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This paper proposed a new libration decoupling analytical speed function (LD-ASF) in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration stability in cargo transportation. The LD-ASF is further optimized for payload transportation efficiency by a novel coordinate game theory to balance competing control objectives among payload transport speed, stable end body's libration, and overall control input via model predictive control. The transfer period is divided into several sections to reduce computational burden. The validity and efficacy of the proposed LD-ASF and coordinate game-based model predictive control are demonstrated by computer simulation. Numerical results reveal that the optimized LD-ASF results in higher transportation speed, stable end body's libration, lower thrust fuel consumption, and more flexible optimization space than the classic analytical speed function. [ABSTRACT FROM AUTHOR]

As a complex ecosystem composed of flora and fauna, the forest has always been vulnerable to threats. Previous researchers utilized environmental audio collections, such as the ESC-50 and UrbanSound8k datasets, as proximate representatives of sounds potentially present in forests. This study focuses on the application of deep learning models for forest sound classification as an effort to establish an early threats detection system. The research evaluates the performance of several pre-trained deep learning models, including MobileNet, GoogleNet, and ResNet, on the limited FSC22 dataset, which consists of 2,025 forest sound recordings classified into 27 categories. To improve classification capabilities, the study introduces a hybrid model that combines neural network (CNN) with a Bidirectional Long-Short-Term Memory (BiLSTM) layer, designed to capture both spatial and temporal features of the sound data. The research also employs Pareto-Mordukhovich-optimized Mel Frequency Cepstral Coefficients (MFCC) for feature extraction, improving the representation of audio signals. Data augmentation and dimensionality reduction techniques were also explored to assess their impact on model performance. The results indicate that the proposed hybrid CNN-BiLSTM model significantly improved classification loss and accuracy scores compared to the standalone pre-trained models. GoogleNet, with an added BiLSTM layer and augmented data, achieved an average reduced loss score of 0.7209 and average accuracy of 0.7852, demonstrating its potential to classify forest sounds. Improvements in loss score and classification performance highlight the potential of hybrid models in environmental sound analysis, particularly in scenarios with limited data availability.

Abstract In the current landscape, there is a rapid increase in the creation of new algorithms designed for specialized problem scenarios. The performance of these algorithms in unfamiliar or practical settings often remains untested. This paper presents a new development, the multi-objective Runge–Kutta optimizer (MORKO), which is built upon the principles of elitist non-dominated sorting and crowding distance. The goal is to achieve superior efficiency, diversity, and robustness in solutions. MORKO effectiveness is further enhanced by incorporating various strategies that maintain a balance between diversity and execution efficiency. This approach not only directs the search toward optimal regions but also ensures that the process does not become stagnant. The efficiency of MORKO is compared against renowned algorithms like the multi-objective marine predicator algorithm (MOMPA), multi-objective gradient-based optimizer (MOGBO), multi-objective evolutionary algorithm based on decomposition (MOEA/D), and non-dominated sorting genetic algorithm (NSGA-II) on several test benchmarks such as ZDT, DTLZ, constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design (brushless DC wheel motor, safety isolating transformer, helical spring, two-bar truss, welded beam, disk brake, tool spindle and cantilever beam) problems. We used unique, non-overlapping performance metrics for this comparison and suggested a fresh correlation analysis technique for exploration. The MORKO algorithm outcomes were rigorously tested and confirmed using the non-parametric statistical evaluations. The MORKO algorithm proves to excel in deriving comprehensive and varied solutions for many tests and practical challenges, owing to its multifaceted features. Looking ahead, MORKO has potential applications in complex engineering and management tasks.

