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An added edge to a graph is called an inset edge. Predicting k inset edges, which minimize the average distance of a graph, is known to be NP-Hard. However, when k = 1 , the complexity of the problem is polynomial. In this paper, some tools for a precise analysis of the effect of inset edges on the tree's average distance are established. Using the tools, one can avoid using the distance matrix for better performance of algorithms. Here, some applications of the tools and a tight bound for the change of average distance when an inset edge is added to a tree are presented. [ABSTRACT FROM AUTHOR]

Spectral clustering is one of the most important clustering approaches, often yielding performance superior to other clustering approaches. However, it is not scalable to large data sets in its original form due to the computational burden of the required large-matrix eigen-decomposition. In this paper, a two-step spectral clustering algorithm is introduced by extending recent advances of scalable spectral clustering based on low-rank affinity matrix using landmarks. In the first step, a scalable spectral clustering algorithm using raw landmark-based affinity matrix is adopted. In the second step, a novel low-rank affinity matrix is learnt via the probability density estimators, constructed from the estimated clusters as derived from the first step. Since the prior information on cluster labels can be utilised in the second step, this learnt affinity matrix reflects intrinsic pairwise data relationships much better. While the proposed more complicated algorithm results in a higher computational cost than the previous landmark-based spectral research, it can be shown that the associated computational cost still scales well with data size. It is demonstrated that the proposed algorithm is capable of achieving far superior performance than other state-of-the-art algorithms for several benchmark multi-class image data sets. [ABSTRACT FROM AUTHOR]

Almost all systems are inevitably subject to various uncertainties or disturbances from the external environment in practical applications. Taking these factors into consideration, in this paper a distributed algorithm with state noise and gradient disturbance is proposed for solving distributed optimization problem with closed convex set constraint based on multi-agent system under weight-balanced graph. Moreover, based on the gradient tracking and projection methods, the proposed distributed algorithm with gradient tracking can improve the convergence rate by introducing a projection error term and an auxiliary parameter. In contrast to some existing constrained distributed gradient algorithms, the proposed one can make the convergence faster and enhance the performance of convergence. The proposed algorithm is illustrated with two simulation examples to show its effectiveness and robustness. [ABSTRACT FROM AUTHOR]

For multilayer perceptron (MLP), the initial weights will significantly influence its performance. Based on the enhanced fractional derivative extend from convex optimization, this paper proposes a fractional gradient descent (RFGD) algorithm robust to the initial weights of MLP. We analyze the effectiveness of the RFGD algorithm. The convergence of the RFGD algorithm is also analyzed. The computational complexity of the RFGD algorithm is generally larger than that of the gradient descent (GD) algorithm but smaller than that of the Adam, Padam, AdaBelief, and AdaDiff algorithms. Numerical experiments show that the RFGD algorithm has strong robustness to the order of fractional calculus which is the only added parameter compared to the GD algorithm. More importantly, compared to the GD, Adam, Padam, AdaBelief, and AdaDiff algorithms, the experimental results show that the RFGD algorithm has the best robust performance for the initial weights of MLP. Meanwhile, the correctness of the theoretical analysis is verified. [ABSTRACT FROM AUTHOR]

