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Critical chain buffer management (CCBM) has been extensively studied in recent years. This paper investigates a new formulation of CCBM, the multimode chance-constrained CCBM problem. A flow-based mixed-integer linear programming model is described and the chance constraints are tackled using a scenario approach. A reinforcement learning (RL)-based algorithm is proposed to solve the problem. A factorial experiment is conducted and the results of this study indicate that solving the chance-constrained problem produces shorter project durations than the traditional approach that inserts time buffers into a baseline schedule generated by solving the deterministic problem. This paper also demonstrates that our RL method produces competitive schedules compared to established benchmarks. The importance of solving the chance-constrained problem and obtaining a project buffer tailored to the desired probability of completing the project on schedule directly from the solution is highlighted. Because of its potential for generating shorter schedules with the same on-time probabilities as the traditional approach, this research can be a useful aid for decision makers. [ABSTRACT FROM AUTHOR]

Ethical morality is one of the significant issues in self-driving cars. The paper provides a newer approach to solve the ethical decision problems in self-driving cars until there is no concrete ethical decision to all problems. This paper gives a two-way approach to solve a problem, with first being the mapping of problem to the solution already known or which has a fixed set of solutions and action priorities defined to a problem previously. Now, if no solution is found or mapping is unsuccessful, then the second stage activates, where the solution from Deep Q-learning model is calculated. It estimates the best Q value and returns that solution or action which maximizes the reward at that instance. The reward function is designed with decreasing priorities and acts accordingly, where the users can change or define their priorities if needed. The case study and results show that the solution that is present in the paper will lead to solving ethical morality problems in self-driving cars up to a great extent. [ABSTRACT FROM AUTHOR]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]

The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are the focus of this special issue. This issue compiles extensively revised and improved versions of the top papers from the workshop on Mathematical Modeling and Problem Solving at PDPTA'23, the 29th International Conference on Parallel and Distributed Processing Techniques and Applications. Covering fundamental research in matrix operations and heuristic searches to real-world applications in computer vision and drug discovery, the issue underscores the crucial role of supercomputing and parallel and distributed computing infrastructure in research. Featuring nine key studies, this issue pushes forward computational technologies in mathematical modeling, refines techniques for analyzing images and time-series data, and introduces new methods in pharmaceutical and materials science, making significant contributions to these areas. [ABSTRACT FROM AUTHOR]

Using attribute graphs for node embedding to detect community structure has become a popular research topic. However, most of the existing algorithms mainly focus on the network structure and node features, which ignore the higher-order relationships between nodes. In addition, only adopting the original graph structure will suffer from sparsity problems, and will also result in suboptimal node clustering performance. In this paper, we propose a hypergraph network embedding (HGNE) for community detection to solve the above problems. Firstly, we construct potential connections based on the shared feature information of the nodes. By fusing the original topology with feature-based potential connections, both the explicit and implicit relationships are encoded into the node representations, thus alleviating the sparsity problem. Secondly, for integrating the higher-order relationship, we adopt hypergraph convolution to encode the higher-order correlations. To constrain the quality of the node embedding, the spectral hypergraph embedding loss is utilized. Furthermore, we design a dual-contrast mechanism, which draws similar nodes closer by comparing the representations of different views. This mechanism can efficiently prevent multi-node classes from distorting less-node classes. Finally, the dual-contrast mechanism is jointly optimized with self-training clustering to obtain more robust node representations, thus improving the clustering results. Extensive experiments on five datasets indicate the superiority and effectiveness of HGNE. [ABSTRACT FROM AUTHOR]

To solve the problem of oscillation instability in permanent magnetic synchronous generator (PMSG)-based wind power connected systems during low-voltage ride through (LVRT) process, a parameter adjustment strategy based on interaction energy path optimization is proposed in this paper. Firstly, a modular state-space model of PMSG under fault transient conditions is constructed, and the system is divided into five subsystems. Then, the dynamic energy function of subsystems reflecting the oscillation stability of the system is derived. Based on that, the dynamic energy flow path is described considering the introduction of LVRT control. On this basis, the interaction energy between LVRT control links and subsystems is analyzed, and the coupling mechanism of voltage support and damping characteristics in the LVRT process is explained. Further, aiming at the optimal change rate of the total interaction energy in the LVRT process, the adjustment strategy of LVRT control parameters is constructed to meet voltage and damping requirements. Finally, a PMSG-connected system model is built on the MATLAB/Simulink platform to verify the effectiveness of the adjustment strategy. The results show that the proposed method can effectively improve the damping level under the fault transient condition, as well as supporting system voltage. [ABSTRACT FROM AUTHOR]

The gazelle optimization algorithm (GOA) is an iterative optimization method inspired by the agile movements of gazelles, employing adaptive step sizes and velocity adjustments for rapid convergence in continuous search spaces. However, GOA tends to lack diversity, leading to issues like local minima trapping and premature convergence. This paper addresses these limitations by introducing dynamic opposition-based learning (OBL) and incorporating a balanced sine-control-logistic chaotic mapping system, resulting in the improved GOA (IGOA). Dynamic OBL accelerates the search process, improving learning and selecting superior candidate solutions, while chaotic mapping in chaotic local search widens ranges for exploration and local optima escape. Evaluating IGOA against seven algorithms, including GOA and others, across 31 general test functions, the results consistently showcase IGOA's superior efficiency in achieving solutions closest to optima, early convergence, and hit rate when compared to alternative algorithms. [ABSTRACT FROM AUTHOR]

