Classical Benchmark Research Articles

Comparative analysis of accuracy and computational complexity across 21 swarm intelligence algorithms

Nonlinear, complex optimization problems are prevalent in many scientific and engineering fields. Traditional algorithms often struggle with these problems due to their high dimensionality and intricate nature, making them time-consuming. Many researchers have proposed new metaheuristic algorithms inspired by biological behaviors in nature, which comparatively show higher performance and accuracy than traditional optimization algorithms. Nature-inspired algorithms, particularly those based on swarm intelligence, offer adaptable and efficient solutions to these challenges. In recent years, swarm intelligence algorithms have made significant advancements. Classical and CEC benchmark suits are immersively useful for studying the performance of optimization algorithms. According to our literature survey, we identified that many algorithms were evaluated based on accuracy. Currently, swarm intelligence algorithms are used in many applications, and efficiency and computational complexity need to be evaluated. A broad-level study of the computational complexity and accuracy of popular swarm intelligence algorithms has not been done recently. Therefore this study we comprehensively evaluate and compare 21 bio-inspired swarm intelligence algorithms on eight non-separable unimodal, eight separable unimodal, five non-separable multimodal, seven separable multimodal functions, and two CEC 2018 many objective functions. We study the structure and mathematical model of the selected algorithms. Then we categorized selected algorithms into six different behavioral groups. We calculated the root mean square error between expected and actual values. Then we performed an RMSE cross-validation statistical test to understand how accurately an algorithm resolves an average problem. We found that Artificial Lizard Search Optimization (ALSO) is the most prominent algorithm in accuracy and efficiency. Besides that, Cat Swarm Optimization (CSO), Squirrel Search Algorithm (SSA), and Chimp Optimization Algorithm (CHOA-B) are also considered more universal algorithms. The Squirrel Search Algorithm (SSA) is ALSO’s second-best algorithm in time complexity. Wasp Swarm Algorithm (WSO), and Bat-Inspired Algorithm (BA) presented the lowest time complexity. Finally, several important issues and research directions are discussed.

Automatic Detection and Classification of Aurora in THEMIS All‐Sky Images

AbstractWe report a novel machine‐learning algorithm for automatically detecting and classifying aurora in all–sky images (ASI) that is largely trained without requiring ground–truth labels. By including a small number of labeled images, we are able to automatically label all of the approximately 700 million images in the Time History of Events and Macroscale Interactions during Substorms (THEMIS) ASI data set from 2008 to 2022. We use a two–stage approach. In the first stage, we adapt the Simple framework for Contrastive Learning of Representations (SimCLR) algorithm to learn latent representations of THEMIS all–sky images. We then finetune a classifier network on the latent representations our model learns of the manually labeled Oslo aurora THEMIS (OATH) data set. We demonstrate that this two–stage approach achieves excellent classification results on data for which there is no current ML classification benchmark. The outcome of this work will facilitate efficient information retrieval for researchers interested in specific categories of aurora and will enable large scale statistical studies and machine learning analyses of THEMIS all–sky images that have not previously been possible. To demonstrate possible ways to utilize this database, we performed a statistical analysis of the occurrence rates of auroral labels with respect to solar wind parameters, interplanetary magnetic field vector, and geomagnetic indices. We further investigate the occurrence rates of auroral phenomena in the annotated data set and their geoeffectiveness by utilizing the co–located THEMIS ground magnetometer data set.

ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training.

Negative flips are errors introduced in a classification system when a legacy model is updated. Existing methods to reduce the negative flip rate (NFR) either do so at the expense of overall accuracy by forcing a new model to imitate the old models, or use ensembles, which multiply inference cost prohibitively. We analyze the role of ensembles in reducing NFR and observe that they remove negative flips that are typically not close to the decision boundary, but often exhibit large deviations in the distance among their logits. Based on the observation, we present a method, called Ensemble Logit Difference Inhibition (ELODI), to train a classification system that achieves paragon performance in both error rate and NFR, at the inference cost of a single model. The method distills a homogeneous ensemble to a single student model which is used to update the classification system. ELODI also introduces a generalized distillation objective, Logit Difference Inhibition (LDI), which only penalizes the logit difference of a subset of classes with the highest logit values. On multiple image classification benchmarks, model updates with ELODI demonstrate superior accuracy retention and NFR reduction.

