribution networks. In this paper, uncertainty scenarios are handled with Monte Carlo Simulation (MCS) under genetic algorithm (GA) and differential evolution- (DE-) based optimization, and probability distribution functions (pdf) of bus voltages and line current are obtained to be used in chance-constrained stochastic programming. This present study focuses on investigating impact of uncertainties in PV system operating under different irradiance scenarios on power loss with probabilistic constraints in distribution networks instead of precise deterministic limits to contribute more efficient and reliable use of energy. By combining meta-heuristic optimization and MCS technique under one framework, this paper contributes to knowledge base of how to allocate PV plants within distribution networks under chance-constrained strategy. In order to show the effectiveness of the proposed methodology, obtained optimization results are tested using MCS under set of uncertainty conditions and network constraints are evaluated for limit violation probabilities. The effectiveness of this method is investigated based on comparative results of two different optimization methods through probabilistic analysis and simulation. [ABSTRACT FROM AUTHOR]

Tunicate Swarm Algorithm (TSA) is a new bio-based optimization technique that has proven not only to be able to compete with other methods but has also shown successful performance in classic design engineering problems/benchmark test problems. However, like some population-based methods, TSA tends to be trapped in local optima, converging to global optima in a long time, unbalanced exploitation/exploration, and the inability to effectively solve high-capacity engineering problems. In this paper, the M-TSA, which is a Modified version of the TSA, is proposed to overcome such problems. M-TSA was developed in three steps. The first is the new movement strategy that improves the movement of tunicates with a spiral movement, the second is the new herd strategy that improves the herd movement of tunics with the Levy movement, and the third is the consideration of the FAD effect. In this study, the efficiency and robustness of the M-TSA algorithm is tested on the CEC'17 test suite, six real-life design engineering problems, and two complex power system engineering problems. The test results were compared with other techniques reported in the literature and with the original TSA. Comparing the results from the M-TSA technique with other techniques proves the effectiveness of M-TSA with better exploration/exploitation balance and optimal solution finding. In this paper, MATLAB 2020b software is used for optimization problems simulation. [ABSTRACT FROM AUTHOR]

Many optimization mathematical models, associated with the technical-economic processes of real-world problems, have elements of uncertainty in their structure, which places them in stochastic optimization programming. Their diversity and complexity, due to the large uncertainty space, require special methods of solving, because there is no general solution method. Within this context, in this paper we consider the category of optimization models that can contain random variable type coefficients and/or imposed probability levels on the constraints. The purpose of the paper is to propose a methodology dedicated to these studied models. Applying the methodology leads to developing a deterministic linear programming model, associated with the initial stochastic model. In fact, the proposed methodology reduces the stochastic formulation to a deterministic formulation. The methodology is illustrated with a numerical case study based on a manufacturing problem. Solving the obtained deterministic model is carried out in the version assisted by a specialized software product (WinQSB Version 2.0). It allows for the performing of a sensitivity analysis of the optimal solution, and/or a parametric analysis relative to certain model coefficients, both also presented in the paper. The main result of the study in this paper is the proposed methodology, which is applicable on a large scale, for any mathematical model of stochastic optimization of the mentioned type, regardless of complexity, dimensions and the domain of the process to which it is associated. The numerical results obtained when applying this methodology indicate its efficiency and effectiveness in finding the solution for the studied models. The approach to this issue in the present paper is determined by the wide range of stochastic optimization problems in the various studied real-life processes and by the imperative need to adopt the best decisions in conditions of uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, we introduce LagunAR, a mobile outdoor Augmented Reality (AR) application for providing heritage information and 3D visualization on a city scale. The LagunAR application was developed to provide historical information about the city of La Laguna in the XVI century, when it was the main city in the Canary Islands. The application provides a reconstructed 3D model of the city at that time that is shown on a mobile phone over-imposed on the actual city using geolocation. The geolocated position is used also for providing information of several points of interest in the city. The paper describes the design and implementation of the application and details the optimization techniques that have been used to manage the full information of the city using a mobile phone as a sensor and visualization tool. We explain the application usability study carried out using a heuristic test; in addition it is probed by users in a qualitative user test developed as preliminary research. Results show that it is possible to develop a real-time application that shows the user a city-scale 3D model and also manages the information of the points of interest. [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]