Affordance grounding aims to locate objects' "action possibilities" regions, an essential step toward embodied intelligence. Due to the diversity of interactive affordance, i.e., the uniqueness of different individual habits leads to diverse interactions, which makes it difficult to establish an explicit link between object parts and affordance labels. Human has the ability that transforms various exocentric interactions into invariant egocentric affordance to counter the impact of interactive diversity. To empower an agent with such ability, this paper proposes a task of affordance grounding from the exocentric view, i.e., given exocentric human-object interaction and egocentric object images, learning the affordance knowledge of the object and transferring it to the egocentric image using only the affordance label as supervision. However, there is some "interaction bias" between personas, mainly regarding different regions and views. To this end, we devise a cross-view affordance knowledge transfer framework that extracts affordance-specific features from exocentric interactions and transfers them to the egocentric view to solve the above problems. Furthermore, the perception of affordance regions is enhanced by preserving affordance co-relations. In addition, an affordance grounding dataset named AGD20K is constructed by collecting and labeling over 20K images from 36 affordance categories. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. The code is available via: github.com/lhc1224/Cross-View-AG. [ABSTRACT FROM AUTHOR]

Existing methods for filter pruning mostly rely on specific data-driven paradigms but lack the interpretability. Besides, these approaches usually assign layer-wise compression ratios automatically only under given FLOPs by neural architecture search algorithms or just manually, which are short of efficiency. In this paper, we propose a novel interpretable task-inspired adaptive filter pruning method for neural networks to solve the above problems. First, we treat filters as semantic detectors and develop the task-inspired importance criteria by evaluating correlations between input tasks and feature maps, and observing the information flow through filters between adjacent layers. Second, we refer to the human neurobiological mechanism for the better interpretability, where the retained first layer filters act as individual information receivers. Third, inspired by the phenomenon that each filter has a deterministic impact on FLOPs and network parameters, we provide an efficient adaptive compression ratio allocation strategy based on differentiable pruning approximation under multiple budget constraints, as well as considering the performance objective. The proposed method is validated with extensive experiments on the state-of-the-art neural networks, which significantly outperforms all the existing filter pruning methods and achieves the best trade-off between neural network compression and task performance. With ResNet-50 on ImageNet, our approach reduces 75.49% parameters and 70.90% FLOPs, only suffering from 2.31% performance degradation. [ABSTRACT FROM AUTHOR]

Function generation synthesis of mechanisms can be considered as the design of correlation of crank angles and dead dead-center position design. These two problems have been clearly defined and solved separately. But some problems may require both correlation of crank angles and dead dead-center design at different configurations. Such problems are called mixed function generation problems. In this paper, an overview of these mixed function generation problems for the planar four-bar mechanism are given and the problems are solved analytically or semi-analytically. Except three of them, all presented mixed function generation problem formulations are novel. The solutions of all problems including three positions for the four-bar mechanism and the solution of a problem including four positions for a four-bar mechanism are addressed. All problems are first reduced to a univariate equation and a fast solution is found. Thus, link lengths can be found quickly by changing the problem definition problems, and several design iterations can be performed in a short time. Numerical solutions of all problems have been demonstrated using Excel. [ABSTRACT FROM AUTHOR]

The sparse triangular solver (SpTRSV) is mostly used for scientific and engineering applications. The structured-grid triangular solver of regular dependencies (STRSV) is a special kind of SpTRSV. Some general SpTRSVs that disregards the regularity of the matrix are unsuitable for solving this problem. This paper proposes an efficient parallel algorithm for STRSV on the SW26010 (a kind of China independently designed many-core processors), namely swStructTRSV. The algorithm makes full use of the fine-grained and low latency communication characteristics of the SW26010 to reduce the waiting time for synchronization, maximizes the regularity of access to improve memory access bandwidth, and achieves overlap between memory access and computation simultaneously. Moreover, the idea of the algorithm can be extended to incomplete LU factorization (ILU factorization) because of consistent dependencies. The experimental results on a core group(8 * 8 network composed of 64 cores) of SW26010 show that swStructTRSV can achieve an average speedup of over 30 in the sequential version. swStructTRSV on SW26010 achieves solving speedups of 2.2 and 6.3 over the fast STRSV (fSpTRSV) previously implemented on SW26010 and MKL on Intel Xeon Gold 6132, respectively. swStructTRSV significantly outperforms cuSparse on NVIDIA TITAN RTX in terms of overall execution time. [ABSTRACT FROM AUTHOR]

The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm's ability to escape from local optimal. To verify IMRFO's performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms. [ABSTRACT FROM AUTHOR]

Drones play an important role in modern cities, they bring convenience but also bring corresponding risks. Falling drones can cause casualties or damage to urban facilities and serious property damage, which increases the third-party risk problem. An effective way to reduce these third-party risks is to avoid high-risk areas in path planning, but most current path planning methods are optimized to minimize flight distance, and third-party risk costs are rarely considered. In this paper, a comprehensive risk-cost UAV path planning method is proposed, which evaluates urban risk by establishing a third-party risk model that incorporates obstacle risk, death risk, and property loss risk. This paper proposes a Min-cost A* algorithm based on city risk assessment, and smooths the generated low-risk path through the improved Floyd algorithm. The results show that the path planning method can effectively reduce the risk in the flight path, improve the reliability of the UAV flight path in the urban environment, and solve the problem of planning the third-party risk path of the UAV. [ABSTRACT FROM AUTHOR]