The cohesive subgraph k-core is a maximal connected subgraph with the minimum degree δ ≥ k of a simple graph, where integer k ≥ 0. Define the core number of a vertex w as the maximum k such that w is contained in a k -core. The core decomposition problem which is calculating the core numbers of all vertices in static graphs, and the core maintenance problem which is updating the core numbers in dynamic graphs are our main concern. Although, core numbers can be updated by the core decomposition algorithms, only a small part of vertices' core numbers have changed after the change of a graph. Thus, it is necessary to update core numbers locally to reduce the cost. In this paper, we study the core maintenance problem on edge-weighted graphs by using the vertex sequence k-order which is ordered by the order that the core decomposition algorithm removes vertices. We design the core maintenance algorithms for inserting one edge at a time and the method of updating the k -order, which reduce the searching range and the time cost evidently. For the removing case, we use the existing subcore algorithm to do the core maintenance and modify it with the method of updating k -order we design. Finally, we do extensive experiments to evaluate the effectiveness and the efficiency of our algorithms, which shows that the order based algorithm has a better performance than the existing for the core maintenance on edge-weighted graphs. • We solve the core maintenance problem on edge-weighted graphs with a tool named weighted k -order. • The weighted k -order, a vertex sequence, is defined on edge-weighted graphs for the first time. • We give the properties of vertices whose core numbers increase on the k-order for inserting case on edge-weighted graphs. • The order based core maintenance algorithm for inserting case on edge-weighted graphs is given, which is the best. • We give the methods to update the k-order after inserting or removing one edge for the next core maintenance. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new strategy for solving highly oscillatory two-dimensional Vlasov-Poisson systems by means of a specific version of the parareal algorithm. The novelty consists in using reduced models, obtained from the two-scale convergence theory, for the coarse solving. The reduced models are useful to approximate the original Vlasov-Poisson model at a low computational cost since they are free of high oscillations. Both models are numerically solved in a particle-in-cell framework. We illustrate this strategy with numerical experiments based on long time simulations of a charged beam in a focusing channel and under the influence of a rapidly oscillating external electric field. On the basis of computing times, we provide an analysis of the efficiency of the parareal algorithm in terms of speedup. [ABSTRACT FROM AUTHOR]

Automata based algorithms have always dominated the history of string matching, thanks to their intrinsic nature of recognizers. Among them, those based on the suffix automaton deserve a special mention, because its use has always led to elegant and very efficient solutions in practice. In this paper, we present two novel string matching algorithms based on the suffix automaton which are particularly suitable for very long strings. The first algorithm simulates the suffix automaton through the well known bit-parallelism approach, while the second makes use of an alternative non-standard simulation. We mainly focus on the second solution, which turns out to be more flexible in practice, and present some extensions of it. Experimental results suggest that both our solutions are very efficient in practical cases scaling much better when the length of the pattern increases. Moreover, our second approach is also competitive with the most effective solutions available for the exact string matching problem. • We present two novel suffix automata based algorithms specifically designed for very long strings. • We describe a generic non-standard approach for simulating the suffix automaton of a string. • Our first solution is a bit-parallel based extension of the BNDM algorithm which can be encoded using few bits. • Our second solution is related to the occurrence of unique factors in the pattern. • Experimental results suggest that both our solutions are very efficient in practical cases. [ABSTRACT FROM AUTHOR]

In the l -Path Vertex Cover problem (resp., the l -Component Order Connectivity problem) the input is an undirected graph G and an integer k. The goal is to decide whether there is a set of vertices of size at most k whose deletion from G results in a graph that does not contain a path with l vertices (resp., does not contain a connected component with at least l vertices). In this paper we give a parameterized algorithm for l -Path Vertex Cover when l = 5 , 6 , 7 , whose running times are O ⁎ (3.945 k) , O ⁎ (4.947 k) , and O ⁎ (5.951 k) , respectively. We also give an algorithm for l -Component Order Connectivity whose running time is O ⁎ ((l − 1 − ϵ l) k) for every l ≥ 4 , where ϵ l > 0 for every l. [ABSTRACT FROM AUTHOR]

The benefits of using crossover in crossing fitness gaps have been studied extensively in evolutionary computation. Recent runtime results show that majority-vote crossover is particularly efficient at optimizing the well-known Jump benchmark function that includes a fitness gap next to the global optimum. Also estimation-of-distribution algorithms (EDAs), which use an implicit crossover, are much more efficient on Jump than typical mutation-based algorithms. However, the allowed gap size for polynomial runtimes with EDAs is at most logarithmic in the problem dimension n. In this paper, we investigate variants of the Jump function where the gap is shifted and appears in the middle of the typical search trajectory. Such gaps can still be overcome efficiently in time O (n log ⁡ n) by majority-vote crossover and an estimation-of-distribution algorithm, even for gap sizes almost n. However, if the global optimum is located in the gap instead of the usual all-ones string, majority-vote crossover would nevertheless approach the all-ones string and be highly inefficient. In sharp contrast, an EDA can still find such a shifted optimum efficiently. Thanks to a general property called fair sampling , the EDA will with high probability sample from almost every fitness level of the function, including levels in the gap, and sample the global optimum even though the overall search trajectory points towards the all-ones string. Finally, we derive limits on the gap size allowing efficient runtimes for the EDA. • We study 2 algorithms using majority-vote crossover and an estimation-of-distribution algorithm on modified jump functions. • We derive theorems on the algorithms' runtime using rigorous mathematical analyses. • All 3 algorithms can overcome the fitness gap of the jump functions efficiently for moderate sizes of the gap. • All but the estimation-of-distribution algorithm usually fail to find an optimum located within the gap. • The estimation-of-distribution is efficient since it samples fairly on all fitness levels towards the optimum. [ABSTRACT FROM AUTHOR]

