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Among the properties melatonin is claimed to possess, are the immuno-inflammation inductive capacities that would be responsible of some of the paramount of activities melatonin is reported to have in most of the human pathological conditions. In the present paper, we measured the effect of melatonin on established cellular models of immuno-inflammation, and found none. The discrepancies are discussed, especially because those properties are reported at pharmacological concentration (1 μM and beyond) at which the melatonin receptors are desensitized by internalization, leading to putative non-receptor-dependent mechanism of action. • The melatonin enthusiasts are ruining Science reputation, by claiming facts that are often wishful thinking. • Melatonin is not able to boost most of the actors of the immune system, even at pharmacological concentrations. • We hope to be able to show that some of the activities of melatonin are pure fantasies. [ABSTRACT FROM AUTHOR]

Existing RGB-D tracking algorithms advance the performance by constructing typical appearance models from the RGB-only tracking frameworks. There is no attempt to exploit any complementary visual information from the multi-modal input. This paper addresses this deficit and presents a novel algorithm to boost the performance of RGB-D tracking by taking advantage of collaborative clues. To guarantee input consistency, depth images are encoded into the three-channel HHA representation to create input of a similar structure to the RGB images, so that the deep CNN features can be extracted from both modalities. To highlight the discriminatory information in multi-modal features, a feature enhancement module using a cross-attention strategy is proposed. With the attention map produced by the proposed cross-attention method, the target area of the features can be enhanced and the negative influence of the background is suppressed. Besides, we address the potential tracking failure by introducing a long-term mechanism. The experimental results obtained on the well-known benchmarking datasets, including PTB, STC, and CTDB, demonstrate the superiority of the proposed RGB-D tracker. On PTB, the proposed method achieves the highest AUC scores against compared trackers across scenarios with five distinct challenging attributes. On STC and CDTB, our FECD obtains an overall AUC of 0.630 and an F-score of 0.630, respectively. • The single-channel depth maps were encoded into three-channel HHA images. • A feature enhancement method with a cross-attention module to enhance features. • A long-term tracking mechanism to detect failures and recapture lost targets. • Experiments were conducted on several standard tracking benchmarking datasets. [ABSTRACT FROM AUTHOR]

• GSOEM-SV was first proposed to predict slope stability. • GSOEM-SV uses a soft voting method to integrate five mesh search hyperparameter optimized models. • GSOEM-SV outperforms 15 improved models and 2 integrated models. • A practical slope stability prediction application was developed based on uni-app. Slope stability evaluation is a complex and uncertain system problem, and carrying out slope stability prediction is the prerequisite and foundation for slope disaster prevention. In order to achieve fast and accurate prediction of slope stability, this paper considers height, total slope angle, unit weight, cohesion, internal friction angle, and pore water pressure ratio as input features and proposes an intelligent slope stability prediction method based on grid search optimization ensemble learning model by soft voting (GSOEM-SV). First, 390 sets of on-site data were collected to form a dataset, and analyses including correlation coefficients, density estimates, and box lines were carried out. Then, the grid search optimization algorithm is used to optimize the hyperparameters of five algorithms—Gradient Boosting Decision Trees, Light Gradient Boosting Machine, Categorical Boosting, Support Vector Machine, and Random Forest, and integrates them through soft voting. Furthermore, this paper optimizes the hyperparameters of the above five algorithms based on grid search, particle swarm and simulated annealing algorithms, builds 15 improved models and 2 ensemble models and conducts comparison. The results reveal that the GSOEM-SV has the highest slope stability prediction accuracy, up to 91 %, the area under the curve (AUC) is 0.950, and its F 1 score 0.917, which are better than the 15 improved and 2 integrated models. In addition, a set of slope stability prediction app based on uni-app is developed in the paper. It provides a technical foundation and an open and shareable information service platform for slope hazard prediction in geotechnical engineering. [ABSTRACT FROM AUTHOR]

Tube bending is a widely used process in marine, automotive, construction and other industries. Different methods are available for this process during which various problems and defects are encountered. This paper aims to develop the most relevant model for bending the tubes using a computer numerical controlled (CNC) bending machine on the first attempt without any defects. To achieve this goal, the parameters affecting the tube bending process on the bending machine are defined. Based on these parameters, 150 bending experiments are conducted in an industrial plant to collect data and their results are used as dataset for machine learning (ML) algorithms. Seven different state-of-the-art ML algorithms –logistic regression, decision tree, k-nearest neighbor, random forest, Naïve Bayes, support vector machine and eXtreme gradient boosting (XGBoost)– are implemented using Python's Scikit-Learn library. Their performance is compared using confusion matrix and classification metrics such as accuracy, precision, recall, F1-score, receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) value. Considering all the performance metrics, logistic regression performed best in terms of prediction on our dataset. XGBoost and support vector machine were quiet successful based on F1 score and AUC, respectively. Overall, logistic regression, Naïve Bayes, Support Vector Machine, and XGBoost showed performances above 90% across all metrics. Decision tree, k-nearest neighbor and random forest performed poorly compared to other algorithms for our data. The proposed ML method is expected to save costs by reducing material waste and labor time and to increase the process efficiency and the product quality. [ABSTRACT FROM AUTHOR]