Due to the increasing complexity of decision problems, many managers employ multiple experts to reach a good decision in a group decision making. Now, if there is ambiguity in the evaluation of experts, the use of fuzzy numbers can be useful for each expert. In these situations, the use of hesitant fuzzy numbers (HFNs) which consists of several fuzzy numbers with special conditions can be suggested. HFNs are as an extension of the fuzzy numbers to take a better determining the membership functions of the parameters by several experts. Because of simple and fast calculations, in this paper, we use triangular HFNs in the pairwise comparison matrix of analytic hierarchy process by opinions of a group of decision makers in a hesitant fuzzy environment. We define consistency of the hesitant fuzzy pairwise comparison matrix and use the arithmetic operations on the HFNs and a new method of comparing HFNs to get the hesitant fuzzy performance score. By using score function to hesitant fuzzy score we can get a final score for alternatives. Finally, a practical example is provided to show the the effectiveness of this study. The obtained results from this paper show that new method can get a better answer by keeping the experts' opinions in the process of solving the problem. [ABSTRACT FROM AUTHOR]

Since 2007, methods that utilize side-channel data to detect hardware Trojan (HT) problems have been widely studied. Machine learning methods are widely used for hardware Trojan detection, but with the development of integrated circuits (ICs), better results are usually obtained using deep learning methods. In this paper, we propose an architecture inspired by Residual-Block and Dense-Block and combine it with SE Attention Mechanism, which we named the Res-Dense-SE-Net network. By combining residual connectivity, dense connectivity, and attention mechanism, the Res-Dense-SE-Net network can enjoy the advantages of these three network architectures at the same time, which can improve the expressiveness and performance of the model. The Res-Dense-SE-Net network can capture the key features in the image better, and it can solve the problems of gradient vanishing and feature transfer efficiently, which can in turn improve the classification accuracy and the generalization ability of the model. Based on the publicly available AES series of hardware Trojans from TrustHub and the publicly available hardware Trojan-side channel data by Faezi et al., we evaluate the effectiveness of the method proposed in this paper. The experimental results show that when a single Trojan exists, the method proposed in this paper has a high accuracy rate; and when multiple types of hardware Trojans exist at the same time and need to be categorized, the categories of hardware Trojans can also be effectively identified, and the categorization accuracy is high compared with the existing deep learning methods. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time and fixed-time consensus tracking of Euler–Lagrange systems (ELSs) under a directed graph is studied. Firstly, note that the settling time of finite-time consensus tracking (Fin-TCT) is related to the initial values, while the settling time of fixed-time consensus tracking (Fix-TCT) is independent of the initial values. The reason for considering both Fin-TCT and Fix-TCT is to compare them in terms of the settling time. Then, this paper designs the distributed estimated-based hierarchical control algorithms to solve the above problems. By Lyapunov stability analysis, if the coupling gains are properly designed, the Fin-TCT and Fix-TCT of the ELSs with external disturbances can be achieved. Finally, simulation results are presented to demonstrate the feasibility of the developed control algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the problem of multi-object detection such as target occlusion and tiny targets in road scenes, this paper proposes an improved YOLOv5 multi-object detection model based on ML-AFP (multi-level aggregation feature perception) mechanism. Since tiny targets such as non-motor vehicle and pedestrians are not easily detected, this paper adds a micro target detection layer and a double head mechanism to improve the detection ability of tiny targets. Varifocal loss is used to achieve a more accurate ranking in the process of non-maximum suppression to solve the problem of target occlusion, and this paper also proposes a ML-AFP mechanism. The adaptive fusion of spatial feature information at different scales improves the expression ability of network model features, and improves the detection accuracy of the model as a whole. Our experimental results on multiple challenging datasets such as KITTI, BDD100K, and show that the accuracy, recall rate and mAP value of the proposed model are greatly improved, which solves the problem of multi-object detection in crowded road scenes. [ABSTRACT FROM AUTHOR]

The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m3/s, which is much lower than the safe overflow flow rate of 22,000 m3/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs. [ABSTRACT FROM AUTHOR]

This paper presents rank-awareness algorithms to solve sparse blind deconvolution using modulated input. We consider sparse blind deconvolution as a rank-one column-sparse matrix recovery problem, so the proposed algorithms can use both the rank-one property and the sparsity of the unknowns. Unknown input s is first multiplied by a random sign sequence r and then convolved with an arbitrary filter h to obtain the measurements y . The unknown signal s is assumed to have a sparse representation. Sparse blind deconvolution using modulated input has unique applications, such as the blind calibration of the random demodulation system. When the number of measurements has satisfied certain conditions, blind deconvolution can be solved without considering signal sparsity. This paper mainly studies how to use signal sparsity to reduce the number of measurements required for sparse blind deconvolution. We propose two methods to solve this problem. The first method uses the ℓ 1 -norm regularization to promote the unknown signal to iterate in the direction of sparsity. The second method transforms the sparse blind deconvolution problem into a rank-one constrained block-sparse signal recovery problem, and we propose the rank-awareness sparse blind demodulation algorithm to solve it. Our proposed methods could effectively reduce the number of measurements required for sparse blind deconvolution. Under certain conditions, our proposed sparse blind deconvolution algorithms required 320 and 160 measurements, while 400 measurements were required when signal sparsity was not considered. The simulation results verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Purpose: The Social Difficulties Inventory (SDI-21) assesses everyday problems experienced by cancer patients, including difficulties with self-care, work and relationships. Early development and psychometric evaluation studies have validated the SDI-21 for computer administration. However, several recent studies have administered the SDI-21 on paper. We sought to test the score equivalence of electronic and paper versions of the SDI-21. Methods: A randomised two-arm crossover trial in a sample of cancer patients with varied diagnoses. Patients completed electronic (via the internet) and paper versions of the SDI-21, with half randomly assigned to complete the electronic version first ( n = 51) and half the paper version first ( n = 60). Patients were asked to complete both versions at home, within 2 weeks. Analyses were performed for the SDI-21 summary score and three subscales. Results: Score distributions and internal reliabilities for the paper and electronic versions were highly similar. There were no significant differences between mean summary or subscale scores for the two administration modes. All mean score differences (all <0.25 of a scale point) were well below the SDI-21's established minimally important differences, and all 95 % confidence intervals were narrow and included zero. Intraclass correlations between paper and electronic scores were uniformly high and significant (all ≥0.85) and above the standard acceptable level of reliability. Conclusions: Paper and electronic versions of the SDI-21 can be considered equivalent and used interchangeably. This is important because, despite the growth of electronic formats, paper versions are currently still necessary to ensure inclusive use of the SDI-21 with representative samples. [ABSTRACT FROM AUTHOR]