Abstract This paper investigates the flexural bearing behavior of reinforced concrete beams through experimental analysis and advanced machine learning predictive models. The primary problem centers around understanding how varying compositions of construction materials, particularly the inclusion of recycled aggregates and carbon fiber-reinforced polymer (CFRP), affect the structural performance of concrete beams. Eight beams, including those with natural aggregates, recycled aggregates, fly ash, and CFRP, were tested. The study employs state-of-the-art machine learning frameworks, including Random Forest Regressor (RFR), XGBoost (XGB), and LightGBM (LGBM). The formation of these models involved data acquisition from experiments, preprocessing of key input features (such as rebars area, cement portion, recycled and natural aggregate masses, silica fume, fly ash, compressive strength, and CFRP presence), model selection, and hyperparameter tuning using Pareto optimization. The models were then evaluated using performance metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Outputs focus on load-induced deflection and mid-span displacement. With a dataset of 4851 samples, the optimized models demonstrated excellent performance. The experimental results revealed substantial enhancements in both compressive strength and load-bearing capacity, notably observed in beams incorporating 70% recycled aggregate and 10% silica fume. These beams exhibited a remarkable increase in compressive strength of up to 53.03% and a 7% boost in load-bearing capacity compared to those without recycled aggregate. By integrating experimental analysis with advanced computational techniques, this study advances the understanding of eco-friendly construction materials and their performance, shedding light on the intricate interactions between sustainable construction materials and the flexural bearing behavior of beams.

A novel design strategy for improving the radiative performance of simultaneous multibeam (SMB) phased arrays is addressed. The proposed scheme relies on the adoption of mixed and multiple antenna element factors with a dynamic selection of their radiation patterns whose choice depends on the desired SMB pointing directions. In addition, a Penrose-inspired clustering technique is also employed for reducing the array feed points. Compared with traditional phased arrays based on a single antenna element factor, the novel array architecture allows the scan angle range to be widened by improving the minimum array gain as well as reducing the peak side lobe level (PSLL). The superior radiative performance of the proposed approach with respect to the clustered phased arrays with a single-mode element factor is assessed in SMB scenarios comprising two and three main lobe peaks. The notable SMB radiative improvement has been also confirmed from a statistical point of view by considering up to four and five concurrent main lobes. The remarkable radiative improvements confirm the effectiveness of the proposed solution, which also represents an appealing candidate for its exploitation in multiuser and multibeam communications. [ABSTRACT FROM AUTHOR]

We numerically investigate the relationship between the main parameters of thinned antenna arrays using a specifically designed evolutionary algorithm, the Multi-Objective Pareto Evolution for Thinning (MOPET). We provide some useful results that allow for the assessment of the achievable performance of antenna arrays and help researchers and practitioners design radar, 5G, and 6G systems. In particular, our approach allows us to quantify the advantage of thinned arrays with respect to traditional equispaced arrays (EA); as an example, using the same number of radiators, we can obtain the same directivity of an EA with a reduction in the side-lobe level (SLL) of more than 10dB, or increase the directivity of a couple of dB maintaining the same SLL of the EA, or get a combination of the two improvements. Moreover, the advantage of thinned architectures with respect to standard EA seems to improve with the increase in the dimension of the array. [ABSTRACT FROM AUTHOR]

This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines (FOWTs), and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses. Although the three-dimensional potential flow (TDPF) panel method is recognized for its precision in calculating FOWT motion responses, its computational intensity necessitates an alternative approach for efficiency. Herein, a novel application of varying fidelity frequency-domain computational strategies is introduced, which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy. The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies. Optimization objectives are centered on the platform's motion response in heave and pitch directions under general sea conditions. The steel usage, the range of design variables, and geometric considerations are optimization constraints. The angle of the pontoons, the number of columns, the radius of the central column and the parameters of the mooring lines are optimization constants. This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm. For the case of the IEA UMaine VolturnUS-S Reference Platform, Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives. The efficacy of final designs is substantiated through the time-domain calculation model, which ensures that the motion responses in extreme sea conditions are superior to those of the initial design. [ABSTRACT FROM AUTHOR]