This paper introduces an improved differential evolution algorithm based on cooperative multi-population (CMp-DE for short), which combines diverse population collaboration mechanisms and catalytic factors into an improved differential evolution framework. By harnessing various population collaboration mechanisms, the algorithm enhances the diversity of individuals within populations during initial iterations and reduces it during later iterations, thereby harmonizing the algorithm's exploratory and exploitative capabilities. Furthermore, a novel mutation operator is proposed that divides the iterative process into exploration and exploitation phases, thereby augmenting the algorithm's global exploration prowess. Lastly, a catalytic operator is introduced to generate new individuals near post-crossover individuals based on a specified rule, which enhances the algorithm's ability to escape local optima and increasing stability. The proposed CMp-DE is benchmarked against the CEC2017 benchmark test functions and compared against 13 algorithms, including five differential evolution algorithms and their variants, as well as eight state-of-the-art metaheuristic optimization algorithms. This evaluation assesses the CMp-DE's solution accuracy, convergence, stability, and scalability. Finally, the applicability of CMp-DE is validated by addressing six practical optimization problems. The experimental results show that CMp-DE surpasses other algorithms in terms of both convergence accuracy and robustness. Moreover, integrating a catalytic operator with other optimization algorithms notably boosts performance in convergence accuracy and stability. The inclusion of the catalytic operator has significantly enhanced the performance of algorithms compared to their performance before its addition. This underscores the potential of the catalytic operator in improving the performance of various algorithms, particularly in terms of convergence accuracy and robustness. [ABSTRACT FROM AUTHOR]

Modernizing traditional power grids into smart grids represents a significant advancement in improving efficiency and sustainability within the energy sector. However, integrating renewable energy sources and the intricate network of interconnected devices has presented new challenges, especially in grid stability. Anticipating and maintaining grid stability ensures a continuous energy supply and prevents potential disruptions. This research paper explores the smart grid stability issue through machine learning. Two distinct scenarios are examined. In the first scenario, the problem is approached as a binary classification task, where grid states are categorized as stable or unstable, a well-established method for addressing grid stability. In the second scenario, an effort is made to predict continuous values that reflect the degree of grid stability. These continuous values are subsequently mapped to a conditional function, facilitating stability detection based on a predefined threshold. To address data imbalance, two balancing strategies, specifically the Synthetic Minority Over-sampling Technique (SMOTE) and K-means SMOTE, are employed in each scenario. The analysis covers a range of regressors and classifiers, focusing on tree-based models, boosting algorithms, ensemble learners, and Multilayer Perceptrons (MLP). The investigation results demonstrate that combining the K-means SMOTE strategy with the CatBoost Classifier in the first scenario achieves the highest accuracy, reaching an impressive 99.6%, and an exceptional ability to detect grid instability, achieving 100% accuracy. Furthermore, the results of the second scenario are promising, with accuracy rates reaching 99.4%, thanks to the utilization of a stacking ensemble learner. Notably, this study introduces Explainable AI, a groundbreaking initiative in this field, to delve into the inner workings of the proposed models and enhance their transparency. This pioneering effort marks a significant step forward in understanding and optimizing the predictive capabilities of machine learning models applied to smart grid stability. [Display omitted] • Reframing the smart grid stability problem as a regression task. • Underscoring the significance of predicting the grid stability value. • Assessing the impact of data balancing strategies on the prediction results. • Evaluating several classifiers and regressors for smart grid stability. • Achieving 100% instability detection accuracy using K-means SMOTE and CatBoost. • Pioneering eXplainable AI (XAI) techniques to enhance model transparency. [ABSTRACT FROM AUTHOR]

This paper introduces an improved differential evolution algorithm based on cooperative multi-population (CMp-DE for short), which combines diverse population collaboration mechanisms and catalytic factors into an improved differential evolution framework. By harnessing various population collaboration mechanisms, the algorithm enhances the diversity of individuals within populations during initial iterations and reduces it during later iterations, thereby harmonizing the algorithm's exploratory and exploitative capabilities. Furthermore, a novel mutation operator is proposed that divides the iterative process into exploration and exploitation phases, thereby augmenting the algorithm's global exploration prowess. Lastly, a catalytic operator is introduced to generate new individuals near post-crossover individuals based on a specified rule, which enhances the algorithm's ability to escape local optima and increasing stability. The proposed CMp-DE is benchmarked against the CEC2017 benchmark test functions and compared against 13 algorithms, including five differential evolution algorithms and their variants, as well as eight state-of-the-art metaheuristic optimization algorithms. This evaluation assesses the CMp-DE's solution accuracy, convergence, stability, and scalability. Finally, the applicability of CMp-DE is validated by addressing six practical optimization problems. The experimental results show that CMp-DE surpasses other algorithms in terms of both convergence accuracy and robustness. Moreover, integrating a catalytic operator with other optimization algorithms notably boosts performance in convergence accuracy and stability. The inclusion of the catalytic operator has significantly enhanced the performance of algorithms compared to their performance before its addition. This underscores the potential of the catalytic operator in improving the performance of various algorithms, particularly in terms of convergence accuracy and robustness. [ABSTRACT FROM AUTHOR]