To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function ( f 1 - f 13 ) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems. [ABSTRACT FROM AUTHOR]

To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function ( f 1 - f 13 ) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC), as the extension of the mobile cloud, has been widely investigated. However, in most existing VFC works, the scenario with point-of-interests (PoIs) is rarely considered, which can lead to underutilization of vehicle computing resources and overloading of the computing load for roadside units (RSUs). Therefore, this paper focuses on improving the quality of services for PoI-based VFC by utilizing the computation capabilities of vehicles to assist the RSU. An NP-hard problem is formulated to maximize the number of tasks completed within the duration of delay constraints. To solve the problem, a delay-aware task offloading algorithm, named Delay-Aware Task Offloading (DTO), is proposed. DTO preferentially operates on tasks with tighter delay constraints. To minimize the completion delay for each offloaded task, DTO selects the set of service vehicles to perform tasks. Moreover, a vehicle-resource-aware task offloading algorithm, named Vehicle-Resources-Aware Task Offloading (VRTO), is proposed to maximize the number of tasks completed within the duration of delay constraints by efficiently utilizing the available resource of the vehicle. Specifically, VRTO allocates the data to each service vehicle according to the service configuration of vehicles and the delay constraints of tasks. Finally, extensive experiments are carried out to evaluate the effectiveness of the proposed algorithms. The performance is successfully improved by more than 30% compared with the baselines in terms of the number of completed tasks. [ABSTRACT FROM AUTHOR]

Identifying the location of the sensor nodes is a challenging aspect of Wireless Sensor Networks (WSNs) in a distributed environment. It is vital to understand where data and events come from when sensor nodes collect data and report on them. This paper uses a nature-inspired Jaya algorithm to estimate the location coordinates of target nodes in a distributed architecture of WSNs. The Jaya algorithm handles the nonlinear optimization problem of node localization in a randomly distributed sensor node configuration. The range-based, Received Signal Strength Indicator method is used for the distance estimation. MATLAB software is used to assess the proposed work, and a comparison is made with the Particle Swarm Optimization, Krill Herd Optimization, and Salp Swarm Algorithm based node localization algorithms in terms of localization error and computation time. The localization error analysis is done for the different numbers of anchor nodes and different values of degree of irregularity to verify the effectiveness of the proposed work. [ABSTRACT FROM AUTHOR]

Removal of volatile organic compounds (VOCs) from the air has been an important issue in many industrial fields. Traditionally, the operation of VOCs removal systems has relied on fixed operating conditions determined by domain experts based on their expertise and intuition. In practice, this manual operation cannot respond immediately to changes in the system environment. To facilitate the autonomous operation of the system, the operating conditions should be optimized properly in real time to adapt to the changes in the system environment. Recently, optimization frameworks have been widely applied to real-world industrial systems across various domains using different approaches. The primary motivation for this study is the effective implementation of an optimization framework targeting a VOCs removal system. In this paper, we present a data-driven autonomous operation method for optimizing the operating conditions of a VOCs removal system to enhance the overall performance. An optimization problem is formulated with the decision variables denoting the parameters associated with the operating condition, the environmental variables representing the measurements for the system environment, the constraints specifying the control ranges of the parameters, and the objective function representing the system performance as determined by the operating conditions and environment. Using the previous operation data from the system, a neural network is trained to model the system performance as a function of the decision and environmental variables to approximate the objective function. For the current state of the system environment, the optimal operating condition is derived by solving the optimization problem. A case study of a targeted VOCs removal system demonstrates that the proposed method effectively optimizes the operating conditions for improved system performance without intervention from domain experts. [ABSTRACT FROM AUTHOR]

Recently, sparse signal and image recovery have shown significant promise in different biomedical fields. In this paper, we introduce a novel method to recover structured-sparse biomedical signals and images in a hierarchical Bayesian framework. The proposed method promoted sparse distribution using a Bernoulli–Laplace prior. In addition to sparse prior, we consider cluster prior on sparsity patterns. To implement the Bayesian inference, we use an MCMC technique to sample the target posterior distribution. Using generated samples, the model parameters and hyperparameters are estimated in an unsupervised scheme. Finally, the estimation procedure has been completed using the MAP estimator. Our method solves the inverse problem automatically without needing to alter the parameters manually. We have used synthetic data sets with several sparsity scenarios to explore the proposed algorithm, which outperforms the existing recovery methods, e.g., CoSaMP, BCS, EBSBL, and Cluss. Finally, through experiments using different real-life biomedical data (EMG and ECG signals and MR images), the superiority of the proposed method is demonstrated. This study demonstrates that using a combination of Bernoulli–Laplace and structured prior on sparsity patterns can recover structured-sparse biomedical signals and images precisely. [ABSTRACT FROM AUTHOR]

