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The present paper studies formal properties of so-called modal information logics (MILs)—modal logics first proposed in (van Benthem 1996) as a way of using possible-worlds semantics to model a theory of information. They do so by extending the language of propositional logic with a binary modality defined in terms of being the supremum of two states. First proposed in 1996, MILs have been around for some time, yet not much is known: (van Benthem 2017, 2019) pose two central open problems, namely (1) axiomatizing the two basic MILs of suprema on preorders and posets, respectively, and (2) proving (un)decidability. The main results of the first part of this paper are solving these two problems: (1) by providing an axiomatization [with a completeness proof entailing the two logics to be the same], and (2) by proving decidability. In the proof of the latter, an emphasis is put on the method applied as a heuristic for proving decidability 'via completeness' for semantically introduced logics; the logics lack the FMP w.r.t. their classes of definition, but not w.r.t. a generalized class. These results are build upon to axiomatize and prove decidable the MILs attained by endowing the language with an 'informational implication'—in doing so a link is also made to the work of (Buszkowski 2021) on the Lambek Calculus. [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]

This paper addresses a linear programming problem with interval right-hand sides, forming a family of linear programs associated with each realization of the interval data. The paper focuses on the outcome range problem, which seeks the range of an additional function—termed the outcome function—over all possible optimal solutions of such linear programs. We explore the problem's applicability in diverse contexts, discuss its connections to certain existing problems, and analyze its computational complexity and theoretical foundations. Given the inherent computational challenges, we propose three heuristics to solve the problem. The first heuristic employs a reformulation–linearization technique (RLT) to obtain an outer approximation of the range of the outcome function. We also present two algorithms—a gradient-restoration-based approach (GI) and a bases inspection method (BI)—for computing an inner approximation of the range. Computational experiments illustrate the competitive advantage of our proposed approaches versus off-the-shelf solvers. The GI and BI methods present promising results in finding a cheap but tight inner approximation, while the performance of the RLT technique decreases as problem size and uncertainty increase. [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]

This paper introduces the definition of an autonomous valet parking system, standardization trends, processes designed to implement the system. Autonomous valet parking is a system in which a vehicle can be parked in a parking space without a user, and can also be moved to a specific location when the user wants to. The autonomous valet parking system is being developed by dividing it into search driving process, autonomous parking process, and return driving process. Currently, the autonomous valet parking system can demonstrate the entire parking process in a specific scenario. However, there are limitations, i.e., the system requires high costs, and some technologies do not show stable results. In this paper, we have highlighted the problems that should be solved to complete the autonomous valet parking system and the technologies for solving these problems. From this paper, researchers will be able to learn about the technical aspects and the developmental direction of the autonomous valet parking system. [ABSTRACT FROM AUTHOR]

3D point cloud registration is a basic task in computer vision. In recent years, various of learning-based methods have been proposed to solve this problem. These methods effectively overcome the original problem of over-reliance on initial conditions and enhance the ability of obtaining the corresponding relationship. However, few methods pay enough attention to local features and tend to cause some mismatches. Therefore, this paper proposes two networks to extract local features sufficiently. To obtain a more accurate correspondence relationship between the point clouds, we propose a feature weight allocation network (FWANet), in which the expression ability of feature is enhanced using the proposed significant feature extraction module. Besides that, we utilize an interference elimination module to remove the interference points and enhance the internal correlation of point clouds. We also propose a spatial structural generation network (SSGNet), which fully utilizes the spatial location information to determine the spatial correspondence and generate a reliable connection after concatenating multi-dimensional features. At last, a complete feature space can be effectively captured after combining our FWANet with SSGNet together. We conducted multiple experiments on ModelNet40 datasets and achieved excellent results. Experimental results on four types of data demonstrate the superiority of our algorithm against the state-of-the-art ones. Our code will be available at https://github.com/liu-zikang/registration as soon as the paper is accepted. [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]