• A novel post-earthquake, rapid failure mode identification methodology is proposed. • Surface crack and crushing pattern of RC columns are quantified by three indices. • Ten image-based scenarios according to the availability of indices are considered. • Eight ML-based models are trained and validated through 5-fold cross validation. • High accuracy of models for various ML algorithms and scenarios are achieved. The seismic failure mode identification of reinforced concrete columns is crucial for post-earthquake stability and safety assessment of the damaged concrete buildings. In this paper, a multi-feature crack image-based methodology is proposed for non-destructive non-contact failure mode prediction in reinforced concrete columns damaged in an earthquake. A databank of 455 surface crack patterns from quasi-static experiments on 111 reinforced concrete columns with a variety of structural and geometric properties is collected by the authors aimed at the development and validation of the methodology. For the intricacy retrieval of the surface crack maps, multi-feature fractal-based procedures are taken into consideration. Various machine learning algorithms are employed to train the seismic failure mode predictive models using the characteristics of the damaged RC columns. Ten scenarios are defined based on the image-extracted indices, and structural and geometric characteristics of rectangular reinforced concrete columns. Hyperparameter tuning of the machine learning algorithms using GridsearchCV function has optimized the performance of the models. A k-fold cross-validation procedure is also executed to examine the robustness of the results. Results reveal that the predictive model in which the image-extracted indices and structural parameters are jointly used as input features of supervised learning provides 98% accuracy for the testing dataset with Extreme gradient boost algorithm. In addition, among the scenarios that only image-derived parameters are considered as input features, an accuracy of 83% is obtained with Extreme gradient boost algorithm for the testing dataset. [ABSTRACT FROM AUTHOR]

This paper presents data-driven models for photovoltaic module temperature prediction and analyzes the relation and effects of ambient conditions to module temperature. A total of 12 different machine learning and regression algorithms are implemented, with a large experimental dataset of 345,600 × 7. Prior to implementing those algorithms, data preprocessing is performed to prepare the datasets and determine the informative attributes for the models. Using PCA with module temperature as target to predict, the selected features for models' inputs were determined to be ambient temperature, solar radiation, wind speed, and relative humidity, and each algorithm is cross-validated and tuned with optimal performance parameters. Results show that while relative humidity is more likely to introduce less information to the model, other aforementioned features are the important parameters to predict the module temperature. While for linear modeling, LASSO algorithm provided the best performance, the ANN model demonstrated the best overall results as it produced the most accurate predictions with lowest errors. A similar performance is attained by the proposed non-linear model, KRR and Gradient Boosting algorithm, with a slight advantage to the KRR model. Furthermore, in comparison to experimental data, the ANN model and the proposed non-linear model provided an R2 values of 0.986 and 0.981, with a MAE of 0.982 and 1.476, and MSE of 2.181and 3.464, respectively. Moreover, the proposed model supplied accurate results when compared to models from literature in an out-of-sample testing, it also proven to be robust and accurate when used to predict the PV power output. • Prediction of photovoltaic module temperature considering ambient weather conditions. • Predictive models have been developed using twelve different machine learning and regression algorithms. • Ambient temperature and solar radiation are key parameters and important variables to predict the PV module temperature. • The performance of developed models was evaluated and compared with other models in the literature. [ABSTRACT FROM AUTHOR]

• A database on compressive strength of recycled aggregate concrete is built up. • The compressive strength prediction models with high accuracy are constructed. • The multi-objective optimization design of recycled aggregate concrete mixture proportions based on machine learning and NSGA-II algorithm is carried out. • The influence factors on compressive strength of recycled aggregate concrete are analyzed. This paper employs Support Vector Regression (SVR), Random Forest Regression (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) algorithms to establish the compressive strength prediction models for Recycled Aggregate Concrete (RAC) and analyze the influence of ten inputs on RAC compressive strength. Combined with the best prediction model, the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-II) is applied for multi-objective optimization of mixture proportions in RAC addressing cost, carbon emissions, and compressive strength as key objectives. The results demonstrate that the RAC compressive strength prediction model using the GB algorithm exhibits the highest accuracy, with the R2 value of 0.99 for the training set and 0.85 for the testing set. Feature importance and SHAP analysis reveal that cement and water contents are the primary factors affecting the compressive strength of RAC. Among the three mineral admixtures including Silica Fume (SF), Fly Ash (FA), and Ground Granulated Blast Furnace Slag (GGBFS), SF exhibits superior improvement in the compressive strength of RAC compared to FA and GGBFS. Partial dependency plot analysis indicates that concrete strength remains unaffected by recycled coarse aggregate content within the range of 0 to 300 kg/m3. Pareto fronts of a tri-objective mixture optimization problem for RAC are successfully obtained through the GB model combined with the NSGA-II algorithm, which can guide the optimization of RAC preparation. [ABSTRACT FROM AUTHOR]

