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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 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.

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]

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]

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]

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]

The threat posed by Alzheimer's disease (AD) to human health has grown significantly. However, the precise diagnosis and classification of AD stages remain a challenge. Neuroimaging methods such as structural magnetic resonance imaging (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to diagnose and categorize AD. However, feature selection approaches that are frequently used to extract additional data from multimodal imaging are prone to errors. This paper suggests using a static pulse-coupled neural network and a Laplacian pyramid to combine sMRI and FDG-PET data. After that, the fused images are used to train the Mobile Vision Transformer (MViT), optimized with Pareto-Optimal Quantum Dynamic Optimization for Neural Architecture Search, while the fused images are augmented to avoid overfitting and then classify unfused MRI and FDG-PET images obtained from the AD Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets into various stages of AD. The architectural hyperparameters of MViT are optimized using Quantum Dynamic Optimization, which ensures a Pareto-optimal solution. The Peak Signal-to-Noise Ratio (PSNR), the Mean Squared Error (MSE), and the Structured Similarity Indexing Method (SSIM) are used to measure the quality of the fused image. We found that the fused image was consistent in all metrics, having 0.64 SIMM, 35.60 PSNR, and 0.21 MSE for the FDG-PET image. In the classification of AD vs. cognitive normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, the precision of the proposed method is 94.73%, 92.98% and 89.36%, respectively. The sensitivity is 90. 70%, 90. 70%, and 90. 91% while the specificity is 100%, 100%, and 85. 71%, respectively, in the ADNI MRI test data.

Abstract 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.

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.

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.

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.

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.

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]

This paper presents the placement and sizing of energy hubs (EHs) in electricity, gas, and heating networks. EH is a coordinator framework for various power sources, storage devices, and responsive loads. For simultaneous modeling of economic, operation, reliability, and flexibility indices, the proposed scheme is expressed as a three-objective optimization in the form of Pareto optimization based on the sum of weighted functions. The objective functions of this problem respectively minimize the planning cost of EHs (equal to the total cost of construction of hubs and their expected operating cost), the expected energy loss of the mentioned networks, and the expected energy not-supplied (EENS) of these networks in the case of an N − 1 event. The problem is constrained by power flow equations and operation and reliability constraints of these network together with the EH planning and operation model, and flexibility constraints of the EHs. Then, to achieve unique optimal solution in the shortest possible time, a linear approximation model is extracted for the proposed scheme. Moreover, scenario-based stochastic programming (SBSP) is employed to model uncertainties of load, energy cost, renewable power, and accessibility of the mentioned network equipment. Finally, the obtained numerical results indicate the capability of the proposed scheme in enhancing the economic and flexibility situation of EHs and improving the reliability and operation status of energy networks along with achieving optimal planning and operation for EHs.

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]

The prior domain knowledge, i.e., the regularity property of continuous multiobjective optimization problems (MOPs), could be learned to guide the search for evolutionary multiobjective optimization. This paper proposes a learning-to-guide strategy (LGS) for assisting the search for multiobjective optimization algorithms in dealing with MOPs. The main idea behind LGS is to capture the regularity via learning techniques to guide the evolutionary search to generate promising offspring solutions. To achieve this, a generative model called the generative topographic mapping (GTM) is adopted to capture the manifold distribution of a population. A set of regular grid points in the latent space are mapped into the decision space within some manifold structures to guide the search for mating with some parents for offspring generation. Following this idea, three alternative LGS-based generation operators are developed and investigated, which combine the local and global information in the offspring generation. To learn the regularity more efficiently in an algorithm, the proposed LGS is embedded in an efficient evolutionary algorithm (called LGSEA). The LGSEA includes an incremental training procedure aimed at reducing the computational cost of GTM training by reusing the built GTM model. The developed algorithm is compared with some newly developed or classical learning-based algorithms on several benchmark problems. The results demonstrate the advantages of LGSEA over other approaches, showcasing its potential for solving complex MOPs. [ABSTRACT FROM AUTHOR]