The traditional mathematical models for solving the economic dispatch problem at the generation level primarily focus on minimizing overall operational costs while ensuring demand is met across various periods. However, contemporary power systems integrate a diverse mix of generators from both conventional and renewable energy sources, contributing to economically efficient energy production and playing a pivotal role in reducing greenhouse gas emissions. As the complexity of power systems increases, the scope of economic dispatch must expand to address demand across multiple regions, incorporating a range of objective functions that optimize energy resource utilization, reduce costs, and achieve superior economic and technical outcomes. This paper, therefore, proposes an advanced optimization model designed to determine the hourly power output of various generation units distributed across multiple areas within the power system. The model satisfies the dual objective functions and adheres to stringent technical constraints, effectively framing the problem as a nonlinear programming challenge. Furthermore, an in-depth analysis of the resulting and exchanged energy quantities demonstrates that the model guarantees the hourly demand. Significantly, the system's efficiency can be further enhanced by increasing the capacity of the interconnection links between areas, thereby generating additional savings that can be reinvested into expanding the links' capacity. Moreover, the multi-objective model excels not only in meeting the proposed objective functions but also in optimizing energy exchange across the system. This optimization is applicable to various types of energy, including thermal and renewable sources, even those characterized by uncertainty in their primary resources. The model's ability to effectively manage such uncertainties underscores its robustness, instilling confidence in its applicability and reliability across diverse energy scenarios. This adaptability makes the model a significant contribution to the field, offering a sophisticated tool for optimizing multi-area power systems in a way that balances economic, technical, and environmental considerations. [ABSTRACT FROM AUTHOR]

Abstract Background The aim was to investigate the noise and bias properties of quantitative 177Lu-SPECT with respect to the number of projection angles, and the number of subsets and iterations in the OS-EM reconstruction, for different total acquisition times. Methods Experimental SPECT acquisition of six spheres in a NEMA body phantom filled with 177Lu was performed, using medium-energy collimators and 120 projections with 180 s per projection. Bootstrapping was applied to generate data sets representing acquisitions with 20 to 120 projections for 10 min, 20 min, and 40 min, with 32 noise realizations per setting. Monte Carlo simulations were performed of 177Lu-DOTA-TATE in an anthropomorphic computer phantom with three tumours (2.8 mL to 40.0 mL). Projections representing 24 h and 168 h post administration were simulated, each with 32 noise realizations. Images were reconstructed using OS-EM with compensation for attenuation, scatter, and distance-dependent resolution. The number of subsets and iterations were varied within a constrained range of the product number of iterations $$\times$$ × number of projections $$\le 2400$$ ≤ 2400 . Volumes-of-interest were defined following the physical size of the spheres and tumours, the mean activity-concentrations estimated, and the absolute mean relative error and coefficient of variation (CV) over noise realizations calculated. Pareto fronts were established by analysis of CV versus mean relative error. Results Points at the Pareto fronts with low CV and high mean error resulted from using a low number of subsets, whilst points at the Pareto fronts associated with high CV but low mean error resulted from reconstructions with a high number of subsets. The number of projection angles had limited impact. Conclusions For accurate estimation of the 177Lu activity-concentration from SPECT images, the number of projection angles has limited importance, whilst the total acquisition time and the number of subsets and iterations are parameters of importance.

Credit purchase enables the manufacturers in the e-commerce environment to provide pre-sales service that consumers can experience first and pay later. This paper considers demand associated with price and green promotion service level and builds four decentralized game models to study two green promotion service allocation strategies and demand forecasting information sharing strategies in a dual-channel environment. The effects of the degree of dual-channel competition and free-riding on the perfect Bayesian Nash equilibrium are studied. The results show that the retailer should actively cooperate with the manufacturer and share private forecasting information if the coefficient of channel substitution is relatively high. Sharing information will aggravate double marginalization and hurt the retailer. In addition, the retailer's profit is positively influenced by the forecasting accuracy in four models. When the manufacturer invests in the green promotion service, the prediction accuracy hurts the manufacturer's profit without information sharing and there is a positive impact with information sharing. In particular, when a retailer provides service, we take the consumer's free-riding behavior into account, and we find that free-riding hurts both parties and the whole supply chain. In addition, the manufacturer's profit is irrelevant to the prediction accuracy without information sharing and positively influenced by the accuracy with information sharing. [ABSTRACT FROM AUTHOR]