• Greedy strategy fused with centroid and elite guidance boosts algorithm exploration. • Adding self-learning to the Levy flight algorithm reduces the risk of local optima. • Per-dimension mirror boundary control assures algorithm search within feasible bounds. • An adaptive survival-of-the-fittest strategy improves the algorithm's robustness. • IARO is compared to 14 algorithms on 52 functions and 7 engineering problems. The Artificial Rabbit Optimization (ARO) algorithm has been proposed as an effective metaheuristic optimization approach in recent years. However, the ARO algorithm exhibits shortcomings in certain cases, including inefficient search, slow convergence, and vulnerability to local optima. To address these issues, this paper introduces a multi-strategy improved Artificial Rabbit Optimization (IARO) algorithm. Firstly, in the enhanced search strategy, we propose integrating the centroid guidance mechanism and elite guidance mechanism with the greedy strategy to update the position during the exploration phase. Additionally, the Levy flight strategy integrated with self-learning, is employed to update the position during the development phase to improve convergence speed and prevent falling into local optima. Secondly, the algorithm incorporates a per-dimension mirror boundary control strategy to map individuals exceeding the boundary back within the boundary back inside the boundary. This boundary control strategy ensures the algorithm operates within bounds and enhances convergence speed. Finally, within the survival of the fittest strategy, an adaptive factor is introduced to gradually enhance the population's overall adaptability. This factor regulates the balance between exploration and exploitation, allowing the algorithm to fully explore the search space and improve its robustness. To substantiate the effectiveness of the proposed IARO algorithm, a rigorous and systematic verification analysis was undertaken. Comparative experiments for qualitative and quantitative analysis were conducted on three benchmark test sets, namely CEC2017, CEC2020, and CEC2022. The analysis results, including the Wilcoxon rank-sum test, consistently demonstrates that this improved algorithm outperforms ARO and other state-of-the-art optimization algorithms comprehensively. Finally, the feasibility of the IARO algorithm has been verified in seven classical constrained engineering problems. [ABSTRACT FROM AUTHOR]

In recent years cryptographic tokens have gained popularity as they can be used as a form of emerging alternative financing and as a means of building platforms. The token markets innovate quickly through technology and decentralization, and they are constantly changing, and they have a high risk. Negotiation strategies must therefore be suited to these new circumstances. The genetic algorithm offers a very appropriate approach to resolving these complex issues. However, very little is known about genetic algorithm methods in cryptographic tokens. Accordingly, this paper presents a case study of the simulation of Fan Tokens trading by implementing selected best trading rule sets by a genetic algorithm that simulates a negotiation system through the Monte Carlo method. We have applied Adaptive Boosting and Genetic Algorithms, Deep Learning Neural Network-Genetic Algorithms, Adaptive Genetic Algorithms with Fuzzy Logic, and Quantum Genetic Algorithm techniques. The period selected is from December 1, 2021 to August 25, 2022, and we have used data from the Fan Tokens of Paris Saint-Germain, Manchester City, and Barcelona , leaders in the market. Our results conclude that the Hybrid and Quantum Genetic algorithm display a good execution during the training and testing period. Our study has a major impact on the current decentralized markets and future business opportunities. [ABSTRACT FROM AUTHOR]

The unceasing evolution of analytical instrumentation determines an exponential increase of data production, which in turn boosts new cutting-edge analytical challenges, requiring a progressive integration of artificial intelligence (AI) algorithms into the instrumental data treatment software. Machine learning, deep learning, and computer vision are the most common techniques adopted to exploit the information potential of advanced analytical chemistry measures. In this paper, our primary focus is on elucidating the remarkable advantages of leveraging AI tools for comprehensive two-dimensional gas chromatography data (pre)processing. We illustrate how AI techniques can efficiently explore the complex datasets derived from multidimensional platforms combining comprehensive two-dimensional separations with mass spectrometry in the challenging application area of food-omics. Pattern recognition based on image processing, computer vision, and AI smelling are discussed by introducing the principles of operation, reviewing available tools and software solutions, and illustrating their potentials and limitations through selected applications. • Artificial Intelligence boost the informative potential of GC × GC. • Computer Vision based on chromatogram images. • AI smelling machine with GC × GC-MS predicts food aroma properties. • Advancing the explorative potential of comprehensive two-dimensional chromatography. • Augmented visualization combines visual features with chemical information. [ABSTRACT FROM AUTHOR]

High-resolution residential smart meter data play a pivotal role in numerous applications, ranging from assessing hosting capacity in low-voltage grids to evaluating the economic viability of household-level solutions such as the integration of photovoltaic (PV) installations with battery energy storage systems (BESS). However, privacy concerns often restrict access to large-scale residential smart meter datasets, leaving only synthetic load profiles available to the public, which generally only represent averages of individual data. In response to this challenge, this paper proposes an innovative approach leveraging Generative Adversarial Networks (GANs) to generate synthetic residential load profiles. Our method operates in two distinct phases: First, we employ the DoppelGANger (DGAN) architecture to construct annual time series of daily consumption values. DGAN has been selected due to its demonstrated capacity for capturing interday and seasonal dynamics. Second, a wavelet-based decomposition-recombination technique is employed to create stochastic daily profiles with realistic intraday variations and peak demand behavior. Our DGAN-based approach demonstrates remarkable effectiveness, as evidenced by evaluation against a real-world dataset through a series of qualitative microbenchmarks. Moreover, the practical utility of the two-step methodology is explored in three downstream applications: (i) the integration of PV systems, (ii) PV-BESS systems increasing PV self-consumption, and (iii) PV-BESS systems boosting PV self-consumption while performing peak shaving. Our methodology is shown to accurately capture and model the intricate complexities inherent in real smart meter data. • Modeling high-frequency annual residential load profiles via two-step approach. • DGAN captures interday, weekly and seasonal long-term correlations. • Wavelet-based modeling at daily level assures realistic stochastic peak load behavior. • Benchmark GAN output via autocorrelation, annual consumption and profile variability. • Fidelity of synthetic data was validated for various PV and PV-BESS applications. [ABSTRACT FROM AUTHOR]