This paper develops a multi-objective optimization model for national high-speed railway network planning. Three objectives are proposed from the macro, meso, and micro perspectives, and these three objectives are considered simultaneously. Using real data, case studies are conducted to optimize China's "four east–west and four north–south railway lines" network, which includes Beijing, Shanghai, Wuhan, Guangzhou, and Zhengzhou as main hubs. The results show that, on the one hand, the optimization model reduces the overlap between long-distance passengers and short-distance passengers on the high-speed railway line, facilitates the travel of passengers, and improves the line service capability; on the other hand, optimization of the network shortens the travel time of the passengers, reduces the cost of the railway, and improves the operation efficiency of the high-speed railway line network. The results show that the total travel time of all high-speed railway passengers in the optimization model is reduced by 18.4%, while the benefit rate of the operator increased by 21.99%. [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.

Simple Summary: This paper introduces a new method for cleaning impaired speech by combining Pareto-optimized deep learning with Non-negative Matrix Factorization (NMF). The approach effectively reduces noise in impaired speech while preserving the desired speech quality. The method involves calculating the spectrogram of a noisy voice clip, determining a noise threshold, computing a noise-to-signal mask, and smoothing it to avoid abrupt transitions. Using a Pareto-optimized NMF, the modified spectrogram is decomposed into basis functions and weights, allowing for reconstruction of the clean speech spectrogram. The final result is a noise-reduced waveform achieved by inverting the clean speech spectrogram. Experimental results validate the method's effectiveness in cleaning alaryngeal speech signals, indicating its potential for real-world applications. The problem of cleaning impaired speech is crucial for various applications such as speech recognition, telecommunication, and assistive technologies. In this paper, we propose a novel approach that combines Pareto-optimized deep learning with non-negative matrix factorization (NMF) to effectively reduce noise in impaired speech signals while preserving the quality of the desired speech. Our method begins by calculating the spectrogram of a noisy voice clip and extracting frequency statistics. A threshold is then determined based on the desired noise sensitivity, and a noise-to-signal mask is computed. This mask is smoothed to avoid abrupt transitions in noise levels, and the modified spectrogram is obtained by applying the smoothed mask to the signal spectrogram. We then employ a Pareto-optimized NMF to decompose the modified spectrogram into basis functions and corresponding weights, which are used to reconstruct the clean speech spectrogram. The final noise-reduced waveform is obtained by inverting the clean speech spectrogram. Our proposed method achieves a balance between various objectives, such as noise suppression, speech quality preservation, and computational efficiency, by leveraging Pareto optimization in the deep learning model. The experimental results demonstrate the effectiveness of our approach in cleaning alaryngeal speech signals, making it a promising solution for various real-world applications. [ABSTRACT FROM AUTHOR]

Multi-objective task graph scheduling is a well-known NP-hard problem that plays a significant role in heterogeneous distributed systems. The solution to the problem is expected to optimize all scheduling objectives. Pretty large state-of-the-art algorithms exist in the literature that mostly apply different metaheuristics for solving the problem. This study proposes a new hybrid algorithm comprising an improved multi-objective differential evolution algorithm (DE) and Pareto-front neighborhood search to solve the problem. The novelty of the proposed hybrid method is achieved by improving DE and hybridizing it with the neighborhood search method. The proposed method improves the performance of differential evolution by applying appropriate solution representation as well as effective selection, crossover, and mutation operators. Likewise, the neighborhood search algorithm is applied to improve the extracted Pareto-front and speed up the evolution process. The effectiveness and performance of the developed method are assessed over well-known test problems collected from the related literature. Meanwhile, the values of spacing and hyper-volume metrics are calculated. Moreover, the Wilcoxon signed method is applied to carry out pairwise statistical tests over the obtained results. The obtained results for the makespan, reliability, and flow-time of 50, 18, and 41, respectively, by the proposed hybrid algorithm in the study confirmed that the developed algorithm outperforms all proposed methods considering the performance and quality of objective values. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurological condition that gradually weakens the brain and impairs cognition and memory. Multimodal imaging techniques have become increasingly important in the diagnosis of AD because they can help monitor disease progression over time by providing a more complete picture of the changes in the brain that occur over time in AD. Medical image fusion is crucial in that it combines data from various image modalities into a single, better-understood output. The present study explores the feasibility of employing Pareto optimized deep learning methodologies to integrate Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images through the utilization of pre-existing models, namely the Visual Geometry Group (VGG) 11, VGG16, and VGG19 architectures. Morphological operations are carried out on MRI and PET images using Analyze 14.0 software and after which PET images are manipulated for the desired angle of alignment with MRI image using GNU Image Manipulation Program (GIMP). To enhance the network's performance, transposed convolution layer is incorporated into the previously extracted feature maps before image fusion. This process generates feature maps and fusion weights that facilitate the fusion process. This investigation concerns the assessment of the efficacy of three VGG models in capturing significant features from the MRI and PET data. The hyperparameters of the models are tuned using Pareto optimization. The models' performance is evaluated on the ADNI dataset utilizing the Structure Similarity Index Method (SSIM), Peak Signal-to-Noise Ratio (PSNR), Mean-Square Error (MSE), and Entropy (E). Experimental results show that VGG19 outperforms VGG16 and VGG11 with an average of 0.668, 0.802, and 0.664 SSIM for CN, AD, and MCI stages from ADNI (MRI modality) respectively. Likewise, an average of 0.669, 0.815, and 0.660 SSIM for CN, AD, and MCI stages from ADNI (PET modality) respectively. [ABSTRACT FROM AUTHOR]