In this paper, we investigate a very challenging task of automatically generating presentation slides for academic papers. The generated presentation slides can be used as drafts to help the presenters prepare their formal slides in a quicker way. A novel system called PPSGen is proposed to address this task. It first employs the regression method to learn the importance scores of the sentences in an academic paper, and then exploits the integer linear programming (ILP) method to generate well-structured slides by selecting and aligning key phrases and sentences. Evaluation results on a test set of 200 pairs of papers and slides collected on the web demonstrate that our proposed PPSGen system can generate slides with better quality. A user study is also illustrated to show that PPSGen has a few evident advantages over baseline methods. [ABSTRACT FROM PUBLISHER]

Pyrolysis is the most commonly employed technology to convert widely available biomaβ materials into useful products like biofuels. In the present study, pyrolysis of paper mulberry (Brouβonetia papyrifera) was performed in order to observe the effect of temperature and particle size on product distribution and yield, and application of biochar product to improve growth and yield of maize. The experiments were performed in a fixed-bed reactor using three pyrolysis temperatures (350°C, 450°C, 550°C) and three particle sizes (1 mm, 2 mm and 3 mm) of B. papyrifera. The pyrolysis products were collected within three different groups as gaseous products, liquids and solid biochar. The results showed that the maximum biochar product was obtained at 350°C and 3 mm particle size while the highest bio-oil yield was attained at 450°C and 2 mm particle size. However, the optimum gas yield was achieved at 550°C and 1 mm particle size. A pot experiment was conducted to determine the effect of produced biochar on maize plant emergence, growth and yield. The results showed a substantial increase in plant height, shoot dry weight and maize yield compared to untreated control. These findings revealed that the production of biofuels can be improved by optimizing operating conditions and biochar produced during pyrolysis could be used as soil amendment for improving growth and yield of maize. © 2014 Friends Science Publishers [ABSTRACT FROM AUTHOR]

The potential of laser ablation in conservation of paper artifacts of cultural interest is under investigation since the second half of nineties. However, the works carried out on the topic are relatively a few as compared with those dedicated to stone, metal and painted artworks. Furthermore, in the latter cases, widespread applications have been successfully carried out, whereas laser cleaning of aged paper is still far from the conservation practice. There are serious risks to produce short- and/or long-term chemical alterations and/or mechanical damages at relatively low fluences. Here, we report a systematic investigation on laser removal of mold growths from prepared and naturally aged paper samples. Fundamental wavelength and second harmonic of QS Nd:YAG laser are tested and compared through optical and spectroscopic characterizations. This allowed defining optimized irradiation conditions and foreseeing further improvements from pulse duration optimization. [ABSTRACT FROM AUTHOR]

Lithium–ion batteries are key elements of electric vehicles and energy storage systems, and their accurate State of Charge (SOC) estimation is momentous for battery energy management, safe operation, and extended service life. In this paper, the Multi-Kernel Relevance Vector Machine (MKRVM) and Whale Optimization Algorithm (WOA) are used to estimate the SOC of lithium–ion batteries under different operating conditions. In order to better learn and estimate the battery SOC, MKRVM is used to establish a model to estimate lithium–ion battery SOC. WOA is used to automatically adjust and optimize weights and kernel parameters of MKRVM to improve estimation accuracy. The proposed model is validated with three lithium–ion batteries under different operating conditions. In contrast to other optimization algorithms, WOA has a better optimization effect and can estimate the SOC more accurately. In contrast to the single kernel function, the proposed multi-kernel function greatly improves the precision of the SOC estimation model. In contrast to the traditional method, the WOA-MKRVM has a higher precision of SOC estimation. [ABSTRACT FROM AUTHOR]

With this note, we want to open a discussion on the duality theory for Interval Optimization. We begin by showing that some duality results Wu ( J Optim Theory Appl 144:615-628, ) are useless. [ABSTRACT FROM AUTHOR]

Water footprint (WF) is widely used as a life cycle assessment (LCA) tool to assess the environmental impacts of water usage associated with forestry-based production. The calculations of WF are significantly influenced by the raw materials and the process. Some information is available on WF in the papermaking industry. However, there has been little consideration of the correlation between the WF and the properties of paper. Technically, the WF and the properties of paper are impacted by the raw materials. Generally, the ideal formula of raw materials used to make paper could decrease the WF while maintaining the properties of the paper. In the current study, the extreme vertices mixture design was used to optimize the WF and properties of the handsheet by the raw materials. The new model indicated that the WF of the handsheet was decreased significantly while the properties was maintained through the adjustment of the raw materials. [ABSTRACT FROM AUTHOR]