• Piecewise nonlinear resistor-capacitor inductor shunted josephson junction is presented. • The rate equations of the model are obtained and the model has two steady states. • Bursting, chaotic, bistable, coexisting and antimonotonicity behaviors are obtained. • The microcontroller and dynamical attractors find qualitative agreement. • Offset boosting control is achieved in the voltage and current variables. This paper evaluates the theoretical and microcontroller execution probing of piecewise nonlinear resistor-capacitor inductor shunted Josephson junction circuit (PNRCISJJC). Kirchhoff's laws establish the equivalent rate equations of the PNRCISJJC where two steady states with one stable and its counterpart unstable for excitation current less than or equal to one with a hat tip to Routh–Hurwitz criterion. No steady state for the applied current greater than one is observed in the PNRCISJJC. The PNRCISJJC exhibits bursting behaviors, varying structures of hidden chaotic characteristics, bistable period-4-oscillations, coexisting behaviors, regular and hidden chaotic bubbles, and antimonotonicity. Furthermore, linear offset boosting based on the current and voltage state variables uncovered that the polarity of the hidden chaotic signal of either state variable can be flexibly altered by varying the offset boosting parameters. The agreement between dynamical analysis and microcontroller realization dynamics finalizes the investigation. [ABSTRACT FROM AUTHOR]

Establishing an accurate quality prediction model is an essential prerequisite for quality management in diesel engine manufacturing factories. However, the large production scale and non-linear relationship between features and the diesel engine power significantly aggravate the complexity of establishing a power prediction model. The identification of key features can not only improve the prediction performance but also facilitate the control of product quality. The mainstream feature selection methods estimate features on a whole static sampling space, which ignores dynamic correlation among candidate features, selected features, and the label variable. This paper focuses on the relevance between the candidate features and diesel engine quality, the complementary and redundancy between the candidate features and selected features, proposing a conditional mutual information-based dynamic feature selection method for the prediction of diesel engine quality. Then, a high-accuracy AdaBoost model is proposed with support vector regression-partial swarm optimization (SVR-PSO) as weak learners, showing a satisfactory non-linear fitting ability and a convenient parameter optimization process. Finally, several cases are performed to evaluate the effectiveness of the proposed methods. The proposed feature selection method shows a better performance than common feature selection methods on UCI datasets. Results on diesel engine power prediction declare that the proposed AdaBoost-SVR-PSO model is an effective quality prediction method compared with adaptive boosting, multilayer perception, and gradient boosting decision tree. [ABSTRACT FROM AUTHOR]

This paper aims to propose a new hybrid Machine Learning (ML) with Bayesian Optimization (BO) methods for predicting the patch loading resistance, P u of longitudinally unstiffened plate girders. A total of 354 tests of the unstiffened plate girder under patch loading are collected and used for the training and testing to establish the proposed models. Five ML models including Support Vector Machines (SVM), Decision Tree (DT), Gradient Boosted Tree (GBT), Extreme Gradient Boosting algorithm (XGBoost), and CatBoost regression (CAT) are employed, and the BO method is used to optimize the hyperparameters of these ML models, in order to show which of them is best-suited for prediction of the PLR of longitudinally unstiffened plate girders. It was found that the BO-GBT presents the best accuracy compared to others. The performance of the BO-GBT model is validated by comparing its predictive results with the current design standards and the existing formulae. Additionally, the Shapley Additive Explanations (SHAP) method is employed to evaluate the importance and contributions of each input variable on the proposed model, and a Graphical User Interface (GUI) tool is developed to conveniently estimate the P u of the unstiffened plate girders. Finally, the BO-GBT model is used to develop a support tool for finding suitable geometric dimensions and material properties of longitudinally unstiffened girder under patch loading in the preliminary design stage. The optimization tool is accessible online for the users for more convenient use in practical design purposes. [ABSTRACT FROM AUTHOR]