The traditional variational mode decomposition (VMD) algorithm causes under-decomposition and over-decomposition problems when performing signal decomposition. Many studies have addressed various optimization algorithms. When the optimization parameters are set too small, the best decomposition cannot be found. On the other hand, setting too large parameters leads to a waste of computational resources. Therefore, an improved VMD algorithm is proposed in this paper, which effectively solves the above problems by establishing an adaptive method for choosing the values of decomposition parameters and its iteration termination condition. In order to meet the monitoring data transmission requirement, a hybrid compression scheme based on the improved VMD is proposed. The monitoring signal is effectively decomposed by the improved VMD algorithm, and then thresholded by the lifting wavelet transform (LWT) method, and further compressed by combining quantization as well as entropy encoding methods. It is experimentally verified that its compression scheme can effectively improve compression performance and has a high signal-to-noise ratio, providing an effective solution for data transmission in equipment monitoring. [ABSTRACT FROM AUTHOR]

The scarcity of defect samples and the imbalance of defect types lead to the fact that achieving defect detection in color-patterned fabrics remains a challenge in the textile industry. Defect detection methods based on traditional auto-encoder are difficult to solve the problem of defect detection in complex color-patterned fabrics. In order to solve the problem of weak feature representation of traditional auto-encoders, this paper proposes an unsupervised method based on triplet attention multi-scale U-shape denoising convolutional auto-encoder (TA_MSUDCAE). The method further enhances the feature representation capability of the auto-encoder by introducing a triplet attention mechanism based on utilizing the multi-scale information of the image. Firstly, the defect-free samples that are added with Gaussian noise are used as inputs to the model in the training stage. The model is trained to reconstruct and repair the defective regions. Secondly, the test image is input to the trained model to obtain a normal reconstructed image, and the residual image is obtained by calculating the difference between the input image and the reconstructed image. Finally, the defect detection and localization results are obtained by threshold segmentation and mathematical morphology processing of the residual image. A large number of experiments have been carried out on a variety of representative color-patterned fabrics, and the results prove the effectiveness of the proposed method in fabric defect detection. [ABSTRACT FROM AUTHOR]

Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchmark functions for experiments. By comprehensive comparison of the test function results and Friedman test results, the results obtained by using the NSCSO algorithm to solve the MOP problem have better performance. Compares the NSCSO algorithm with other multi-objective optimization algorithms in six different engineering design problems. The results show that NSCSO not only performs well in multi-objective function tests, but also obtains realistic solutions in multi-objective engineering example problems. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms, widely applied across various domains due to their simplicity and strong optimization capabilities, play a crucial role in problem-solving. While the Aquila Optimizer is recognized for its effectiveness, it often exhibits slow convergence rates and susceptibility to local optima in certain scenarios. To address these concerns, this paper introduces an enhanced version, termed Tent-enhanced Aquila Optimizer (TEAO). TEAO incorporates the Tent chaotic map to initialize the Aquila population, promoting a more uniform distribution within the solution space. To balance exploration and exploitation, novel formulas are proposed, accelerating convergence while ensuring precision. The effectiveness of the TEAO algorithm is validated through a comprehensive comparison with 14 state-of-the-art algorithms using 23 classical benchmark test functions. Additionally, to assess the practical feasibility of the approach, TEAO is applied to six constrained engineering problems and benchmarked against the performance of the same 14 algorithms. All experimental results consistently demonstrate that TEAO outperforms other advanced algorithms in terms of solution quality and stability, establishing it as a more competitive choice for optimization tasks. [ABSTRACT FROM AUTHOR]

Existing weakly supervised named entity recognition (NER) research only deals with flat entities and ignores nested entities. This paper proposes a multi-stage nested entity recognition method (MNR) that utilizes weakly labeled data to recognize nested entities. However, weak labels generated through external knowledge bases have two problems: incompleteness and labeling bias. To address this challenge, the MNR comprises two models. First, we propose a neural transition-based attention model (NTAM) to solve the problem of weak-label incompleteness by learning the correlation between words. Simultaneously, the NTAM obtains candidate entities, including nested entities. Second, we propose a multi-marker fusion attention judgment model (MAJM) for selecting candidate entities through context semantics, candidate entities' meanings, and their boundary information, thereby solving the labeling bias problem. The boundary information of candidate entities is enhanced by fusing their type markers. To our knowledge, we are the first to recognize nested entities under weak supervision by alleviating the noise of weakly labeled data. Experiments on three public nested NER datasets prove the effectiveness of our proposed method under weak supervision and demonstrate that the method outperforms previous state-of-the-art models under supervision. [ABSTRACT FROM AUTHOR]

Large-scale natural frequency-related topology optimization problems pose a great challenge due to the high computational cost required by the iterative computation of the system eigenvalues and their sensitivities in each design iteration. This paper presents a new framework based on a multi-step relay strategy in conjunction with the idea of successive iteration of analysis and design (SIAD), to find high-quality solutions at affordable computational costs. The method starts from a relatively coarse finite element discretization, and then use gradually refined meshes to improve the design resolution. For a given level of mesh resolution, the method interleaves the eigenvalue solution routine with the optimization iterations to achieves sequential approximations of the eigenpairs along with the structural design evolution, thus avoiding the time-consuming eigenpair analysis in each optimization iteration. By sequentially solving the optimization problem and projecting intermediate designs and the corresponding approximate eigenmodes from a coarser mesh onto finer meshes, the proposed multi-step relay method can further substantially alleviate the computational burden and generate high-resolution boundaries in the final design. Numerical examples show that this method can be used to solve natural frequency maximization topology optimization problems with millions of degrees of freedom on a desktop workstation, and is much more efficient than the conventional double-loop method. [ABSTRACT FROM AUTHOR]