In order to solve the incalculability caused by the issue of inconsistent objective functions in multi-agent deep reinforcement learning, the concept of Nash equilibrium is introduced. However, a Marko game may have multiple equilibriums, how to filter out a stable and optimal one is worth studying. Besides solution concept, how to keep the balance between exploration and exploitation is another key issue in reinforcement learning. On basis of the methods, which can converge to Nash equilibrium, this paper makes improvement through Pareto optimization. In order to alleviate the problem of over fitting caused by Pareto optimization and non-convergence caused by strategy change, we use stratified sampling in place of random sampling as assistance. What's more, our methods are trained through fictitious self-play to make full of self-learning experiences. By analyzing the experiment carried out on MAgent platform, the proposed methods are not only far better than traditional methods, but also reaching or even surpassing the state of art MADRL methods. [ABSTRACT FROM AUTHOR]

This paper is poised to inform educators, policy makers and software developers about the untapped potential of PWAs in creating engaging, effective, and personalized learning experiences in the field of programming education. We aim to address a significant gap in the current understanding of the potential advantages and underutilisation of Progressive Web Applications (PWAs) within the education sector, specifically for programming education. Despite the evident lack of recognition of PWAs in this arena, we present an innovative approach through the Framework for Gamification in Programming Education (FGPE). This framework takes advantage of the ubiquity and ease of use of PWAs, integrating it with a Pareto optimized gamified programming exercise selection model ensuring personalized adaptive learning experiences by dynamically adjusting the complexity, content, and feedback of gamified exercises in response to the learners' ongoing progress and performance. This study examines the mobile user experience of the FGPE PLE in different countries, namely Poland and Lithuania, providing novel insights into its applicability and efficiency. Our results demonstrate that combining advanced adaptive algorithms with the convenience of mobile technology has the potential to revolutionize programming education. The FGPE+ course group outperformed the Moodle group in terms of the average perceived knowledge (M = 4.11, SD = 0.51). [ABSTRACT FROM AUTHOR]

This paper studies the problem of scheduling \begin{document}$ n $\end{document} jobs with equal processing times on \begin{document}$ m $\end{document} uniform machines to optimize two criteria simultaneously. The main contribution is an \begin{document}$ O(n\log m+n^3) $\end{document}-time algorithm for two general min-max criteria, improving the previous \begin{document}$ O(n\log m+n^4) $\end{document} time complexity. For a particular min-sum criterion (total weighted completion time or total tardiness) in combination with a general min-max criterion, \begin{document}$ O(n\log m+n^3) $\end{document}-time algorithms are also obtained, improving the previous \begin{document}$ O(n\log m+n^3\log n) $\end{document} time complexity. The algorithms can produce all Pareto optimal points together with the corresponding schedules. [ABSTRACT FROM AUTHOR]