Turret trucks used for special warehousing operations have exceptional abilities, qualifications, and working principles than other warehousing handling equipment. Also, at the same time, if decision-makers cannot select as proper to needs, the cost of being idle of these kinds of machines is very high. According to the comprehensive literature review and the paper's findings, evaluating turret trucks is complex, complicated, and time-consuming for decision-makers, as many conflicting criteria and uncertainties affect the evaluation processes. Hence, it is required to employ a practical, powerful, and practical multi-criteria decision-making (MCDM) approach that can handle ambiguities to solve these kinds of problems. For this purpose, the current paper proposes an extended version of a hybrid decision-making tool consisting of FUCOM (Full Consistency Method) and the WASPAS (Weighted Aggregated Sum Product Assessment) technique with the help of the intuitionistic fuzzy Dombi aggregation operators. It can help to select the appropriate turret trucks that can help reduce the costs of remaining idle and provide economic effectivity of logistics and warehousing operations. According to the outcomes of the suggested model, the width, lift motor power and lift height of the turret trucks are the most critical and influential criteria, as they determine the corridor width, accordingly space utilization, warehousing capacity, and unit warehousing costs. Finally, the intuitionistic fuzzy model is applied to solve the turret truck selection problems. A comprehensive sensitivity analysis consisting of three phases was performed to test the validation of the proposed model and its obtained results. The sensitivity analysis results approve the proposed model's applicability and validity. Thus, the analysis results validated that the proposed approach is a robust and practical MCDM framework, and its results are accurate and reasonable. [ABSTRACT FROM AUTHOR]

In this note, we correct a mistake in the paper (López et al., J Optim Theory Appl 159(3):741-768, ). [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]

Intelligent swarm optimization algorithms have become increasingly common due to their success in solving real-world problems. Dwarf Mongoose Optimization (DMO) algorithm is a newly proposed intelligent swarm optimization algorithm in recent years. It was developed for continuous optimization problem solutions in its original paper. But real-world problems are not always problems that take continuously variable values. Real-world problems are often problems with discrete variables. Therefore, heuristic algorithms proposed for continuous optimization problems need to be updated to solve discrete optimization problems. In this study, DMO has been updated for binary optimization problems and the Binary DMO (BinDMO) algorithm has been proposed. In binary optimization, the search space consists of binary variable values. Transfer functions are often used in the conversion of continuous variable values to binary variable values. In this study, twelve different transfer functions were used (four Z-shaped, four U-shaped, and four Taper-shaped). Thus, twelve different BinDMO variations were obtained (BinDMO1, BinDMO2, ..., BinDMO12). The achievements of BinDMO variations were tested on thirteen different unimodal and multimodal classical benchmark functions. The effectiveness of population sizes on the effectiveness of BinDMO was also investigated. When the results were examined, it was determined that the most successful BinDMO variation was BinDMO1 (with Z1-shaped transfer function). The most successful BinDMO variation was compared with three different binary heuristic algorithms selected from the literature (SO, PDO, and AFT) on CEC-2017 benchmark functions. According to the average results, BinDMO was the most successful binary heuristic algorithm. This has proven that BinDMO can be chosen as an alternative algorithm for binary optimization problems. [ABSTRACT FROM AUTHOR]

The recently proposed semantic-aligned matching detection transformer (SAM–DETR model) accelerates the convergence of the detection transformer (DETR) by mapping object queries into an identical embedding space as the encoder's output feature map. However, SAM–DETR model has the problem of low detection accuracy compared to other DETR variants. We observe that the lower detection accuracy of SAM–DETR model is caused by the insufficient number of sample points and the inaccurate localization of the sample points during re-sampling, which blurs the generated attention map. This paper proposes an object detector based on a feature correction and double sampling DETR (FCDS-DETR) to solve this problem. FCDS-DETR takes SAM–DETR model as a baseline and builds on it by adding a feature correction module and a double sampling mechanism to achieve further improvement in detection accuracy with a limited number of additional parameters without sacrificing convergence speed. Firstly, FCDS-DETR improves the sampling point localization accuracy by adding a feature correction module to model the inter-channel dependence of the feature maps to be sampled. Secondly, the number of sampled points is increased by the double sampling mechanism, and attention fusion is used to fuse the attention weight maps corresponding to the two sets of sampled points to improve the recognizability of the attention weight maps. The experimental results show that the average precision is improved by +0.7 on the COCO dataset compared with the SAM–DETR model, and the number of parameters is increased by only 10.34 % , which improves the detection performance of the model very well. [ABSTRACT FROM AUTHOR]