Tuning Vision-Language Models With Multiple Prototypes Clustering.

Benefiting from advances in large-scale pre-training, foundation models, have demonstrated remarkable capability in the fields of natural language processing, computer vision, among others. However, to achieve expert-level performance in specific applications, such models often need to be fine-tuned with domain-specific knowledge. In this paper, we focus on enabling vision-language models to unleash more potential for visual understanding tasks under few-shot tuning. Specifically, we propose a novel adapter, dubbed as lusterAdapter, which is based on trainable multiple prototypes clustering algorithm, for tuning the CLIP model. It can not only alleviate the concern of catastrophic forgetting of foundation models by introducing anchors to inherit common knowledge, but also improve the utilization efficiency of few annotated samples via bringing in clustering and domain priors, thereby improving the performance of few-shot tuning. We have conducted extensive experiments on 11 common classification benchmarks. The results show our method significantly surpasses the original CLIP and achieves state-of-the-art (SOTA) performance under all benchmarks and settings. For example, under the 16-shot setting, our method exhibits a remarkable improvement over the original CLIP by 19.6%, and also surpasses TIP-Adapter and GraphAdapter by 2.7% and 2.2%, respectively, in terms of average accuracy across the 11 benchmarks.

A variationally-consistent hybrid equilibrium element formulation for linear poroelasticity

A poroelastic medium is defined as a continuous system in which the mechanical response arises from the interaction between a deformable elastic solid skeleton and a pressurised fluid, fully saturating the interconnected porous network. The coupled theory of Poromechanics is effectively employed to solve a broad class of problems spanning various fields, ranging from its original application in Geomechanics to addressing contemporary challenges in tissue Biomechanics. Besides the seminal theory introduced by Biot, several variational multi-field principles have been proposed, leading to various finite element formulations. Finite Element approaches are mostly based on a two-field discretisation involving the coupled solid displacement and interstitial fluid pressure as primary variables. However, this choice of the two-primary variables leads to a saddle-point problem, posing a challenge in achieving accurate solutions and thus making the use of stabilisation methods rather mandatory. This study proposes a novel variationally consistent Hybrid Equilibrium Element formulation, based on the complementary strain energy function. A two-field minimisation principle is formulated, with total Cauchy stress and interstitial fluid pressure serving as primary variables. The proposed variational principle provides a variationally consistent framework for designing inherently self-equilibrated Hybrid Equilibrium Elements, where the solid displacement field emerges as a Lagrangian variable to enforce a co-diffusivity constraint at element sides. The fulfilment of the inf-sup condition, typically challenging in developing stable and computationally efficient finite element formulations, is no longer required, a minimisation principle having been adopted, rather than the classical saddle-point principle. The proposed Hybrid Equilibrium Element approach offers a statically admissible solution even in the proximity of singularities or high space gradient stress distribution, providing a more comprehensive assessment of stress distributions and concentrations. The convexity of the new variational principle has been established, ensuring solution uniqueness. The formulation is based on a 2-D triangular Hybrid Equilibrium Element employed to solve plane strain problems. The element has been implemented in the Open source Finite Element Analysis Program (FEAP) and its performance evaluated against classical benchmark problems for poroelasticity, with the emphasis on stress accuracy. The numerical results obtained for the quasi-static analysis of poroelastic systems show the advantages of the proposed approach, in which accurate stress distributions are displayed, even adopting quite coarse meshes. Remarkably, in the proposed approach no stabilisation technique is required.