During terminal guidance, the attack platform is provided with a high‐resolution image of the target area through the application of synthetic aperture radar. Additionally, the stealth trajectory with low observability can significantly impact mission success. This paper considers both the performance of missile‐borne synthetic aperture radar imaging and stealth performance as influencing factors for terminal trajectory optimization, which is modelled as a constrained multi‐objective optimization problem. The application of the pseudospectral method in the solution of optimal control problems has led to the proposal of the hybrid genetic algorithm pseudospectral optimization framework. The problem is decomposed into several single‐objective optimal control problems, which can generate a specific initial population for the genetic algorithm to obtain a set of Pareto‐optimal solutions. Finally, the numerical simulations demonstrate the effectiveness of the proposed optimization approach compared with the benchmark scheme. [ABSTRACT FROM AUTHOR]

Most species of Keratella possess dome-shaped, dorsal plates comprising a network of polyhedral units (facets), delineated by slightly raised ridges. The arrangement of facets define a species' facet pattern (FP), with the resulting structure resembling a geodesic dome. Researchers have sorted species into categories based on their FPs, but those have not been analyzed. Additionally, while a strong lorica has been suggested to protect Keratella from predatory attack or other actions causing blunt force trauma (BFT), we know little of how that occurs. Thus, in our study we tested two hypotheses. (1) There is support for categorizing Keratella species into unique groupings based on their FPs. (2) FPs provide resistance to physical stresses. To test that hypothesis we used the structural analysis software SkyCiv©. Our results indicate support for four FP categories. Additionally, the SkyCiv analysis provided preliminary 'proof-of-concept' that Keratella FPs have a functional significance: i.e., adding or subtracting facets in our model was followed by a change in predicted structural reliability. We posit that FPs are adaptations protecting Keratella from fractures to the lorica that may result from BFT incurred during predatory attack by copepods or while caught within the branchial chambers of daphnids. [ABSTRACT FROM AUTHOR]

Background: The aim was to investigate the noise and bias properties of quantitative 177Lu-SPECT with respect to the number of projection angles, and the number of subsets and iterations in the OS-EM reconstruction, for different total acquisition times. Methods: Experimental SPECT acquisition of six spheres in a NEMA body phantom filled with 177Lu was performed, using medium-energy collimators and 120 projections with 180 s per projection. Bootstrapping was applied to generate data sets representing acquisitions with 20 to 120 projections for 10 min, 20 min, and 40 min, with 32 noise realizations per setting. Monte Carlo simulations were performed of 177Lu-DOTA-TATE in an anthropomorphic computer phantom with three tumours (2.8 mL to 40.0 mL). Projections representing 24 h and 168 h post administration were simulated, each with 32 noise realizations. Images were reconstructed using OS-EM with compensation for attenuation, scatter, and distance-dependent resolution. The number of subsets and iterations were varied within a constrained range of the product number of iterations × number of projections ≤ 2400 . Volumes-of-interest were defined following the physical size of the spheres and tumours, the mean activity-concentrations estimated, and the absolute mean relative error and coefficient of variation (CV) over noise realizations calculated. Pareto fronts were established by analysis of CV versus mean relative error. Results: Points at the Pareto fronts with low CV and high mean error resulted from using a low number of subsets, whilst points at the Pareto fronts associated with high CV but low mean error resulted from reconstructions with a high number of subsets. The number of projection angles had limited impact. Conclusions: For accurate estimation of the 177Lu activity-concentration from SPECT images, the number of projection angles has limited importance, whilst the total acquisition time and the number of subsets and iterations are parameters of importance. [ABSTRACT FROM AUTHOR]