Aiming at the problem of multi-object detection such as target occlusion and tiny targets in road scenes, this paper proposes an improved YOLOv5 multi-object detection model based on ML-AFP (multi-level aggregation feature perception) mechanism. Since tiny targets such as non-motor vehicle and pedestrians are not easily detected, this paper adds a micro target detection layer and a double head mechanism to improve the detection ability of tiny targets. Varifocal loss is used to achieve a more accurate ranking in the process of non-maximum suppression to solve the problem of target occlusion, and this paper also proposes a ML-AFP mechanism. The adaptive fusion of spatial feature information at different scales improves the expression ability of network model features, and improves the detection accuracy of the model as a whole. Our experimental results on multiple challenging datasets such as KITTI, BDD100K, and show that the accuracy, recall rate and mAP value of the proposed model are greatly improved, which solves the problem of multi-object detection in crowded road scenes. [ABSTRACT FROM AUTHOR]

Flexible flat cable (FFC) detection is the premise of robot 3C assembly and is challenging because FFCs are often non-axis aligned with arbitrary orientations having cluttered surroundings. However, to date, the traditional robotic object detection methods mainly regress the object horizontal bounding box, in which the size and aspect ratios do not reflect the actual shape of the target object and hardly separate the FFCs in dense. In this paper, rotated object detection was introduced into FFC detection, and a YOLO-based arbitrary-oriented FFC detection method named YOLOOD was proposed. Firstly, oriented bounding boxes are used to reflect the object's physical size and angle information and better separate the FFCs from the dense background. Secondly, the circular smooth label angular classification algorithm is adopted to obtain the angle information of FFCs. Finally, the head point regression branch is introduced to distinguish between the head and the tail of the FFC and expand the range of FFC detection angle to 0 ∘ , 360 ∘ . The proposed YOLOOD can reach the detection performance with an average precision of 90.82% and a detection speed of 112 FPS on an FFC dataset. Meanwhile, an actual FFC grasping experiment demonstrated the proposed YOLOOD's effectiveness and feasibility in practical assembly scenarios. In conclusion, this paper innovatively introduces rotating object detection into robot object detection, and the proposed YOLOOD solves the problem of detecting and locating non-axis aligned FFCs and has particular significance for robot 3C assembly. [ABSTRACT FROM AUTHOR]

The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m3/s, which is much lower than the safe overflow flow rate of 22,000 m3/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs. [ABSTRACT FROM AUTHOR]

In this paper, a node detection method is proposed to detect the damaged parts of armor automatically. The traditional detection of armor damage is judged by experience first, and then the parts are decomposed and confirmed, which makes the problem solving time long and efficiency low. In this paper, the in-mold electronic decoration technology is used on the surface of armor, and the node displacement changes after the surface film is damaged are used to achieve the purpose of damage detection. By using the wavelet analysis, the singularity of the wavelet function can be found and the damaged part can be determined. The Lipschitz index can be used to judge the singularity of the wavelet function to detect the local damage on the armor surface. Finally, the changes of Lipschitz index of the signal with different damage degrees on the helmet deck were simulated. In terms of influencing factors of optimization process, moldex3D was used to simulate and analyze the damaged parts of armor—different injection molding parameter schemes were set to optimize the armor film. In the first stage, the displacement change at the damaged location is detected by the probe node through simulation. In the second stage, the armor was simulated by setting appropriate process parameters such as melt temperature, filling time, filling pressure, and filling time. In the third stage, the wavelet analysis and a Lipschitz index were used to detect the location of damaged nodes. In the fourth stage, the change curve of wavelet analysis is verified by the analysis experiment. Through the experimental verification, it can be seen that the position displacement of the damaged armor changes and the singularity is generated on the wavelet function to achieve the purpose of damage detection. The influencing factors of the film injection molding are temperature and pressure. Through the simulation analysis, we optimize the injection molding parameters of the film, thus improving the damage resistance of the armor. Finally, the optimal parameters of vulnerable parts are obtained and the optimization scheme is determined. [ABSTRACT FROM AUTHOR]

The efficiency of truck loading-and-unloading tasks has become one of the major problems in logistics transportation, and intelligent loading-and-unloading robots are considered as effective solutions to this problem. Recently, carton detection with deep learning is widely used for those robots; however, lack of large annotated datasets is one of major impediments for rapidly deploying carton detection models. We currently have presented the Sacked Carton Dataset (SCD) that contains carton images from four logistics scenarios, such as Comprehensive Pharmaceutical Logistics Company (CPLC), E-commerce Logistics Company (ECLC), Fruit Market (FM), and Large Furniture Market (LFM). However, due to the domain shift, the carton detection model trained with source domain (which are annotated) has poor generalization ability when applied to the target domain (which are not annotated and from new logistics scenarios). To solve this problem, a novel image synthesis method is proposed to quickly acquire a labeled dataset, which replace the foreground texture of the source datasets with the texture of the target datasets. In this paper, a Local Surfaces Segmentation Algorithm is proposed to extract the carton stacking skeleton and an Invisible Contour Reconstruction Algorithm is proposed to complete the contour of the carton. Our method can keep the context relationship of foreground objects and backgrounds unchanged, and it also can greatly augment the target datasets. Code is available here. [ABSTRACT FROM AUTHOR]

High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems. [ABSTRACT FROM AUTHOR]