• Analyses general algorithms as sets of sequences (tasks) over a domain. • Defines an equivalence relation on sets of sequences to recover algorithmic tasks. • Characterises the execution trace sets of Blum-Shub-Smale machines. • Compares trace-based approaches to algorithms to iterator-based accounts. Traditional models of computation and algorithms take a constructive approach, essentially considering a transition function over a given state space. Yet algorithms may also be considered in terms of the observed behaviour they generate. This paper marries these perspectives with a domain-independent, trace based account of computational and algorithmic processes. We focus on the model of real computation due to Blum, Shub and Smale [3] and provide a precise characterisation of the execution trace sets generated by their machines. [ABSTRACT FROM AUTHOR]

Chaotic systems are suitable for image encryption due to their numerous intrinsic properties. However, the low-dimensional chaotic systems used in many existing chaos-based image encryption algorithms suffer from various drawbacks. To overcome such problems, a new four-dimensional multi-scroll hyperchaotic system is constructed in this paper. By studying the stability of equilibrium point, dissipation attractors, Lyapunov exponent spectrum, and bifurcation diagram of the system, these are proved that the system has rich dynamical behaviors. Then, based on the system, a new color image encryption algorithm is designed by using the classical structure of the 'scrambling-diffusion-scrambling' algorithm. In this image encryption algorithm, an optimized Arnold transform is used and the plaintext pixel sum is applied to the scrambling parameters so that different plaintext image pixels correspond to different scrambling parameters and are fully resistant to plaintext attacks. Then for the three Red, Green, Blue (RGB) channels, three different diffusion directions are adopted. Finally, the correlation between the pixels is further broken by an ascending permutation scrambling operation to get the final encrypted image. The experimental results show that the key space is up to 2207, the information entropy is up to 7.9998, the Number of Pixels Change Rate (NPCR) and the Unified Average Changing Intensity (UACI) values are close to the ideal values, the scheme can resist exhaustive and differential attacks, and the scheme has good robustness through noise attacks and crop attacks. • Design of hyperchaotic system based on multi-scroll and its encryption algorithm in color image. • The encryption algorithm uses a "scrambling-diffusion-scrambling" structure. • Experiments show that the algorithm is highly secure. [ABSTRACT FROM AUTHOR]