We present a new gradient-like dynamical system related to unconstrained convex smooth multiobjective optimization which involves inertial effects and asymptotic vanishing damping. To the best of our knowledge, this system is the first inertial gradient-like system for multiobjective optimization problems including asymptotic vanishing damping, expanding the ideas previously laid out in [H. Attouch and G. Garrigos, Multiobjective Optimization: An Inertial Dynamical Approach to Pareto Optima, preprint, arXiv:1506.02823, 2015]. We prove existence of solutions to this system in finite dimensions and further prove that its bounded solutions converge weakly to weakly Pareto optimal points. In addition, we obtain a convergence rate of order O(t-2) for the function values measured with a merit function. This approach presents a good basis for the development of fast gradient methods for multiobjective optimization. [ABSTRACT FROM AUTHOR]

In this paper, triple quasi-zero stiffness (QZS) passive vibration isolators whose restoring force curve has a three-stage softening effect are proposed. Multi-coupled SD oscillators with three independent geometrical parameters are used as negative stiffness mechanisms to achieve QZS characteristics at the origin and symmetrical positions on both sides of the origin. Isolation performances of different triple QZS isolators are analyzed to show influences of the selection of QZS regions away from the origin on the range of isolation regions. Pareto optimizations of system parameters are carried out to get a larger range of small restoring force regions and small stiffness regions. Isolation performances of two triple QZS isolators are discussed to show the influence of different Pareto optimization solutions through the comparisons with single and double QZS isolators. Results showed that triple QZS isolators have both the advantages of single and double QZS isolators which results in better isolation performances under both small and large excitation amplitudes. An improvement in isolation performances for triple QZS isolators is found with the decrease in average stiffness due to the appearance of two symmetrical QZS regions away from the origin. Larger displacements of QZS regions away from the origin result in better isolation performances when excitation amplitude is large, and triple QZS characteristics are similar to double QZS isolators at this time. Smaller restoring forces of QZS regions away from the origin lead to better isolation performances when excitation amplitude is small, and triple QZS characteristics are similar to single QZS isolators at this moment. Compared with the decrease in average stiffness, the improvement of isolation performances shows a hysteresis phenomenon due to the difference between static and dynamic characteristics. [ABSTRACT FROM AUTHOR]

Despite many advantages of distributed generation (DG), it can have adverse effects on the network protection coordination by raising the short-circuit level of buses. This issue can often be resolved by incorporating fault current limiters (FCLs) that are properly located and sized in the network. We propose an efficient method to simultaneously attain the optimal location and size of DG and FCL units for protection coordination of overcurrent relays and cost reduction in a distribution network. The proposed method involves three stages. First, two separate objective functions are derived, representing the operation times of relays and the network costs associated with the installation of DGs and FCLs and system losses. The Strength Pareto Evolutionary Algorithm 2 (SPEA-2) is then used for finding a Pareto-optimal solution set for the interrelated multiobjective problem at hand. Finally, the k-means clustering method is utilized to group the best solutions. In contrast to the conventional weighted sum (WS) method, the proposed method is more computationally efficient while being capable of treating complex networks with non-convex Pareto front solutions. These features are demonstrated by implementing the proposed approach in the 14-bus IEEE test grid and comparing the results with those obtained using the conventional WS method. [ABSTRACT FROM AUTHOR]

High-level synthesis (HLS) has democratized field programmable gate arrays (FPGAs) by enabling high-level device programmability and rapid microarchitecture customization through the use of directives. Nevertheless, the manual selection of the appropriate directives, i.e., the annotations included in the high-level source code to instruct the synthesis process, is a difficult task for programmers without a hardware background. In this letter, we present CollectiveHLS, an ultrafast knowledge-based HLS design optimization method that automatically extracts the most promising directive configurations and applies them to the original source code. The proposed optimization scheme is a fully data-driven approach for generalized HLS tuning, as it is not based on quality of result models or meta-heuristics. We design, implement, and evaluate our method with more than 100 applications of Machsuite, Rodinia, and GitHub on a ZCU104 FPGA. We achieve an average geometric mean speedup of x14.1 and x10.5 compared to the unoptimized, i.e., without HLS directives and optimized designs, a high design feasibility score, and an average inference latency of 38 ms. [ABSTRACT FROM AUTHOR]