At present, the packing method of "plastic bag–buffer packing–packing paper box" is adopted for bearing packaging. However, the common packing method has a poor packing effect and poor versatility. In this study, a new biomimetic cobweb cushion is proposed to solve the problem of insufficient cushioning capacity of high-precision bearing cushion packaging pads. First, according to the nature of cobweb form, the cobweb cushion structure configuration is determined. Next, based on the structure of the cushion and the relationship between the parameters of radial thread and spiral thread, a mechanical and target optimization model is established. The stress nephogram of bearing and the cobweb cushion are analyzed under three drop heights of 381, 610, and 700 mm, in the finite element simulation software to ensure that the maximum bearings stress is not beyond the material yield strength. Via the 3D printing technology, a cobweb cushion shell cast is made. Drop tests of the bearing were performed, and the results were verified with the finite element simulation analysis. This research can provide technical support for the protection of high-precision bearings from accidental drops during transportation. [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]

A multi-modal search space can be defined as having multiple attraction basins - each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers. [ABSTRACT FROM AUTHOR]

In this note, it is shown that there is error in the existing technique (Khan, I.U., Ahmad, T., Maan, N: A simplified novel technique for solving fully fuzzy linear programming problems. J. Optim. Theory Appl. 159:536-546, ). [ABSTRACT FROM AUTHOR]

This paper proposes a strengthened RIME algorithm to tackle continuous optimization problems. RIME is a newly proposed physical-based evolutionary algorithm (EA) inspired by the soft and hard rime growth process of rime-ice, which has a powerful exploitation ability. But in complex optimization problems, RIME will easily trap into local optima and the optimization will become stagnation. Noticing this issue, we introduce three techniques to the original RIME: (1) Latin hypercube sampling replaces the random generator as the initialization strategy, (2) modified hard rime search strategy, and (3) embedded distance-based selection mechanism. We evaluate our proposed SRIME in 10-D, 30-D, 50-D, and 100-D CEC2020 benchmark functions and eight real-world engineering optimization problems with nine state-of-the-art EAs. Experimental and statistical results show that the introduction of three techniques can significantly accelerate the optimization of the RIME algorithm, and SRIME is a competitive optimization technique in real-world applications. Ablation experiments are also provided to analyze our proposed three techniques independently, and the embedded distance-based selection contributes most to the improvement of SRIME. The source code of SRIME can be found in https://github.com/RuiZhong961230/SRIME. [ABSTRACT FROM AUTHOR]

To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems. [ABSTRACT FROM AUTHOR]

Several optimization-based population search methods have been proposed; they use various operators that permit exploring the search space. These methods typically suffer from local search (LS) problems and are unbalanced between exploration and exploitation. Consequently, recent researchers sought to modify the algorithms to avoid search problems using local search techniques to intensify the exploitation when is necessary. This paper proposes a novel single-based local search optimization algorithm called the non-monopolize search (NO). The NO is a single-solution metaphor-free algorithm, and its operators are designed based to explore and exploit along the iterative process. The NO works only with a candidate solution, and the operators modify the dimension to move the current solution along the search space. The NO is an effective LS method that combines the benefits of exploration with exploitation. Different from other LS, the NO can escape from suboptimal solutions thanks to the randomness incorporated into its operators. This is the main advantage of the NO. Experiments are conducted on standard benchmark functions to validate the performance of the proposed non-monopolize search optimization technique. The results are compared with other well-known methods, and the proposed NO got better results. Moreover, the proposed NO can be considered a powerful alternative to improve the optimization algorithms' performance and help avoid local search problems. Source codes of NO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/156154-the-non-monopolize-search-no. [ABSTRACT FROM AUTHOR]

With large-scale grid-connected renewable energy, new power systems require more flexible and reliable energy storage power sources. Pumped storage stations play an important role in peak shaving, valley filling, and promoting renewable energy consumption. This paper presents the reasonable energy-abandonment operation of a combined power generation system (CPGS), in which a pumped storage station is the core control power, with an ultra-high proportion of renewable energy. Firstly, based on the seasonal characteristics of wind, solar, and load demand, typical days are selected through improved clustering analysis algorithms. Then, a daily optimal scheduling model for combined power generation systems (CPGS) is developed with the goals of economy, low-carbon, and stable operation. Finally, the correlation between the energy-abandonment rate and pumped storage station peak shaving and system optimization operation indicators is obtained by a reasonable energy-abandonment calculation method considering source-grid-load coordination. Taking the operation data of an energy base in the western region of China as an example, when the penetration rate of renewable energy is 60–70% in the future, the operating cost on the power side is greatly affected by the construction of the source side. When the system operates at a planned reasonable energy-abandonment rate of 2%, electricity regulation, load tracking, and daily operating costs all show better performance. [ABSTRACT FROM AUTHOR]