This paper aims to develop a unique artificial neural network (ANN)-based equation as well as MATLAB- and Python-based graphical user interfaces (GUIs) using the most comprehensive and up-to-date database for predicting the behaviour of axially loaded concrete-filled double skin tubular (CFDST) short and slender columns with normal- and high-strength materials. Two machine learning (ML) methods, which are ANN and extreme gradient boosting (XGBoost), are trained and tested using 1721 sets of data, with 129 of them collected from experimental studies and 1592 generated by finite element (FE) simulations. The accuracy of the developed ML models is assessed through comparing their predictions with the experimental and FE results. To demonstrate the effect of each parameter on the predicted results, the SHapley Additive exPlanations (SHAP) method is used. The developed ML models are also used to conduct parametric studies to examine the effect of geometric and material parameters on the predicted results. The accuracy of the ML models and the proposed ANN-based equation in predicting the ultimate axial capacity of CFDST columns is compared with that of six design methods including two design code provisions and four design equations proposed by researchers. A numerical example is presented to illustrate the design procedure of the CFDST column using the proposed ANN-based equation. The results indicate that the ANN model performs better on unseen data than the XGBoost model with lower root mean square error for the test set. The results also show that the ML models and the proposed ANN-based equation are superior to the other design models in prediction accuracy. • 1721 data samples of CFDST columns are used to train and test the ML models. • Optimized ANN and XGBoost models are used to predict the behaviour of CFDST columns. • ANN-based equation, MATLAB- and Python-based GUIs are developed. • Parametric studies are conducted using the trained ML models. • The superiority of the proposed design models over six design equations is shown. [ABSTRACT FROM AUTHOR]

Plagiarism detection (PD) in natural language processing involves locating similar words in two distinct sources. The paper introduces a new approach to plagiarism detection utilizing bidirectional encoder representations from transformers (BERT)-generated embedding, an enhanced artificial bee colony (ABC) optimization algorithm for pre-training, and a training process based on reinforcement learning (RL). The BERT model can be incorporated into a subsequent task and meticulously refined to function as a model, enabling it to apprehend a variety of linguistic characteristics. Imbalanced classification is one of the fundamental obstacles to PD. To handle this predicament, we present a novel methodology utilizing RL, in which the problem is framed as a series of sequential decisions in which an agent receives a reward at each level for classifying a received instance. To address the disparity between classes, it is determined that the majority class will receive a lower reward than the minority class. We also focus on the training stage, which often utilizes gradient-based learning techniques like backpropagation (BP), leading to certain drawbacks such as sensitivity to initialization. In our proposed model, we utilize a mutual learning-based ABC (ML-ABC) approach that adjusts the food source with the most beneficial results for the candidate by considering a mutual learning factor that incorporates the initial weight. We evaluated the efficacy of our novel approach by contrasting its results with those of population-based techniques using three standard datasets, namely Stanford Natural Language Inference (SNLI), Microsoft Research Paraphrase Corpus (MSRP), and Semantic Evaluation Database (SemEval2014). Our model attained excellent results that outperformed state-of-the-art models. Optimal values for important parameters, including reward function are identified for the model based on experiments on the study dataset. Ablation studies that exclude the proposed ML-ABC and reinforcement learning from the model confirm the independent positive incremental impact of these components on model performance. • BERT-based plagiarism detection with RL and ML-ABC. • Reward function in RL improves detection of minority plagiarism class. • Model outperforms state-of-the-art on SNLI, MSRP, SemEval2014 datasets. • Ablation studies highlight the impact of ML-ABC and RL on performance. [ABSTRACT FROM AUTHOR]

Prediction-based dynamic multiobjective optimization evolutionary algorithms have become one of the mainstream methods for solving dynamic multiobjective optimization problems. However, the unknown nonlinear relationships in sequential environments bring great challenge to the construction of prediction model and the inevitable error between the historical obtained solutions and the real pareto-optimal solutions troubles the prediction accuracy. In this paper, a gradient boosting regression tree based dynamic multiobjective optimization evolutionary algorithm is proposed, called MOEA/D-XGB. The time series solutions formed by subspace decomposition are trained in the XGBoost-based predictor to produce a high-quality initial population when environmental change occurs. And a novel population promotion strategy based on generalized additive model is proposed to improve the quality of historical obtained solutions. The performance comparisons with five state-of-the-art algorithms have shown that MOEA/D-XGB achieves best performance in 59 out of 70 experiments on MIGD and 60 out of 70 experiments on MHV, respectively, which demonstrates that the proposed design is capable of significantly improving the performance of dynamic multiobjective optimization. [ABSTRACT FROM AUTHOR]

A metaheuristic-based structural optimization method, whilst being popularly adopted due to its advantages in by-passing gradient function calculations, requires the use of time-consuming advanced analyses for constraint evaluation. To overcome this drawback, the present paper proposes a robust (machine learning-based) optimization method that combines the light gradient boosting machine (LightGBM) with the efficient p-best differential evolution (E p DE) method. In essence, the LightGBM classification model is constructed to assess the constraint (safety and integrity) satisfaction of structures. An efficient framework using a so-called safety parameter is proposed to prevent inaccurate predictions of the LightGBM model. The E p DE processes the optimization procedures on the constructed classification LightGBM model. This enables an enhanced machine learning-based optimization technique that not only maintains the sufficiently accurate optimal design of structures but also significantly reduces the required computing efforts, as compared to standard optimization schemes. Various examples of steel structure designs (i.e., two of which have been provided herein) have been successfully performed by the proposed approach. These illustrate the accuracy and robustness of the proposed method, where good comparisons with reference algorithms (including standard DE with "DE/rand/1" mutational strategy, Jaya, Rao-1 and CaDE) are evidenced. The statistical values collected present the accuracy and reliability of the proposed method in obtaining the minimum total weight designs with substantial (some 60%) reduction of computing efforts required to furnish optimal results. [ABSTRACT FROM AUTHOR]