In this paper, GMV as a major AOCS (Attitude Orbit Control System)/GNC (Guidance, Navigation and Control) system provider presents its work on the development of a GNC system for a Kinetic Impact mission. Asteroid impacts on Earth are a known source of extinction and played an important role in the evolution of life on Earth. This comes as no surprise given the high number of objects orbiting in Earth's vicinity—asteroid impacts become a question of "when" rather than "if". This comes as no surprise given the high number of objects orbiting in Earth's vicinity—asteroid impacts become a question of "when" rather than "if". Under this motivation, thousands of potentially hazardous asteroids (PHAs) are monitored around the globe and naturally the question on the next step rose: What can we do when an incoming asteroid is identified as being on collision course with Earth? This is the motivation behind the field of Planetary Defence. Several mitigation strategies are being studied, but most of these have technology readiness levels still far off from the required. The one that comes closest is the kinetic impactor concept, which is the focus of this work. The concept is simple from a theoretical standpoint - a spacecraft (impactor) is sent into a collision course against the hazard NEO transferring enough momentum through kinetic energy to slightly change the NEO's trajectory and causes it to miss the Earth. The main component of kinetic impactors is the GNC, due to the necessity of a fully autonomous system. The major elements are the camera and the guidance, since they will allow detecting the target and modifying its trajectory to have a successful impact, respectively. This study includes the implementation of the required algorithms as well as an analysis of their performance in the impact scenario. The main variables that control the simulations have been examined in order to determine the design limits. The sensitivity analysis illustrates that the errors introduced in the model were chosen conservatively, inasmuch as the impact could be reached introducing higher uncertainties and errors in the systems considered. In general, even with the not excessively complex algorithms employed, the performance of the GNC is good. This study opens the door for the development of a kinetic impactor mission. • This research introduces the key parameters that shape a kinetic impactor. • Current state of the art and algorithms of the GNC system would allow the impact. • The performance is limited by the sensor employed and the image processing. • The study demonstrates that the use of an Unscented Kalman Filter is possible. • Results open the door for the development of a kinetic impact mission. [ABSTRACT FROM AUTHOR]

Nonuniform irradiance on the Photovoltaic (PV) array would cause mismatch problem and decrease the generated power. To relieve the negative effects of partial shading, reconfiguring the interconnection of the PV array is a favorable solution. However, the current reconfiguration techniques are usually limited by different corresponding factors, e.g., not suit for various instantaneous shade cases, the shortage of experts' knowledge or large amounts of data, easy coverage to local optima, and the strict assumptions for application. Therefore, in this paper a unsupervised adaptive PV array reconfiguration technique is proposed with a Divide and Conquer Q-Learning (DCQL) based array reconfiguration scheme designed to decrease the influence of partial shades. To testify the performance of the proposed PV array reconfiguration scheme, mathematic theoretical evaluation and electric characteristic analyses are made and compared with those obtained by the conventional Total-Cross-Tied (TCT) arrangement, SuDoKu connection, Genetic Algorithm (GA) based scheme, as well as some lately proposed Grasshopper Optimization Algorithm (GOA), Harris Hawks Optimizer (HHO), and Lo Shu (LS) technique based reconfiguration methods. Besides, additional estimation indices, including mismatch loss, fill factor, percentage power loss and percentage power enhancement are calculated with substantial analyses and comparisons. Subsequently, the application of the proposed scheme in real-time conditions is evaluated, with energy saving and income analysis estimated in comparison with other eminent challenger reconfiguration schemes. At last, a comprehensive qualitative comparison assessment, with respect to dispersion ability under partial shading conditions (PSC), sensor requirement, wiring complexity and capital investment is carried out and proves the versatility of proposed methodology. • States, actions and rewards are designed in the Q-Learning based reconfiguration. • The combination of divide and conquer principle fastens the decision making process. • The proposed reconfiguration is estimated better performance through variety contrasts. [ABSTRACT FROM AUTHOR]

Quantum computing has entered its mature life thanks to the availability of cloud-based Noisy Intermediate-Scale Quantum (NISQ) technologies. These devices allow quantum researchers and practitioners to design, develop and test quantum algorithms on actual hardware, paving the way toward new approaches in solving problems intractable by classical computers. However, in spite of these achievements in quantum technologies, the size of the problems that can be actually solved by quantum algorithms is still limited by the small number of qubits and the considerable noise in computation that still characterizes NISQ devices. As a consequence, there is a strong need of introducing innovative computing architectures able to interconnect a set of NISQ devices to increase the capabilities of current quantum computers of solving hard problems in a reliable way. In this paper, the concept of Distributed Noisy-Intermediate Scale Quantum (D-NISQ) is introduced as a reference computational model by which designing innovative frameworks where quantum devices interact to solve a complex problem by working cooperatively. In detail, the D-NISQ model consists of a hybrid and hierarchical architecture where classical and quantum processors interact to iteratively split up a problem into a collection of sub-problems, solve each one of them by means of a proper quantum algorithm, and fuse output quantum information so as to compute the final solution to the problem posed. As demonstrated by two case studies based on the well-known Grover's algorithm, a multi-threaded implementation of the D-NISQ reference model allows for greater reliability in solving problems by quantum computation. • Quantum computing has entered its mature life thanks to NISQ technologies. • NISQ devices are still characterized by critical issues in computation. • Distributed quantum architectures can improve the capabilities of NISQ devices. • D-NISQ is a reference model for distributed quantum computation. [ABSTRACT FROM AUTHOR]