In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the implementation and analysis of PID controllers in an automated voltage regulator (AVR) system. A novel tuning technique, Virtual Time response-based iterative gain evaluation and re-design (V-Tiger), is introduced to iteratively adjust PID gains for optimal control performance. The study begins with the development of a mathematical model for the AVR system and initialization of PID gains using the Pessen Integral Rule. Virtual time-response analysis is then conducted to evaluate system performance, followed by iterative gain adjustments using Particle Swarm Optimization (PSO) within the V-Tiger framework. MATLAB simulations are employed to implement various controllers, including the V-Tiger PID controller, and their performance is compared in terms of transient response, stability, and control signal generation. Robustness analysis is conducted to assess the system's stability under uncertainties, and worst-case gain analysis is performed to quantify robustness. The transient response of the AVR with the proposed PID controller is compared with other heuristic controllers such as the Flower Pollination Algorithm, Teaching–Learning-based Optimization, Pessen Integral Rule, and Zeigler-Nichols methods. By measuring the peak closed-loop gain of the AVR with the controller and adding uncertainty to the AVR's field exciter and amplifier, the robustness of proposed controller is determined. Plotting the performance degradation curves yields robust stability margins and the accompanying maximum uncertainty that the AVR can withstand without compromising its stability or performance. Based on the degradation curves, robust stability margin of the V-Tiger PID controller is estimated at 3.5. The worst-case peak gains are also estimated using the performance degradation curves. Future research directions include exploring novel optimization techniques for further enhancing control performance in various industrial applications. [ABSTRACT FROM AUTHOR]

This paper optimizes the assignment of shared automated vehicles under users' uncertain departure times. Automated vehicles can drive themselves, so no staff are needed to relocate vehicles in the one-way carsharing system. To optimize fleet placement and use, a twophase solution method was established. Phase 1 strategically distributes vehicles across stations using a system optimization approach, while Phase 2 tracks vehicle movements via an agent-based simulation model. Phase 1 solutions serve as inputs to Phase 2 simulations. Using a fleet size of roughly 10,000 vehicles, case study applications were run across the Austin, Texas region's six-county network. In the base case setting, results suggest that system profits are optimized when vehicle rental is priced at $1.28=km ($0.8=mi). The number of proactive vehicle relocations falls 9.8% if the relocation operation cost rises from $0.096=km ($0.06=mi) to $0.32=km ($0.2=mi). Average per-trip profit is $10.60 when using high-cost vehicles, and $11.60 when using low-cost vehicles. Results from a 3-h simulation show an average person-trip length of 25 km (15.6 mi), with 29.6 min of average driving time. When a 24-h day was simulated, the vehicle-occupied time and vehicle-distance traveled were 4 h and 200 km (125 mi) per vehicle-day, respectively. The low coefficient of variation of satisfied demand across 30 demand scenarios suggests the robustness of the two-phase solution method. [ABSTRACT FROM AUTHOR]

The casing deformation prevention technology based on the optimization of cement slurry is proposed to reduce the casing deformation of shale oil and gas wells during hydraulic fracturing. In this paper, the fracture mechanism of hollow particles in cement sheath was firstly analyzed by discrete element method, and the effect of hollow particles in cement on casing deformation was investigated by laboratory experiment method. Finally, field test was carried out to verify the improvement effect of the casing deformation based on cement slurry modification. The results show that the formation displacement can be absorbed effectively by hollow particles inside the cement transferring the excessive deformation away from casing. The particles in the uncemented state provide deformation space during formation slipping. The casing with diameter of 139.7 mm could be passed through by bridge plug with the diameter of 99 mm when the mass ratio of particle/cement reaches 1:4. According to the field test feedback, the method based on optimization of cement slurry can effectively reduce the risk of casing deformation, and the recommended range of hollow microbeads content in the cement slurry is between 15% and 25%. [ABSTRACT FROM AUTHOR]

During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods. [ABSTRACT FROM AUTHOR]