In recent years, the behavior recognition technology based on Wi-Fi devices has been favored by many researchers. Existing Wi-Fi-based human behavior recognition technology mainly uses classification algorithms to construct classification models, which has problems such as inaccurate behavior segmentation, failure to extract deep-level features from the original data and design classification models matching the proposed features in the process of behavior recognition. In order to solve above problems, this paper proposes a window variance comparison method by combining the adaptive thresholds calculated to achieve effective segmentation of multiple discontinuous human behaviors, then uses the short-time Fourier algorithm to extract time-frequency features of individual behavior, and extracts the graph structure data from the autocorrelation matrix of time-frequency features and the features themselves. A graph neural network is built for behavior recognition. The experimental results show that the segmentation accuracy of the behavior segmentation method in the two scenes is 0.964 and 0.993, which is better than the existing threshold-based behavior segmentation methods. In addition, this paper extracts graph structure data by spectral energy change method and builds behavior recognition model by using graph neural network, and the recognition accuracy is significantly improved compared with the traditional classification algorithm. [ABSTRACT FROM AUTHOR]

Studies the role of automatic control theory that lead to system methodologies of identification for various types of applied problems. System methodologies developed for the practical use of the mathematical apparatus of the theory; Orientation of research activity to realization and detailed description of the rational structure of identification within the framework of various search systems; Provision of the subject of identification with informational support for transformation of the difficult-to-formalize conditions.

Collaboration is a key driver of science and innovation. Mainly motivated by the need to leverage different capacities and expertise to solve a scientific problem, collaboration is also an excellent source of information about the future behavior of scholars. In particular, it allows us to infer the likelihood that scientists choose future research directions via the intertwined mechanisms of selection and social influence. Here we thoroughly investigate the interplay between collaboration and topic switches. We find that the probability for a scholar to start working on a new topic increases with the number of previous collaborators, with a pattern showing that the effects of individual collaborators are not independent. The higher the productivity and the impact of authors, the more likely their coworkers will start working on new topics. The average number of coauthors per paper is also inversely related to the topic switch probability, suggesting a dilution of this effect as the number of collaborators increases. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles are useful tools for many applications. However, autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a challenging problem when facing a series of problems such as poor consistency, high influence by the native controller of the Unmanned Aerial Vehicles. In this paper, we investigate reinforcement learning-based autonomous local path planning methods for Unmanned Aerial Vehicles with high autonomous decision-making capability and locally high portability. We propose an autonomous local path planning algorithm based on the TD3 strategy to solve the problem of local obstacle avoidance and path planning in unfamiliar environments using autonomous decision-making of Unmanned Aerial Vehicles. The simulation results on Gazebo show that our method can effectively realize the autonomous local path planning task for Unmanned Aerial Vehicles, the success rate of path planning with our method can reach 93% under the interference of no obstacles, and 92% in the environment with obstacles. Finally, our method can be used for autonomous path planning of Unmanned Aerial Vehicles in unfamiliar environments. [ABSTRACT FROM AUTHOR]

A multi-dimensional signal on graph has different features along different directions/dimensions. For example, image signal is isotropic along horizontal and vertical directions, and time-varying signal presents different correlation characters in the time and vertex domains. Nevertheless, the current graph signal processing concentrates on the single shift, which makes it difficult to differentiate the correlation features along different directions. In this paper, we define a multi-shift notion for the time-varying signals on graphs enabling us to separately analyze the multi-dimensional signal along different directions captured by diverse shifts. Furthermore, the multi-shift notion is leveraged to formulate the inpainting problem for time-varying signals on the strong product graph, which can be exploited to characterize three different kinds of elements interaction of time-varying signals by using three different kinds of shifts. The conjugate gradient descent algorithm is further deployed to solve the inpainting problem. Numerical experiments conducted on the synthetic signal and real-world data show the potentiality of the multi-shift representation and the effectiveness of the inpainting algorithm. [ABSTRACT FROM AUTHOR]

Businesses nowadays often feel impelled to rush and implement Big Data and Artificial Intelligence initiatives in their organizations without clarity on their business problems, nor the appropriate methodologies for extracting actionable insights from the data. In contrast, this paper presents a process that starts with an articulated business problem instead of a "data rush". The presented process, dubbed the Business-Driven Data-Supported (BDDS) process, is rigorously anchored in concepts from Theory of Constraints and Information Theory. BDDS guides businesses in solving their problems by stating observed performance gaps, uncovering their underlying root cause, formulating the "right question", utilizing only the "right data", and choosing the "right methodology" to extract the "right information" from the data, leading to the "right actionable insights." Also provided is a prescriptive framework, dubbed the Data-to-Information-Extraction-Methodology (DIEM), for selecting the "right methodology", based on the understanding level of relevant system dependencies and the availability of relevant data. The BDDS process is illustrated by an example from the healthcare industry, and the efficacy and applicability of the DIEM framework are supported by eleven case studies from a broad range of industries. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven solver (DDS) for the growth-induced deformation problems of soft materials. Contrary to the over-reliance of traditional numerical methods on material constitutive models, the DDS requires only a discrete material dataset of stress–strain pairs to describe the stress–strain relationship. With the multiplicative decomposition of the gradient tensor, the growth effects are introduced. The growth-induced deformation problems are then solved as the pre-strain problems. To overcome the difficulty in determining the growth-induced pre-stress without an explicit constitutive model, a strain-driven searching scheme is thus proposed. Several numerical examples demonstrate the robustness and its good performance of the proposed DDS. In particular, the reason for choosing the strain-driven searching scheme is also illustrated according to the numerical example results. [ABSTRACT FROM AUTHOR]