Many-objective optimization problems (MaOPs) are challenging tasks involving optimizing many conflicting objectives simultaneously. Decomposition-based many-objective evolutionary algorithms have effectively maintained a balance between convergence and diversity in recent years. However, these algorithms face challenges in accurately approximating the complex geometric structure of irregular Pareto fronts (PFs). In this paper, an information entropy-driven evolutionary algorithm based on reinforcement learning (RL-RVEA) for many-objective optimization with irregular Pareto fronts is proposed. The proposed algorithm leverages reinforcement learning to guide the evolution process by interacting with the environment to learn the shape and features of PF, which adaptively adjusts the distribution of reference vectors to cover the PFs structure effectively. Moreover, an information entropy-driven adaptive scalarization approach is designed in this paper to reflect the diversity of nondominated solutions, which facilitates the algorithm to balance multiple competing objectives adaptively and select solutions efficiently while maintaining individual diversity. To verify the effectiveness of the proposed algorithm, the RL-RVEA compared with seven state-of-the-art algorithms on the DTLZ, MaF, and WFG test suites and four real-world MaOPs. The results of the experiments demonstrate that the suggested algorithm provides a novel and practical method for addressing MaOPs with irregular PFs. • A novel RL-RVEA addresses many-objective optimization with irregular pareto fronts. • A reinforcement learning-based adaptive reference vector to guide the direction of convergence. • A scalarization approach preserves the diversity of solutions for next generation. • The RL-RVEA outperforms seven advanced many-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

In the era of rapid paper publications in various venues, automatic citation recommendations would be highly useful to researchers when they write papers. Local citation recommendation aims to recommend possible papers to cite given local citation contexts. Previous work mainly computes the similarity score between citation contexts and cited papers on a one-to-one basis, which is quite time-consuming. We train a pair of neural network encoders that map citation contexts and all possible cited papers to the same vector space, respectively. After that, we index the positions of all cited papers in the vector space. This makes our process for searching recommended papers considerably faster. On the other hand, existing methods tend to recommend papers that are highly similar to each other, which makes recommendations lack diversity. Therefore, we extend our algorithm to perform multi-topic recommendations. We generate multi-topic training examples based on the index we mentioned earlier. Furthermore, we specially design a multi-group contrastive learning method to train our model so that it can distinguish different topics. Empirical experiments show that our model outperforms previous methods by a wide margin. Our model is also light weighted and has been deployed online so that researchers can use it to obtain recommended citations for their own paper in real-time. • Proposed FLCR algorithm with sentence-transformer & k-d tree. • Introduced multi-topic citation recommendation for diverse contexts. • Developed large-scale dataset with 1.7 million citation contexts for evaluation. • Demonstrated significant performance improvement over previous methods. • Deployed demo for real-time citation recommendations. [ABSTRACT FROM AUTHOR]

Intelligent Fault-Tolerant Control (IFTC) refers to the applications of machine learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The overall goal of the FTC is to accommodate defects in the system components while they are in use and maintain stability with little to no performance reduction. These systems are crucial for mission-critical and safety-related applications where the safety of people is at stake and service continuity is crucial. In this review paper, a systematic study has been done for the development of FTC with machine learning, deep learning, and transfer learning algorithms. The challenges and limitations faced with their possible solutions through machine learning theories for the IFTC model are lined up. This paper guides researchers on the different possible types of machine learning algorithms and their advanced forms like deep learning and transfer learning. The differences among these are highlighted by the challenges and limitations of each. The paper is significant such that most of the important literature references from the Scopus database particularly related to important electrical and mechanical industrial problems have been discussed to guide the researchers who want to apply IFTC for specific industrial problems, being the research gap. Finally, future research directions for the development of IFTC are highlighted. [ABSTRACT FROM AUTHOR]