Modelling unstable crack propagation in concrete by finite element method with continuous nodal stress

Cracks in concrete will propagate unstably due to excessive shear stress, interactions at the rock-concrete interface, or sudden energy release, significantly compromising structural bearing capacity. To investigate this phenomenon, a nonlinear numerical method for modelling mixed-mode I-II crack propagation has been developed. This approach integrates dynamic equilibrium with a fictitious crack model and an initiation fracture toughness criterion. Key innovations include the incorporation of a finite element method with continuous nodal stress to enhance calculation accuracy, the utilization of kinetic energy to compensate for abrupt losses of strain energy during crack propagation, and the consideration of the deformation of the distributive beam and its interactions with specimens and supports. It was validated by modelling classic benchmarks for the dynamic initiation and propagation of brittle materials under mode I and mixed-mode I-II loading, and applied to analyse unstable crack propagation in concrete for four-point shear (FPS) beam specimens under various ratios of mode I and II stress intensity factors (KI/KII = 0 to 5.32) and loading rates (2 × 10-7 m/s to 2 × 10-3 m/s). Results indicated that the method effectively captures unstable crack propagation, with load-crack mouth shear displacement (CMSD) curves and crack propagation trajectories closely matching the experimental data. Furthermore, it was observed that when the elastic strain energy within the concrete beam exceeds the residual energy stored in the extended cracks, the crack transitions from stability to instability; conversely, if the elastic strain energy is less than or approximately equal to the residual energy, the crack decelerates and returns to stability. Additionally, parametric analyses reveal that lower distributive beam stiffness, shorter preset crack lengths, reduced concrete fracture energy, and a less robust cohesive force–displacement curve increase the likelihood of unstable crack propagation in concrete.

Scattering wave packets of hadrons in gauge theories: Preparation on a quantum computer

Quantum simulation holds promise of enabling a complete description of high-energy scattering processes rooted in gauge theories of the Standard Model. A first step in such simulations is preparation of interacting hadronic wave packets. To create the wave packets, one typically resorts to adiabatic evolution to bridge between wave packets in the free theory and those in the interacting theory, rendering the simulation resource intensive. In this work, we construct a wave-packet creation operator directly in the interacting theory to circumvent adiabatic evolution, taking advantage of resource-efficient schemes for ground-state preparation, such as variational quantum eigensolvers. By means of an ansatz for bound mesonic excitations in confining gauge theories, which is subsequently optimized using classical or quantum methods, we show that interacting mesonic wave packets can be created efficiently and accurately using digital quantum algorithms that we develop. Specifically, we obtain high-fidelity mesonic wave packets in the Z2 and U(1) lattice gauge theories coupled to fermionic matter in 1+1 dimensions. Our method is applicable to both perturbative and non-perturbative regimes of couplings. The wave-packet creation circuit for the case of the Z2 lattice gauge theory is built and implemented on the Quantinuum H1-1 trapped-ion quantum computer using 13 qubits and up to 308 entangling gates. The fidelities agree well with classical benchmark calculations after employing a simple symmetry-based noise-mitigation technique. This work serves as a step toward quantum computing scattering processes in quantum chromodynamics.

Euler Kernel Mapping for Hyperspectral Image Clustering via Self-Paced Learning

Clustering, as a classical unsupervised artificial intelligence technology, is commonly employed for hyperspectral image clustering tasks. However, most existing clustering methods designed for remote sensing tasks aim to solve a non-convex objective function, which can be optimized iteratively, beginning with random initializations. Consequently, during the learning phase of the clustering model, it may easily fall into bad local optimal solutions and finally hurt the clustering performance. Additionally, prevailing approaches often exhibit limitations in capturing the intricate structures inherent in hyperspectral images and are very sensitive to noise and outliers that widely exist in remote sensing data. To address these issues, we proposed a novel Euler kernel mapping for hyperspectral image clustering via self-paced learning (EKM-SPL). EKM-SPL first employs self-paced learning to learn the clustering model in a meaningful order by progressing samples from easy to complex, which can help to remove bad local optimal solutions. Secondly, a probabilistic soft weighting scheme is employed to measure complexity across the data sample, which makes the optimization process more reasonable. Thirdly, in order to more accurately characterize the intricate structure of hyperspectral images, Euler kernel mapping is used to convert the original data into a reproduced kernel Hilbert space, where the nonlinearly inseparable clusters may become linearly separable. Moreover, we innovatively integrate the coordinate descent technique into the optimization algorithm to circumvent the computational inefficiencies and information loss typically associated with conventional kernel methods. Extensive experiments conducted on classic benchmark hyperspectral image datasets illustrate the effectiveness and superiority of our proposed model.

Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking AdaBoost Frameworks

This research addresses the persistent global challenge of poverty, with a specific focus on Indonesia, a nation with a population exceeding 270 million. The primary objective is to enhance the precision and reliability of poverty classification using advanced machine learning technologies. We employed a combination of Bidirectional Gated Recurrent Unit (BiGRU), Backpropagation Neural Network (BPNN), and stacking techniques with AdaBoost to develop an innovative classification model. The methodology involved training each technique separately and then integrating them into a stacked model to leverage their individual strengths. The results were promising, demonstrating a substantial improvement in model performance with precision, recall, and F1 scores reaching as high as 0.98, and an overall accuracy of 98.06%. The use of visual analytics, including pie charts and bar graphs, provided a comprehensive distribution analysis of poverty levels, confirming the balanced nature of the dataset. These findings underscore the critical role of machine learning in formulating effective policies for poverty alleviation and suggest that integrating multiple machine learning algorithm can significantly enhance decision-making processes. The novelty of this research lies in the successful application of a stacked machine learning model combining BiGRU, BPNN, and AdaBoost, which establishes a new benchmark for poverty classification in large-scale social datasets. This study not only contributes to the academic discourse but also paves the way for practical implementations that can drive inclusive and sustainable development.

Multi‐Deep Learning Approach With Transfer Learning for 7‐Stages Diabetic Retinopathy Classification

ABSTRACTProposed novel investigation focused on leveraging an innovative diabetic retinopathy (DR) dataset comprising seven severity stages, an approach not previously examined. By capitalizing on this unique resource, this study′s findings set a new benchmark for DR classification, highlighting the transformative potential of incorporating advanced data into AI models. This study developed a Vgg16 transfer learning model and gauged its performance against established algorithms including Vgg‐19, AlexNet, and SqueezeNet. Remarkably, our results achieved accuracy rates of 96.95, 96.75, 96.09, and 92.96, respectively, emphasizing the contribution of our work. We strongly emphasized comprehensive severity rating, yielding perfect and impressive F1‐scores of 1.00 for “mild NPDR” and 97.00 for “no DR signs.” The Vgg16‐TL model consistently outperformed other models across all severity levels, reinforcing the value of our discoveries. Our deep learning training process, carefully selecting a learning rate of 1e‐05, allowed continuous refinements in training and validation accuracy. Beyond metrics, our investigation underscores the vital clinical importance of precise DR classification for preventing vision loss. This study conclusively establishes deep learning as a powerful transformative tool for developing effective DR algorithms with the potential to improve patient outcomes and advance ophthalmology standards.

A metaheuristic approach to model the effect of temperature on urban electricity need utilizing XGBoost and modified boxing match algorithm

A nonlinear and complicated phenomenon of the relationship between urban electricity needs and temperature influences the operation and planning of power systems. Ensuring the effectiveness and reliability of the power supply requires precise prediction of electricity needs in various consumption scenarios. In this study, an innovative method is used to deal with the complex relationship between urban electricity consumption and temperature changes. In this paper, the initial contributions focus on the integration of two powerful techniques: the Modified Boxing Match (MBM) algorithm and the XGBoost model, which is a complex convolutional neural network. The integration of these approaches facilitates the extraction of advanced features and allows nonlinear relationships between electricity consumption and temperature data. One of the notable aspects of this work is the introduction of a new leapfrog rule in the MBM algorithm, which significantly improves local exploration and accelerates convergence, leading to more accurate power demand forecasts. The XGBoost model’s hyperparameters are optimized using MBM to achieve the best possible solution. The proposed MBM algorithm was tested on 23 well-known classical benchmark function methods, and the results indicate that the recommended technique is more accurate and robust. As a dependable and efficient tool for modeling and predicting temperature–electricity needs, the suggested method can be utilized.