The domain of material science has made great strides in recent years, especially in the fields of metallurgy and ceramic materials and the production of highly trustworthy, cost-effective and economically useful components for use in many industries. For the production of contours and intricate forms in conductive materials, Electrical Discharge Machining (EDM) is by far the most versatile and cost-effective alternative to standard machining processes. In-depth discussion and analysis of the following topics may be found throughout this review paper study. Surface topography and machining properties are investigated in this literature review to determine the impact of mechanical, chemical, electrochemical, and thermal material removal techniques. Different characteristics of dielectric mediums are also covered. Non-conductive and conductive ceramic composite performance characteristics, surfacetexture, mechanical and electrical qualities and geometrical tolerances are investigated as a function of electrode material. Through the perspective of composite materials, a variety of electrical discharge devices' performance metrics and properties are compared and contrasted. The performance metrics and characteristics of a wide variety of electrical discharge machines are compared and contrasted in light of the composite materials in order to identify their distinguishing characteristics. EDM research is being conducted on a variety of sophisticated conductive materials, to explore their unpredictable effects on EDM and their specialized applications. Methods for optimizing the study of composite material and their effects on EDM's numerous aspects include Design of Experiments, Analysis of Variance (ANOVA), Response Surface Methodology, Taguchi with Grey Relational Analysis, and so on. X-ray Diffraction (XRD) and Energy-Dispersive X-ray analysis (EDAX) are used to study the topography of a variety of composite materials and the latest optimization strategies are also investigated using a variety of algorithms. [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]

Since cloud computing has an abundance of users, the system has to execute a wide range of tasks. Task scheduler methods that are both robust and efficient while delivering the best outcomes are required. The task volume and runtime in the cloud vary rapidly, making task assessment and resource mapping difficult. Security issues, communication delays, and data loss are substantial barriers to scheduling. Furthermore, optimization techniques can be utilized to reduce load and assign tasks so that the user can finish tasks faster. This paper offers a hybrid job scheduling technique for cloud computing using adaptive particle swarm optimization and ant colony optimization particle swarm optimization-ant colony optimization (adaptive PSO-ACO). After rapidly finding the initial solution via particle swarm optimization, the ant colony optimization approach establishes its first pheromone distribution. The suggested hybrid algorithm is compared to standalone PSO and ACO algorithms. Compared to ACO, the percentage decrease is 7.9%. Hybrid method has the lowest total cost, 55% less compared to PSO. Tasks vary when virtual machines (VMs) are constant and VMs vary when tasks are constant. Parameters like final cost, makespan, fitness value, computation time and weighted time are assessed to evaluate the performance of the hybrid task scheduling algorithm. [ABSTRACT FROM AUTHOR]

The application of 'Target Cascading' in the product development process is well known to be effective in various industry fields, and it is a methodology that is already actively applied in various forms in the field of vehicle Ride & Handling (R&H) performance development. This paper proposes a model-based vehicle performance benchmarking methodology and a technology for setting system characteristics to achieve handling performance objectives as a means of utilization for this. Rather than introducing already well-known target deployment techniques, a process is proposed for practical use in the vehicle development phase through the development of interfaces between low degrees of freedom vehicle model and optimization software. The methodology presented in this study is expected to be available in a timely manner as a practical vehicle performance development tool. [ABSTRACT FROM AUTHOR]

Middle-box based attacks create serious functional defects in the devices such as firewalls, address translators, load balancers, server units, and other data inspecting devices. Middle-box based attacks are more severe than terminal node-based attacks. These attacks are mainly initiated to malfunction the internal events of middle-level devices. Notably, Internet of Healthcare Things (IoHT) is a completely distributed and heterogeneous environment that leads into more vulnerable events. In the distributed medical system field, the middle-box devices manage secret medical data transactions, internal network communication, patient data protection principles and packet inspection procedures. The medical data collected from each patient needs secrecy and stability in various aspects in the IoHT environment. The middle-box based attacks injected into the IoHT nodes create inefficiency in maintaining patient data, Denial of Service (DoS) at middle-box nodes, excessive diagnosis time, and lack of data protection. For handling these issues, a new security architecture and a new attack detection model are proposed in this paper which has been developed using ranking subsets and Convolutional Neural Network (CNN) principles. The proposed CNN with ranking principles model is designed with binary class fuzzy fisher face optimization technique and flower pollination optimization method to initiate feature extraction. The extracted features of each data flow are analyzed using CNN and ranking subset methodologies. In this proposed model, the continuous involvement of middle-box events in respective devices are classified under various anomaly cases and legitimate cases. The proposed IoHT model attains maximum success rate than the existing models as indicated in implementation section. [ABSTRACT FROM AUTHOR]