Identifying the abnormal samples from a data set and determining their type are two key tasks of anomaly detection. However, the existing anomaly detection algorithms are generally faced with the defects of weak generalization ability and insufficient interpretation, the core reason of which is that they cannot mine specific features for different abnormal types. In this paper, a new anomaly detection algorithm aiming at feature selection for different abnormal types is developed. Inspired by self-supervised learning, we take the stationarity of variance changes of abnormal score similarity as a pretext task and combine it with wrapped search method. Then, the features and the corresponding parameters for different abnormal types can be screened to apply to the downstream task of anomaly integration detection. To verify the efficiency of the new algorithm, we conduct two sets of experiments to compare the new algorithm with 11 classical anomaly detection and 3 clustering anomaly detection algorithms on the data sets WDBC, WPBC and Wilt from DAMI database with the evaluation measures P@n , Adj- P@n , AP, Adj-AP and AUC. The experiment results show that, both in the identification and classification on abnormal samples, all performance measures of the new algorithm are explicitly better than that of the contrastive algorithms. Also, we apply the new algorithm to the Chinese auto insurance market, and find that the results can help managers to identify the main patterns of fraudulent claims and to summarize the feature combinations of fraud behaviors. In general, the new algorithm developed in this paper has the following advantages compared with traditional algorithms: 1) It can directly capture abnormal features and realize effective recognition of abnormal types, which effectively bridge the gap between abnormal judgement and feature screening. 2) Automatic screening of abnormal features can be completed under the condition of self-updating learning optimal strategy. 3) Only a few features are extracted from all features to reveal the abnormal characteristics, which significantly improves the interpretability and generalization ability of the algorithm and its results. In a word, through the novel self-supervised design method, feature screening is skillfully integrated into the anomaly detection task, which may provide a new way for anomaly detection research. [ABSTRACT FROM AUTHOR]

In this paper, to investigate the problem of radio wave propagation in complex environments, a new algorithm is proposed based on the equivalence principle algorithm and the domain decomposition method to rapidly and accurately predict electromagnetic scattering and radio wave propagation in half-space. First, equivalent surfaces are introduced to decompose the obstacle region and replace the scattering effect of obstacles within the equivalent surfaces with equivalent input and output electromagnetic currents. Then, the operator S, representing the relationship between the input and output equivalent electromagnetic currents on the equivalent surfaces, and the operator T, representing the interactions between the equivalent electromagnetic currents on each equivalent surface, are derived; this allows the calculation process of the overall spatial field in a complex environment to be simplified to the iterative calculation process of operators S and T. To reduce the complexity of calculating S, a neural network embedded with physical information is introduced to replace the calculation process. Finally, the training results are applied to an example and compared with the theoretical numerical results yielded by the method of moments in different cases with obstacles. The results reveal that the new method proposed in this paper is highly accurate and achieves a substantial improvement in calculation speed. Therefore, our proposed algorithm provides a new and reliable method for calculating the spatial field in complex environments. • The EPA is introduced to use equivalent surfaces to replace the scattering inside it. • The operators S and T are introduced into the process of solving the field. • A neural network with physical information embedded is applied to calculate the S. • Random undulating terrains are generated by Karhunen–Loève expansion. • MoM providing a good foundation for training is used to generate dataset. [ABSTRACT FROM AUTHOR]

Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains. Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used. Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies. The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research. [Display omitted] • Application of sensors and artificial intelligence to analyse ankle biomechanics. • Lab sensors predict parameters, paving the way for post-modern rehabilitation. • Field devices show potential for ankle injury prediction. • Focus on streamlined smart sensors and effective data management in the future. [ABSTRACT FROM AUTHOR]