In this paper, with motivation from the No Free Lunch theorem, a new human-based metaheuristic algorithm named Preschool Education Optimization Algorithm (PEOA) is introduced for solving optimization problems. Human activities in the preschool education process are the fundamental inspiration in the design of PEOA. Hence, PEOA is mathematically modeled in three phases: (i) the gradual growth of the preschool teacher's educational influence, (ii) individual knowledge development guided by the teacher, and (iii) individual increase of knowledge and self-awareness. The PEOA's performance in optimization is evaluated using fifty-two standard benchmark functions encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, as well as the CEC 2017 test suite. The optimization results show that PEOA has a high ability in exploration–exploitation and can balance them during the search process. To provide a comprehensive analysis, the performance of PEOA is compared against ten well-known metaheuristic algorithms. The simulation results show that the proposed PEOA approach performs better than competing algorithms by providing effective solutions for the benchmark functions and overall ranking as the first-best optimizer. Presenting a statistical analysis of the Wilcoxon signed-rank test shows that PEOA has significant statistical superiority in competition with compared algorithms. Furthermore, the implementation of PEOA in solving twenty-two optimization problems from the CEC 2011 test suite and four engineering design problems illustrates its efficacy in real-world optimization applications. [ABSTRACT FROM AUTHOR]

The problem of continuous community search in temporal graphs aims to identify dense subgraphs that exist continuously over time, which is widely used in sociology, biology, communication, and other fields. Existing methods can find communities that satisfy both temporal and structural constraints, but did not consider constraints on vertex attributes. In this paper, we investigate the continuous community search problem with attribute constraints in temporal graphs. Specifically, we aim to identify graphs that meet temporal, structural, and attribute constraints. To solve the problem efficiently, we first propose a pruning enumeration algorithm (PRUNE_ENUM), which uses the properties of the lower bound of vertices of k-core, the upper bound of attribute intersection, and subgraph continuity to prune useless search space during computation. We then propose an optimized enumeration algorithm (FILTER_ENUM) that first generates much smaller subgraphs using subsets of the given query attributes, then takes these smaller subgraphs as input for enumeration. During the enumeration, we propose two pruning rules to reduce search space based on attribute similarity and subgraph maximization to improve the efficiency. We conduct comparative experiments on four real-world temporal networks, and the experimental results demonstrate the high efficiency of our algorithms. [ABSTRACT FROM AUTHOR]

In this paper, different methods are utilized to solve the coordination issue involving directional overcurrent relays (DOCRs) and distance relays. The proper coordination of DOCRs and distance relays is a critical issue for system security in electrical networks. Finding the DOCRs setting, pickup current(Ip) and timed dial setting (TDS), and operating time for zone-2 of distance relays is the primary objective of solving the coordination problem. The constant parameters A & B of the directional overcurrent relay that are responsible to control the form of the relay´s characteristics as well as the Ip and TDS have been regarded as variables in this problem. The optimal value for these four DOCR settings has been determined using different optimization techniques. The primary and backup relays must operate sequentially and without any violations, and this must be guaranteed by optimization techniques. In order to determine the operation time for zone-2 and DOCRs setting, optimization methods are examined utilizing the 8-bus and IEEE 30-bus networks. Different optimization algorithms, including recent and traditional techniques, are compared. The obtained results show the superiority of the genetic algorithm (GA) in solving the coordination problem of distance relays and DOCRs. Also, the obtained results prove the ability of the GA method compared to the particle swarm algorithm (PSO), grey wolf optimization (GWO), water cycle technique (WCA), equilibrium optimizer (EO), African vultures optimization algorithm (AVOA), flow direction algorithm (FDA), and gorilla troops optimizer (GTO) techniques. [ABSTRACT FROM AUTHOR]

Under the shared big data environment, the existing shared data auditing schemes rarely consider the authorization management of group users. Thus, in this paper, we propose a novel remote shared data checking possession scheme that support group authority management. To implement group user rights management, we firstly introduce a trusted entity group manager. To assist the group manager in authorization management, we formalize a new algebraic structure operator named authorization invisible authenticator (AIA). Meanwhile, we provide a basic AIA scheme for general security scenarios and a standard AIA scheme suitable for high-level security scenarios respectively. The standard AIA scheme can fully meet the needs of the group manager to safely perform rights management work. It is composed of the User Information Table (UIT) and the basic AIA scheme, which has higher security and is applicable to a wider range of scenarios. By distributing AIA through the standard AIA scheme, the group manager can easily carry out authority management, including enrolling, revoking, updating. After solving the problem of authorization management, the detailed design of the scheme based on identity-based encryption (IBE) is given. Furthermore, the security analysis and performance evaluation demonstrate that the scheme is safe and feasible. [ABSTRACT FROM AUTHOR]

In this paper, inhomogeneous chemical kinetics are simulated by describing the concentrations of interacting chemical species by a linear expansion of basis functions in such a manner that the coupled reaction and diffusion processes are propagated through time efficiently by tailor-made numerical methods. The approach is illustrated through modelling α - and γ -radiolysis in thin layers of water and at their solid interfaces from the start of the chemical phase until equilibrium was established. The method's efficiency is such that hundreds of such systems can be modelled in a few hours using a single core of a typical laptop, allowing the investigation of the effects of the underlying parameter space. Illustrative calculations showing the effects of changing dose-rate and water-layer thickness are presented. Other simulations are presented which show the approach's capability to solve problems with spherical symmetry (an approximation to an isolated radiolytic spur), where the hollowing out of an initial Gaussian distribution is observed, in line with previous calculations. These illustrative simulations show the generality and the computational efficiency of this approach to solving reaction-diffusion problems. Furthermore, these example simulations illustrate the method's suitability for simulating solid-fluid interfaces, which have received a lot of experimental attention in contrast to the lack of computational studies. [ABSTRACT FROM AUTHOR]