This paper presents an approach for reducing the memory requirements of periodically executed dataflow applications, while minimizing the period when deployed on a many-core target. Often, implementations of dataflow applications suffer from data duplication if identical data has to be processed by multiple actors. In fact, multi-cast (also called fork) actors can produce huge memory overheads when storing and communicating copies of the same data. As a remedy, so-called Multi-Reader Buffers (MRBs) can be utilized to forward identical data to multiple actors in a First In First Out (FIFO) manner while storing each data item only once by sharing. However, using MRBs may increase the achievable period due to contention when accessing the shared data. This paper proposes a novel multi-objective design space exploration approach that selectively replaces multi-cast actors with MRBs and explores actor and FIFO channel mappings to find trade-offs between the objectives of period, memory footprint, and core cost. In distinction to the state-of-the-art, our approach considers (i) memory-size constraints for on-chip memories, (ii) hierarchical memories to implement the buffers, e.g., tile-local memories, (iii) supports heterogeneous many-core platforms, i.e., core-type dependent actor execution times, and (iv) optimizes the buffer placement and overall scheduling to minimize the execution period by proposing a novel combined actor and communication scheduling heuristic for period minimization called Communication-Aware Periodic Scheduling on Heterogeneous Many-core Systems (CAPS-HMS). Our results show that the explored Pareto fronts improve a hypervolume indicator over a reference approach by up to 66 % for small to mid-size applications and 90 % for large applications. Moreover, selectively replacing multi-cast actors with corresponding MRBs proves to be always superior to never or always replacing them. Finally, it is shown that the quality of the explored Pareto fronts does not degrade when replacing the efficient scheduling heuristic CAPS-HMS by an Integer Linear Program (ILP) solver that requires orders of magnitude higher solver times and thus cannot be applied to large scale dataflow network problems.

A key enabling technology for DC collection systems in offshore wind farms is a suitable wind turbine DC/DC converter. However, there is no consensus regarding the topology, design, or operating frequency of this converter. This paper presents an optimization and comparison of four DC/DC converter topologies, including 1-phase, 3-phase, unidirectional, and bidirectional converters. The converters are compared in terms of their reliability, volume, weight and losses at switching frequencies ranging from 500 Hz to 5 kHz. The medium frequency transformer for each converter is designed using multi-objective optimization, and the overall converter volume calculation takes into account the insulation requirements and physical configuration of the components. The results show that if only unidirectional operation is required, the 1-phase single active bridge is the preferred option due to its high reliability, small size and low losses with an optimal operating frequency of up to 2.5 kHz. For bidirectional systems, the 1-phase and 3-phase dual active bridge topologies have a similar efficiency and optimal operating frequency of 1 kHz. Despite its higher volume, the 3-phase version is the preferred option due to its higher reliability and lower device stresses, provided there is enough available space.

Rural areas, with their vast land and abundant resources, are ripe for the development of distributed energy systems. A two-stage dispatch optimization model has been proposed for a virtual power plant (VPP) in this paper, with the aim of maximizing operational revenue, minimizing costs for villagers, and reducing carbon emissions. This model leverages a benefit allocation strategy based on Pareto optimization, ensuring a balanced approach to conflicting objectives such as financial gain, risk management, and environmental impact. The effectiveness of various allocation strategies is evaluated using the Ideal Point method, which assesses options based on their proximity to an ideal outcome across three critical dimensions: risk, benefit, and carbon emission reduction. This method provides an assessment of each strategy’s impact, ensuring that the chosen strategy is holistic. Case study results have shown that the proposed two-stage model, when combined with the Ideal Point-Pareto optimization method, can effectively utilize dispersed resources in rural areas to enhance operational efficiency and reduce carbon emissions from energy consumption processes. Additionally, with a 47% reduction in computational volume compared to traditional scalar and particle swarm optimization algorithms.