With the advent of the post-pandemic era, the global economic situation has tightened, and the economic benefits of container ports have also been affected. This paper attempts to build a novel berth-tugboat-quay crane joint scheduling method for container ports to reduce the economic cost of container ports. Firstly, from the perspective of the economic cost of container ports, considering random economic factors, this paper proposes a new berth-tugboat-quay crane joint dispatch optimization model, namely E-BUQC, by taking the minimum time cost of vessels in port, the cost of additional freight distance for trucks, the cost of tugboat operation, and the minimum operation cost of quay cranes as the objective function. Then, in order to obtain a relatively high-quality solution, based on chaotic mapping and quantum computing, this paper constructs a new chaotic quantum adaptive satin bower bird optimizer algorithm, namely CQASBO, formulates exclusive coding rules for the E-BUQC model, develops a feasible integer algorithm, and proposes a new joint scheduling solution method of berth-tugboat-quay crane, named E-BUQC_CQASBO. Subsequently, based on the simulation examples of two ports in northern China, the new solution method is tested. The simulation test results show that the E-BUQC model obtains an appropriate solution; The CQASBO constructed in this paper has better performance than other traditional comparison algorithms selected in this paper and can provide a better solution when solving the E-BUQC model. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a distributed machine learning method to protect users' privacy and security. It currently faces the following two problems (McMahan et al., 2017): (1) The performance of the global model degrades when dealing with Non-Independent Identically Distributed (Non-IID) data. Other existing classical methods do not have a rigorous theory based on error processing ; (2) In the process of global model aggregation, the classical FL algorithm either directly averages local models or solely considers the proportion of local data to assign weights to the models, without accounting for the discrepancies between the local models. In this paper, a federated aggregation algorithm Federated Maximum Mean Discrepancy (FedMMD) is proposed to address the deviation between the local models and Non-IID. First of all, this paper utilizes the Dilated Convolution Meet Transformer (DCMT) model for local model feature extraction. This approach aims to capture more feature information and minimize the impact of Non-IID scenarios. Secondly, the learning stability of the local model data participating in the aggregation is compared with Maximum Mean Discrepancy (MMD). The weights of the local models involved in the aggregation are determined using the SKNQ (Student–Keuls–Newman-Q) method and the entropy weight method. The SKNQ method calculates and compares the MMD across multiple clients, while the entropy weight method assigns different weights to clients based on their deviations. On the standard dataset, the FedAvg algorithm (McMahan et al., 2017), the FedProx algorithm (Li et al., 2020) and the FedMMD algorithm are compared. The experimental results demonstrate that the FedMMD algorithm, used in training the global model, not only enhances the accuracy of learning IID and Non-IID data but also improves the generalization capability of the global model. • An DCMT model is proposed to alleviate the model fluctuations caused by NonIID data. • An MMD method is proposed to compared horizontally before the model is aggregated. • Entropy weight method and the SKNQ method are combined to allocate local models' weight when they are fused into global model. [ABSTRACT FROM AUTHOR]

• This paper suggests a grating Moiré fringe-based error correction approach that uses an artificial neural network (ANN) and the quartile method (QM). • The approach uses QM for data cleaning operations to eliminate outliers and creates the ANN model with a structure of fully connected layer-normalization layer-fully connected layer for regression training in order to achieve lightweight and stability. • The proposed QM-ANN error correction model's mean absolute error (MAE) can reach 3.3 nm once the QM data has been cleaned and the ANN model has been trained. Many interference factors of the complex and changeable working environment of the grating sensor are the main factors affecting the accuracy; a grating Moiré fringe error correction model based on the quartile method (QM) and artificial neural network (ANN) is proposed in this paper. Firstly, the grating sensor's signals in the working process are collected, and three kinds of interference signals (temperature, humidity and vibration) from the grating sensor working environment are collected simultaneously. Secondly, the QM is used to detect outliers from the collected data and delete data outliers directly. Subsequently, the processed data are input into the ANN model for training. The model is lightweight and stable, because it adopts the cascaded structure of full connection layer-normalization layer-full connection layer, which does not need deploy the complex convolution layer module etc. Finally, the corrected data are output after being processed by the QM-ANN error correction model. The experimental results show that the mean absolute error (MAE) of the proposed QM-ANN error correction model is better than QM-convolutional neural network (CNN), QM-long short-term memory (LSTM) and ANN, CNN, LSTM models without QM. It shows that the proposed model has better robustness and practicability. [ABSTRACT FROM AUTHOR]