Interference minimization whether on topology or routing level has been a prominent concern in wireless sensor networks (WSNs). Existing graph-based approaches focus on minimizing whole topology and network interference. Work on having an interference minimum routing path on bidirectional connected graph models of WSNs is still left to explore under different interference models. So, this paper focuses on establishing an efficient routing path from the source to the destination node of minimum total link interference and maximum receiver node interference by optimally balancing between both the interference levels on any bidirectional connected graph model of WSN. We have proposed a dual-interference model based optimization (DIMO) routing algorithm by engaging ant colony optimization technique with two distinct interference models simultaneously that are link SUM interference model and receiver interference model. Moreover, a new interference model named as Disc-SUM model has also been developed whose implementation with Dijkstra's algorithm results in a dual-interference minimized routing path in quadratic time complexity. Simulation work has been carried out to validate the DIMO algorithm and the Disc-SUM model for implementing a routing path between the source to the destination node with optimally reduced total link interference, maximum receiver node interference, total transmission cost, and hop counts. [ABSTRACT FROM AUTHOR]

The development of information technology, wireless communications have become prevalent in every other field we can imagine. Sensor nodes are the fundamental element of wireless communication networks. When they are deployed, they gather environmental data from the surrounding area and send it to the base station for further analysis. This paper focused on creating a routing technique that improved wireless communication energy conservation through the use of fuzzy logic and ant colony optimisation in order to extend battery life. Ant colony optimisation algorithm (HFACO) and hybrid fuzzy logic were used in the construction of the routing protocol. Numerous derived techniques have been adapted to dynamic problems in real variables, stochastic problems, multi-targets, and parallel implementations. Ant colony optimisation algorithms have been applied to a wide range of combinatorial optimisation problems, from quadratic assignment to protein folding or routing vehicles. Several techniques that are based on how ant colonies forage have been used recently to solve challenging discrete optimisation problems.By taking into account the node's energy and traffic load, fuzzy logic was utilised to determine the total node cost to the gateway. Ant colony optimisation (ACO) was used to find the shortest path between the source and destination sensor nodes, and the path's value was determined by measuring its shortest distance. When compared to the ACO's performance in the same conditions, the Matlab simulation's results showed superior energy conservation performance. This improved routing technique can be applied to Wi-Fi sensor networks in industrial environments. [ABSTRACT FROM AUTHOR]

The built environment is a significant contributor to global energy consumption and greenhouse gas emissions. Advancements in the adoption of environmentally friendly building technology have become crucial in promoting sustainable development. These advancements play a crucial role in conserving energy. The aim is to achieve an optimal design by balancing various interrelated factors. The emergence of innovative techniques to address energy conservation have been witnessed in the built environment. This review examines existing research articles that explore different metaheuristic optimization techniques (MOTs) for energy conservation in buildings. The focus is on evaluating the simplicity and stochastic nature of these optimization techniques. The findings of the review present theoretical and mathematical models for each algorithm and assess their effectiveness in problem solving. A systematic analysis of selected algorithms using MOT is conducted, considering factors that influence wellbeing, occupant health, and indoor environmental quality. The study examines the variations among swarm intelligence MOTs based on complexity, advantages, and disadvantages. The algorithms' performances are based on the concept of uncertainty in consistently providing optimal solutions. The paper highlights the application of each technique in achieving energy conservation in buildings. [ABSTRACT FROM AUTHOR]

Dynamic binary translation (DBT) is a core technology that enables the migration of legacy software to different instruction set architectures while maintaining the original semantics. However, the development and maintenance of an efficient cross-DBT system are challenging. Key challenges include memory access overhead, inefficient instruction simulation, and frequent context switches. In this paper, we propose three novel optimization techniques. First, we formalize a register mapping cost model and investigate a hierarchical register mapping approach to bridge the memory access overhead. Second, we accelerate floating point (FP) emulation by surrounding the use of hardware FP unit with high-efficiency non-FP code. Third, we present a function inlining approach to alleviating the overhead associated with indirect control lookup. On the system side, we implement our approach on ARM64 and SW64 architectures based on QEMU and extensively evaluate the effectiveness with the SPEC2006 benchmark suite. The experimental results show that an average of 1.28× performance speedup and 13.41% code size reduction can be achieved on SW64. Similarly, on ARM64, we achieve an average of 1.15× performance speedup and 11.48% code size reduction. [ABSTRACT FROM AUTHOR]