Structure-Aware DropEdge Toward Deep Graph Convolutional Networks.

It has been discovered that graph convolutional networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why deep GCNs fail lies in oversmoothing, which isolates the network output from the input with the increase of network depth, weakening expressivity and trainability. In this article, we start by investigating refined measures upon DropEdge-an existing simple yet effective technique to relieve oversmoothing. We term our method as DropEdge++ for its two structure-aware samplers in contrast to DropEdge: layer-dependent (LD) sampler and feature-dependent (FD) sampler. Regarding the LD sampler, we interestingly find that increasingly sampling edges from the bottom layer yields superior performance than the decreasing counterpart as well as DropEdge. We theoretically reveal this phenomenon with mean-edge-number (MEN), a metric closely related to oversmoothing. For the FD sampler, we associate the edge sampling probability with the feature similarity of node pairs and prove that it further correlates the convergence subspace of the output layer with the input features. Extensive experiments on several node classification benchmarks, including both full- and semi-supervised tasks, illustrate the efficacy of DropEdge++ and its compatibility with a variety of backbones by achieving generally better performance over DropEdge and the no-drop version.

Near-Infrared Spectroscopy for Neonatal Sleep Classification.

Sleep, notably active sleep (AS) and quiet sleep (QS), plays a pivotal role in the brain development and gradual maturation of (pre) term infants. Monitoring their sleep patterns is imperative, as it can serve as a tool in promoting neurological maturation and well-being, particularly important in preterm infants who are at an increased risk of immature brain development. An accurate classification of neonatal sleep states can contribute to optimizing treatments for high-risk infants, with respiratory rate (RR) and heart rate (HR) serving as key components in sleep assessment systems for neonates. Recent studies have demonstrated the feasibility of extracting both RR and HR using near-infrared spectroscopy (NIRS) in neonates. This study introduces a comprehensive sleep classification approach leveraging high-frequency NIRS signals recorded at a sampling rate of 100 Hz from a cohort of nine preterm infants admitted to a neonatal intensive care unit. Eight distinct features were extracted from the raw NIRS signals, including HR, RR, motion-related parameters, and proxies for neural activity. These features served as inputs for a deep convolutional neural network (CNN) model designed for the classification of AS and QS sleep states. The performance of the proposed CNN model was evaluated using two cross-validation approaches: ten-fold cross-validation of data pooling and five-fold cross-validation, where each fold contains two independently recorded NIRS data. The accuracy, balanced accuracy, F1-score, Kappa, and AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic) were employed to assess the classifier performance. In addition, comparative analyses against six benchmark classifiers, comprising K-Nearest Neighbors, Naive Bayes, Support Vector Machines, Random Forest (RF), AdaBoost, and XGBoost (XGB), were conducted. Our results reveal the CNN model's superior performance, achieving an average accuracy of 88%, a balanced accuracy of 94%, an F1-score of 91%, Kappa of 95%, and an AUC-ROC of 96% in data pooling cross-validation. Furthermore, in both cross-validation methods, RF and XGB demonstrated accuracy levels closely comparable to the CNN classifier. These findings underscore the feasibility of leveraging high-frequency NIRS data, coupled with NIRS-based HR and RR extraction, for assessing sleep states in neonates, even in an intensive care setting. The user-friendliness, portability, and reduced sensor complexity of the approach suggest its potential applications in various less-demanding settings. This research thus presents a promising avenue for advancing neonatal sleep assessment and its implications for infant health and development.