In numerous domains, there is ample evidence indicating that a significant portion of real-world data exhibits temporal variations, such as medical research and meteorological studies. Particularly in the era of big data, not only does the size of data change dynamically but also its dimensions at an unprecedented pace. Consequently, employing traditional methods to handle such dynamic data becomes highly impractical. To address this limitation, this paper proposes an incremental feature selection algorithm tailored for dynamic feature variations. For scenarios involving an increase in features, the novel algorithm efficiently identifies a target subset with effective features. In order to showcase the efficacy of the proposed algorithm, this paper conduct experiments using four commonly used classifiers and five UCI data sets. The experimental results further validate both the feasibility and efficiency of the new approach. [ABSTRACT FROM AUTHOR]

In recent years, the reduced biquaternion algebras have been widely used in color image processing problems and in the field of electromagnetism. This paper studies eigen-problems of reduced biquaternion matrices by means of a complex representation of a reduced biquaternion matrix and derives new algebraic algorithms to find the eigenvalues and eigenvectors of reduced biquaternion matrices. This paper also concludes that the number of eigenvalues of an n × n reduced biquaternion matrix is infinite. In addition, the proposed algebraic algorithms are shown to be effective in application to a color face recognition problem. • The eigen-problems of reduced biquaternion matrices are further studied based on the complex representation form. • Propose new algebraic algorithms for finding the eigenvalues and the eigenvectors of a reduced biquaternion matrix. • An n × n reduced biquaternion matrix has infinite eigenvalues. • There are multiple eigenvalues corresponding to the same eigenvector of a reduced biquaternion matrix. • The proposed method is more comprehensive and can find more eigenvalues of a reduced biquaternion matrix. [ABSTRACT FROM AUTHOR]

The Peaceman–Rachford splitting method (PRSM) has been widely studied in recent years because of its excellent numerical performance, especially with inertial acceleration. In this paper, we propose a multi-step inertial strictly contractive PRSM (shortly, MISCPRSM) for solving convex optimization problems, which has not been studied before. This paper aims to give an exhaustive analysis of the value relationship between multiple inertial parameters. The second subproblem utilizes an additional customized indefinite proximal term in order to obtain a closed-form solution. The global convergence and complexity result of the introduced method are analyzed by using variational inequality and basic inequalities. Finally, through simulation and computed tomography experiments, the effectiveness and robustness of the MISCPRSM are proved, and the detailed values of inertial parameters are given. [ABSTRACT FROM AUTHOR]

In this paper, we first summarize the algorithm presented in Blasco and Pérez-Díaz (2014) for computing the generalized asymptotes of algebraic curves implicitly defined. This algorithm is based on the computation of Puiseux series. The main and very important contribution of this paper is a new and efficient method that allows to easily compute all the generalized asymptotes of an algebraic plane curve implicitly defined by just solving a triangular system of equations. The method can be easily generalized to the case of algebraic curves implicitly defined in the n -dimensional space. [ABSTRACT FROM AUTHOR]

Aiming at the problems of long reconstruction time, high sampling rate and poor imaging effect caused by traditional illumination speckle in ghost imaging reconstruction, this paper proposes to use ordered orthogonal Hadamard derived speckle as the illumination speckle series of ghost imaging to reduce the measurement times and improve the quality of reconstructed images. In addition, the alternate direction multiplier method is introduced into the image reconstruction algorithm to further improve the imaging performance of ghost imaging. Finally, the method used in this paper is evaluated objectively, and the evaluation results show that the combination of Hadamard derived speckle and alternating direction multiplication has better imaging effect, and can obtain high-quality reconstructed images under low sampling conditions. It is expected that this method can be applied to target detection and recognition, improve detection and recognition ability, and help users get more useful information. • ADMM algorithm is used to solve the underdetermined equations in ghost imaging. • Reconstruction Hadamard combined with ADMM improves the reconstruction effect. • The proposed method has better reconstruction results at low sampling rates. [ABSTRACT FROM AUTHOR]