Grinding has been extensively applied to meet the urgent need for tight tolerance and high productivity in manufacturing industries. However, grinding parameter settings and process control still depend on skilled workers' engineering experience. The process stability in complicated non-uniform wear can't be guaranteed. Moreover, it is impossible to obtain energy-saved grinding strategies. Intelligent monitoring methods are well-recognized to help conquer present trial–error processing deficiencies. However, discrete manufacturing companies have to face increasing difficulties to identify the monitored big data and make credible decisions directly. A decision-making expert system driven by monitored power data (EconG©) is thus developed. EconG© provides a 4-level database structure to efficiently manage multi-source heterogeneous data. Signal conditioning, peaks-valleys feature exaction, and compression approaches are proposed for reducing the storage volume of real-time monitored data. The data size has been reduced to 6.5% of the source. A mathematical comparison model based on the power feature is embedded to diagnose burns, which has been validated by the 16th and 55th surface grinding results. Mapping relation model from inputs, signals to outputs has been built by the power feature-extended artificial neural network algorithm. Prediction accuracy is improved by introducing adaptive control and dynamic changes in material removal. EconG© breaks a single analysis based on grinding parameters. Energy-saved grinding strategies could be intelligently acquired through the presented Pareto optimization method. In the future, a broader and deeper implementation of EconG© will guild manufacturers to respond quickly to explosive demands on intellectualization, sustainability, and flexibility in the arrived 4th industrial revolution. [ABSTRACT FROM AUTHOR]

In this study, the inductor optimization design is performed by applying the Pareto optimization technique. As environmental problems emerge, the electric vehicle market is expanding, and accordingly, volume reduction and high efficiency of the onboard charger (OBC) are required. An OBC consists of a PFC stage and a DC/DC stage. The inductor is a major component in a converter and affects the volume and efficiency of the entire converter system. However, reducing the volume of the inductor leads to an increase in loss due to an increase in the change in flux density. Therefore, it is important to derive a suitable design for the target between the two parameters in the trade-off of loss and volume. This paper introduces the optimal design algorithm for boosting inductors of PFC converters in terms of volume and loss. Volume and loss are difficult to compare with each other, making it difficult to set weights. Therefore, Pareto optimization was applied which can be selected according to the needs and purposes of the decision-maker, without weighting as an optimization method. Through a series of procedures of applying Pareto optimization to the inductor design, several optimal inductor designs can be derived. At this time, the optimal designs become a set of designs in which the loss does not decrease without an increase in volume, or the volume does not decrease without an increase in loss. A designer can select a design with an appropriate volume and loss that meets the purpose of the design or preference. Therefore, through the proposed method, the inductor can be flexibly designed according to the target of the application. The proposed algorithm is applied to the interleaved totem-pole bridgeless boost PFC converter, to review its effectiveness. As a result, several inductor designs are derived in the search space, and various optimal designs are visualized through the Pareto Frontier. This facilitates comparative analysis of various inductor designs and helps designers select reasonable inductors. The validity was verified by selecting one of the obtained optimal inductor designs and driving the experiment with the resulting inductor. [ABSTRACT FROM AUTHOR]

Green manufacturing has become an important research topic owing to the dominant role of the manufacturing industry in environmental conservation, global energy consumption, and carbon emissions. Job scheduling is an active research area that supports industrial development and transformation as a part of industrial manufacturing management. Scheduling and just-in-time (JIT) production are complementary concepts that can help organizations optimize their production processes and achieve their goals more efficiently. The objective of these concepts is to reduce waste by focusing on the timely delivery of products or services to meet customer demand without holding excess inventory or wasting resources. Early/tardy job scheduling aligns with the primary goals of JIT production. This study jointly considers the early/tardy scheduling problem and carbon-emission optimization. A speed-scaling strategy is applied, where a machine has the ability to process jobs at discrete machining speeds. A heuristic method based on a genetic algorithm is proposed to solve the above problem. The proposed algorithm integrates a normal boundary intersection to reinforce the generation of a Pareto optimal solution. Numerical experiments show that the proposed approach provides an optimal and satisfactory Pareto solution within a relatively short computational time. [ABSTRACT FROM AUTHOR]