Noise-Tolerant Hybrid Prototypical Learning with Noisy Web Data

We focus on the challenging problem of learning an unbiased classifier from a large number of potentially relevant but noisily labeled web images given only a few clean labeled images. This problem is particularly practical because it reduces the expensive annotation costs by utilizing freely accessible web images with noisy labels. Typically, prototypes are representative images or features used to classify or identify other images. However, in the few clean and many noisy scenarios, the class prototype can be severely biased due to the presence of irrelevant noisy images. The resulting prototypes are less compact and discriminative, as previous methods do not take into account the diverse range of images in the noisy web image collections. On the other hand, the relation modeling between noisy and clean images is not learned for the class prototype generation in an end-to-end manner, which results in a suboptimal class prototype. In this article, we introduce a similarity maximization loss named SimNoiPro. Our SimNoiPro first generates noise-tolerant hybrid prototypes composed of clean and noise-tolerant prototypes and then pulls them closer to each other. Our approach considers the diversity of noisy images by explicit division and overcomes the optimization discrepancy issue. This enables better relation modeling between clean and noisy images and helps extract judicious information from the noisy image set. The evaluation results on two extended few-shot classification benchmarks confirm that our SimNoiPro outperforms prior methods in measuring image relations and cleaning noisy data.

Interpreting pretext tasks for active learning: a reinforcement learning approach

As the amount of labeled data increases, the performance of deep neural networks tends to improve. However, annotating a large volume of data can be expensive. Active learning addresses this challenge by selectively annotating unlabeled data. There have been recent attempts to incorporate self-supervised learning into active learning, but there are issues in utilizing the results of self-supervised learning, i.e., it is uncertain how these should be interpreted in the context of active learning. To address this issue, we propose a multi-armed bandit approach to handle the information provided by self-supervised learning in active learning. Furthermore, we devise a data sampling process so that reinforcement learning can be effectively performed. We evaluate the proposed method on various image classification benchmarks, including CIFAR-10, CIFAR-100, Caltech-101, SVHN, and ImageNet, where the proposed method significantly improves previous approaches.

Simulation of the Neutral Atmospheric Flow Using Multiscale Modeling: Comparative Studies for SimpleFoam and Fluent Solver

The reproduced planetary boundary layer (PBL) wind is commonly applied in downscaled simulations using commercial CFD codes with Reynolds-averaged Navier–Stokes (RANS) turbulence modeling. When using the turbulent inlets calculated by numerical weather prediction models (NWP), adjustments of the turbulence eddy viscosity closures and wall function formulations are concerned with maintaining the fully developed wind profiles specified at the inlet of CFD domains. The impact of these related configurations is worth discussing in engineering applications, especially when commercial codes restrict the internal modifications. This study evaluates the numerical performances of open-source OpenFOAM 2.3.0 and commercial Fluent 17.2 codes as supplementary scientific comparisons. This contribution focuses on the modified turbulence closures to incorporate turbulent profiles produced from mesoscale PBL parameterizations and the modified wall treatments relating to the roughness length. The near-ground flow features are evaluated by selecting the flat terrains and the classical Askervein benchmark case. The improvement in near-ground wind flow under the downscaled framework shows satisfactory performance in the open-source CFD platform. This contributes to engineers realizing the micro-siting of wind turbines and quantifying terrain-induced speed-up phenomena under the scope of wind-resistant design.

Jacobian vs. disturbance method for UMATs in ABAQUS: An application to isotropic damage mechanics

This paper compares the computational efficiency of different methodologies to estimate the tangent stiffness applied to ABAQUS standard UMAT user-subroutine, using implicit formulation with predictor corrector methodology. It uses two new methodologies to estimate the stiffness matrix for the predictor-corrector iterative process, named Disturbance Method and Implicit to Explicit Method. With the Disturbance Method, it is enough to build UMATs using only a secant matrix, since an approximate tangential material matrix is automatically computed, promoting a faster implementation of new material constitutive relations in the ABAQUS standard. It is also studied the possibility of the use of a new algorithm that transforms the UMAT implicit formulation to an explicit formulation using a new secant predictor corrector algorithm. In this study, a new concrete damage model that considers fracture energy regularization for both tensile and compressive behaviour with Mode I Fracture is utilized. Classical and known benchmarks to test damage models in the scientific community are used to compare both methods for the calculation of the Jacobian matrix in terms of time and number of iterations.