Multi-robot path planning (MRPP) in continuous and known environment is studied in this paper via proposing a novel local path planning approach. To plan optimal collision-free paths for multiple robots simultaneously, a novel implementation method suitable for the meta-heuristic algorithms is devised, and an improved artificial bee colony (ABC) algorithm is developed. Three enhancements to the ABC algorithm are made in this context. Firstly, to better lead the search direction, the global best individual is involved in the search equations of employed bee phase and scout bee phase. Meanwhile, to boost exploitation capability, the learning method of teaching-learning based optimization (TLBO) algorithm is incorporated into the onlooker bee phase. The proposed learning-based ABC (ABCL) algorithm is used to determine the subsequent positions for all the robots based on their current coordinates considering the path length, safety and planning efficiency. The experimental studies on benchmark functions show that ABCL is outstanding in solving different types of optimization problems compared against seven effective meta-heuristic algorithms. More importantly, MRPP simulation results prove that ABCL outperforms its competitors in terms of generating optimal collision-free paths and running time. Compared with the original ABC, ABCL improves these two aspects on average for all tasks by 2.1% and 12.6%, respectively. Therefore, with the proposed implementation method, ABCL can be considered as an effective MRPP solution. • A metaheuristic-based approach is proposed for the multi-robot path planning problem. • A learning-based ABC algorithm is developed to solve time-sensitive online problems. • Simulations of different MRPP tasks were completed, validating the designed approach. [ABSTRACT FROM AUTHOR]

Conventional machine learning methodologies require the centralization of data for model training, which may be infeasible in situations where data sharing limitations are imposed due to concerns such as privacy and gradient protection. The Federated Learning (FL) framework enables the collaborative learning of a shared model without necessitating the centralization or sharing of data among the data proprietors. Nonetheless, in this paper, we demonstrate that the generalization capability of the joint model is suboptimal for Non-Independent and Non-Identically Distributed (Non-IID) data, particularly when employing the Federated Averaging (FedAvg) strategy as a result of the weight divergence phenomenon. Consequently, we present a novel boosting algorithm for FL to address both the generalization and gradient leakage challenges, as well as to facilitate accelerated convergence in gradient-based optimization. Furthermore, we introduce a secure gradient sharing protocol that incorporates Homomorphic Encryption (HE) and Differential Privacy (DP) to safeguard against gradient leakage attacks. Our empirical evaluation demonstrates that the proposed Federated Boosting (FedBoosting) technique yields significant enhancements in both prediction accuracy and computational efficiency in the visual text recognition task on publicly available benchmarks. [ABSTRACT FROM AUTHOR]

Ground motion prediction is an important and complex research subject in earthquake engineering and traditional approaches based on statistical regression have much room for improvement in prediction accuracy. Utilizing the 67,164 ground motion records from KiK-net and K-Net for 777 shallow crustal earthquakes between 1997 and 2019 in Japan, this paper proposes a ground motion prediction model XGBoost-SC based on the machine learning algorithm of eXtreme Gradient Boosting (XGBoost) for Japan. Magnitude, focal depth, hypo-central distance, V s30 , site altitude, and focal mechanism were used as the feature parameters, and XGBoost, Random Forest, and Deep Neural Networks (DNN) algorithms were selected for model training while Bayesian optimization was used to search for optimized hyperparameters to improve the prediction accuracy. XGBoost algorithm was selected for further study based on the comparison of results from the three algorithms. Residual change with magnitude and hypo-central distance, the probability distribution of residuals, residual standard deviation (σ), residual mean squared error (MSE), and Pearson correlation coefficient (R) were used as the evaluation parameters, and a comparison study was performed against the ground motion prediction equation based on traditional approaches. Actual earthquake events were selected to compare the prediction results against the observation records. To further validate and explain the proposed model, SHapley Additive exPlanations (SHAP) analysis was performed to explain the impact of selected feature parameters on the proposed model. The results demonstrate that the proposed XGBoost-SC model has good prediction stability for all periods, and its residual errors are smaller than those of other models. Therefore, the proposed model can better reflect the ground motion attenuation for shallow crustal earthquakes in Japan and can serve as a better model for ground motion prediction in future aseismic design and earthquake disaster mitigation efforts. • A Ground Motion Prediction Model was proposed for shallow crustal earthquakes. • The proposed model was more precise than traditional approaches. • Explained the effect of feature parameters on the prediction results. [ABSTRACT FROM AUTHOR]