Computed tomography (CT)-guided thermal ablation is an emerging treatment method for lung tumors. Ablation needle path planning in preoperative diagnosis is of critical importance. In this work, we proposed an automatic needle path-planning method for thermal lung tumor ablation. First, based on the improved cube mapping algorithm, binary classification was performed on the surface of the bounding box of the patient's CT image to obtain a feasible puncture area that satisfied all hard constraints. Then, for different clinical soft constraint conditions, corresponding grayscale constraint maps were generated, respectively, and the multi-objective optimization problem was solved by combining Pareto optimization and weighted product algorithms. Finally, several optimal puncture paths were planned within the feasible puncture area obtained for the clinicians to choose. The proposed method was evaluated with 18 tumors of varying sizes (482.79 mm3 to 9313.81 mm3) and the automatically planned paths were compared and evaluated with manually planned puncture paths by two clinicians. The results showed that over 82% of the paths (74 of 90) were considered reasonable, with clinician A finding the automated planning path superior in 7 of 18 cases, and clinician B in 9 cases. Additionally, the time efficiency of the algorithm (35 s) was much higher than that of manual planning. The proposed method is expected to aid clinicians in preoperative path planning for thermal ablation of lung tumors. By providing a valuable reference for the puncture path during preoperative diagnosis, it may reduce the clinicians' workload and enhance the objectivity and rationality of the planning process, which in turn improves the effectiveness of treatment. [ABSTRACT FROM AUTHOR]

Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum to illustrate how AIDD generates novel sets of molecules. [ABSTRACT FROM AUTHOR]

Electric retail rate design is relevant to utilities, customers, and regulators as retail rates impact the utility's revenue as well as the customers' electricity bills. In California, regulators approve rate proposals by privately owned vertical integrated utilities. Approval, however, is subject to compliance with multiple, potentially conflicting objectives such as economic or environmental objectives. Additionally, retail rates are price signals that affect how customers use electricity services. When utility customers change their usage, they also impact the ratemaking objectives to which rates have been designed. This suggests a feedback loop, which is particularly pronounced with prosumers, as they can systematically optimize their interactions with the electricity system. Prevalent ratemaking methods may not deliver retail rates that are optimal for multiple objectives when customers are prosumers. We propose a novel ratemaking method that formalizes the problem of designing retail rates as a multi-criteria optimization problem and accounts for prosumer reactions through a simulation-based optimization approach. Through a fictive case study, we found that the resulting Pareto frontiers are useful in recognizing and balancing tradeoffs among conflicting ratemaking objectives. Additionally, our results indicate that prevailing retail rates in California are not Pareto optimal.

One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use particle swarm optimization (PSO) to expand the FR-MOS multi-objective scheduling algorithm by using fuzzy resource management to maximize variety and obtain optimal Pareto convergence. The competing objectives include reliability, cost, utilization of resources, risk probability, and time makespan. Most of the previous studies provide numerous symmetry or equivalent solutions as trade-offs for different objectives, and selecting the optimum solution remains an issue. We propose a novel decision-making strategy named minimum weight optimization (MWO). Multi-objective algorithms use this method to select a set of permutations that provide the best trade-off between competing objectives. MWO is a suitable choice for attaining all optimal solutions, where both the needs of consumers and the interests of service providers are taken into consideration. (MWO) aims to find the best solution by comparing alternative weights, narrowing the search for an optimal solution through iterative refinement. We compare our proposed method to five distinct decision-making procedures using common scientific workflows with competing objectives: Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I, linear normalization II, and weighted aggregated sum product assessment (WASPAS). MWO outperforms these strategies according to the results of this study. [ABSTRACT FROM AUTHOR]