Comprehensive Optimization of PID Controller Parameters for DC Motor Speed Management Using a Modified Jellyfish Search Algorithm

ABSTRACTThe proportional integral derivative (PID) controller plays a crucial role in various industrial applications. However, conventional methods often fail to provide optimal parameter values. Therefore, this study proposes a novel version of the jellyfish search (JS) algorithm, the modified jellyfish search (mJS) algorithm, for optimally tuning PID controllers to regulate the speed of a direct current (DC) motor. The original JS algorithm is known for its nature‐inspired approach; however, it has limitations in terms of exploitation power. To address these issues, the proposed mJS incorporates quasi‐dynamic opposed‐based learning and a Weibull probability distribution. The objective function minimizes the integral of the time‐weighted absolute error (ITAE). The effectiveness of mJS was validated by solving the 23rd classical benchmark function, followed by a comparative analysis with contemporary optimization algorithms using stringent statistical criteria. Then mJS is then applied to optimize the PID parameters of three different DC motor models. Results show that the mJS outperforms the standard JS, gray wolf optimization (GWO), JAYA, and golden jackal optimization (GJO) for various performance indicators. Specifically, the best ITAE values were 3.274e6 for DC motor 1 using the JS algorithm, 0.002737 for DC motor 2 using the GJO algorithm, and 0.02785 for DC motor 3 using the mJS algorithm. Additionally, the mJS and JS algorithms reduced the settling time to 0.005 s for DC motor 1, whereas the best settling time of 0.325 s for DC motor 2 was achieved by the mJS algorithm, and a settling time of 1.19 s was recorded for DC motor 3 using both the mJS and JS algorithms. These findings underscore the practical importance of the developed optimizer in engineering applications and its significant contribution to the literature.

Urban density and spatial carbon emission performance of megacities in China

Increasing urban density promotes a greater social role for cities. The current low-carbon city development goals put forward higher requirements for carbon performance. Based on the viewpoint of minimizing natural resource input and maximizing value acquisition in ecological benefits, the influence of urban density on spatial carbon performance is analyzed by polynomial function relationship, and Beijing and Shanghai with different urban function positioning are taken as examples for comparative analysis. Combined with Point-of-Interest (POI), the classification benchmark of industrial activities was established, and the spatial kernel density of ArcGIS software was used to analyze the industrial agglomeration characteristics of the two cities. The results show that: 1) The “increase-decrease-increase-decrease” trend of urban spatial carbon performance over time fluctuates around the unary linear trend of urban density.2) Population and building density are too low or too high, which is not conducive to emission reduction. The change of economic density is influenced by the industrial structure. The change of urban density is negatively correlated with the growth rate of spatial carbon performance. In a certain development stage, the increase of urban density will inhibit the growth of spatial carbon performance to some extent. 3) Maintaining the original economic and industrial structure, the increase in economic density brought by the simple expansion of scale is not conducive to emission reduction. The increase of economic density brought by the transformation of economic or industrial structure and the reduction of the proportion of secondary industry is conducive to urban emission reduction.

Benchmarking Quantum Computational Advantages on Supercomputers

AbstractThe achievement of quantum computational advantage, also known as quantum supremacy, is a major milestone at which a quantum computer can solve a problem significantly faster than the world's most powerful classical computers. Two tasks, boson sampling and random quantum circuit sampling, have experimentally exhibited quantum advantages on photonic and superconducting platforms respectively. Classical benchmarking is essential, yet challenging, because these tasks are intractable for classical computers. This study reviews models, algorithms and large‐scale simulations of these two sampling tasks. These approaches continue to hold substantial significance for research in both current noisy intermediate‐scale quantum (NISQ) systems and future fault‐tolerant quantum computing.