With rapid development of single-cell multi-modal sequencing technologies, more and more multi-omics data come into being and provide a unique opportunity for the identification of distinct cell types at the single-cell level. Therefore, it is important to integrate different modalities which are with high-dimensional features for boosting final multi-omics data classification performance. However, existing multi-omics data classification methods mainly focus on exploiting the complementary information of different modalities, while ignoring the learning confidence and cross-modal sample relationship during information fusion. In this paper, we propose a multi-omics data classification network via global and cross-modal feature aggregation, referred to as GCFANet. On one hand, considering that a large number of feature dimensions in different modalities could not contribute to final classification performance but disturb the discriminability of different samples, we propose a feature confidence learning mechanism to suppress some redundant features, as well as enhancing the expression of discriminative feature dimensions in each modality. On the other hand, in order to capture the inherent sample structure information implied in each modality, we design a graph convolutional network branch to learn the corresponding structure preserved feature representation. Then the modal-specific feature representations are concatenated and input to a transformer induced global and cross-modal feature aggregation module for learning consensus feature representation from different modalities. In addition, the consensus feature representation used for final classification is enhanced via a view-specific consistency preserved contrastive learning strategy. Extensive experiments on four multi-omics datasets are conducted to demonstrate the efficacy of the proposed GCFANet. • A feature aggregation network is proposed for multi-omics classification. • A contrastive learning strategy is designed for feature representation alignment. • Both complementary information and sample structure are used for consensus learning. • The proposed network is successfully used for drug response prediction. [ABSTRACT FROM AUTHOR]

Deep learning on point clouds drives 3D object detection. Despite rapid progress, point-based methods still suffer from the problems such as incompletion and occlusion, which are caused by the material properties of objects and cluttered scenes. These difficult targets increase the difficulty of identification or even lead to misidentification, severely weakening the performance of point-based methods on 3D object detection. To alleviate the above problems, we propose the Objformer to boost point-based 3D object detection via instance-wise interaction. We design an instance feature encoder to encode clean instance features, which contain key geometric priors and holistic semantic information. Further, an instance interaction module is devised to aggregate the complementary features across instances with label-guided interaction, boosting the performance of the 3D object detection. Experiments show that Objformer outperforms previous point-based state-of-the-arts on two popular benchmarks, ScanNet V2 and SUN RGB-D. Especially, our single-modal Objformer even outperforms the competing advanced multi-modal fusion method on both SUN RGB-D and ScanNet V2. • This paper proposes a novel two-stage end-to-end differentiable architecture for the 3D object detection in point clouds, which is dubbed as Objformer. • Equipped with the specially designed instance feature encoder, Objformer can extract clean instance feature and significant geometric prior of the target. • By encoding the pseudo category label from the 3D proposals into the semantic feature of instance, Objformer can boost the information complementarity of across objects with the instance interaction module. • Proposed Objformer achieves state-of-the-art 3D object detection performance on SUN RGB-D and ScanNet. The significant performance gains on both benchmarks and the improvement over the multi-modal method indicate the superiority of Objformer. [ABSTRACT FROM AUTHOR]

The significance of accurate short-term load forecasting (STLF) for modern power systems' efficient and secure operation is paramount. This task is intricate due to cyclicity, non-stationarity, seasonality, and nonlinear power consumption time series data characteristics. The rise of data accessibility in the power industry has paved the way for machine learning (ML) models, which show the potential to enhance STLF accuracy. This paper presents a novel hybrid ML model combining Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGBoost), k-Nearest Neighbors (kNN), and Support Vector Regression (SVR), examining both standalone and integrated, coupled with signal decomposition techniques like STL, EMD, EEMD, CEEMDAN, and EWT. Through Automated Machine Learning (AutoML), these models are integrated and their hyperparameters optimized, predicting each load signal component using data from two sources: The National Operator of Electric System (ONS) and the Independent System Operators New England (ISO-NE), boosting prediction capacity. For the 2019 ONS dataset, combining EWT and XGBoost yielded the best results for very short-term load forecasting (VSTLF) with an RMSE of 1,931.8 MW, MAE of 1,564.9 MW, and MAPE of 2.54%. These findings highlight the necessity for diverse approaches to each VSTLF problem, emphasizing the adaptability and strength of ML models combined with signal decomposition techniques. • Novel hybrid structure of machine learning and signal decomposition approaches for VSTLF. • Combination of GBR, XGBoost, KNN, and SVR with STL, EMD, EEMD, CEEMDAN, and EWT. • Accurate predictions demonstrated for ONS and ISO-NE databases. • Valuable tool for power companies to enhance forecasting accuracy in VSTLF. • Addresses challenges of nonlinearity and non-stationarity in short-term load forecasting. [ABSTRACT FROM AUTHOR]

Zero-shot learning (ZSL) aims to recognize novel classes that have no labeled samples during the training phase, which leads to the domain shift problem. In reality, there exists a large number of compounded unlabeled samples. Therefore, it is crucial to accurately estimate the data distribution of these compounded unlabeled samples and improve the performance of ZSL. This paper proposes a zero-shot learning boosting framework. Specifically, ZSL is transformed into a co-training problem between the data distribution estimation of the unlabeled samples and ZSL. The data distribution estimation is modeled as concept-constrained clustering. Furthermore, we design an alternative optimization strategy to realize mutual guidance between the two processes. Finally, systematic experiments verify the effectiveness of the proposed concept-constrained clustering for alleviating the domain shift problem in ZSL and the universality of the proposed framework for boosting different base ZSL models. • A general framework is proposed for boosting base zero-shot learning models. • Zero-shot learning is transformed into a co-training problem. • Concept-constrained clustering is proposed to estimate the data distribution. • An alternative optimization strategy is designed to achieve mutual guidance. [ABSTRACT FROM AUTHOR]