Solar photovoltaic energy is the potential energy in the universe for generating electricity and meeting the required load demand. However, on account of partial shading conditions, the difficult task in the PV system is to track global maxima instead of local maxima and maintain the uninterrupted power supply. To solve this problem, a new metaheuristic algorithm is introduced in this paper such as a heap-based optimizer (HBO). The proposed method is developed in MATLAB/Simulink software. The system is examined under distinct irradiation conditions and compared their performance with other methods. The simulation results reveal that the suggested HBO shows a reliable enhancement as compared to other studied methods with regard to tracking maximum power, convergence time, and settling time. The extracted power efficiencies are 99.85% for case 1, 99.96% for case 2, and 99.92% for case 3. It is found that HBO shows better enrichment than other studied methods. [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]

In 3D geological modeling methods based on section, cross-section data is an important data source for constructing 3D geological modeling in complex areas. The efficiency and quality of cross-section data compilation-adjustment will directly affect the time and quality of subsequent regional 3D geological model construction. However, current compilation-adjustment methods for cross-section are inefficient and difficult to effectively control their quality. Therefore, in this paper, a cross-section compilation-adjustment method based on 2D-3D linkage is proposed. It deeply integrates the virtual borehole geological structure (VBGS), 2D-3D linkage strategy, and structural geological feature information into the inspection-adjustment process of geologic bodies (faults, strata, rock masses, and ore bodies) structural abnormality (GSA) and elevation consistency at intersections. A complete cross-section compilation and quality control process can be realized by regional structural stratigraphic framework construction, GSA high efficiency analysis and adjustment, elevation errors quick positioning and adjustment at intersections. This new method solves the problem of inconsistency between the complex geological structures and the real structural geological characteristics and overcomes the defects of local fitting abnormality (depressions or sharp protrusions) of geological interfaces caused by elevation errors at intersections. Software tool is developed based on QuantyView 2D/3D GIS platform, and 73 cross-section data of a 1:50,000 modeling area in western Guizhou, China, is taken as an example for practical application verification. Results show that the proposed new method significantly improves the inspection-adjustment efficiency of GSA and elevation errors at intersections. What's more, quality of cross-sections compilation-adjustment has been effectively controlled, which provides reliable data sources for constructing high quality regional 3D geological models. [ABSTRACT FROM AUTHOR]

The BFGS method is a common and effective method for solving unconstrained optimization problems in quasi-Newton algorithm. However, many scholars have proven that the algorithm may fail in some cases for nonconvex problems under Wolfe conditions. In this paper, an adaptive projection BFGS algorithm is proposed naturally which can solve nonconvex problems, and the following properties are shown in this algorithm: ➀ The generation of the step size α j satisfies the popular Wolfe conditions; ➁ a specific condition is proposed which has sufficient descent property, and if the current point satisfies this condition, the ordinary BFGS iteration process proceeds as usual; ➂ otherwise, the next iteration point x j + 1 is generated by the proposed adaptive projection method. This algorithm is globally convergent for nonconvex problems under the weak-Wolfe-Powell (WWP) conditions and has a superlinear convergence rate, which can be regarded as an extension of projection BFGS method proposed by Yuan et al. (J. Comput. Appl. Math. 327:274-294, 2018). Furthermore, the final numerical results and the application of the algorithm to the Muskingum model demonstrate the feasibility and competitiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The paper is devoted to the problem of identification of hardening parameters of the developed two-level statistical model of polycrystal inelastic deformation taking into account dynamic recrystallization. First, the approximate values of the hardening parameters are obtained, and then they are refined as a result of formulating and solving the optimization problem using the Nelder–Mead method. The result is the set of the parameters that provides high accuracy of the empirical and calculated data on the stress-strain curves during inelastic deformation of a copper polycrystal. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Riemannian version of the difference of convex algorithm (DCA) to solve a minimization problem involving the difference of convex (DC) function. The equivalence between the classical and simplified Riemannian versions of the DCA is established. We also prove that under mild assumptions the Riemannian version of the DCA is well defined and every cluster point of the sequence generated by the proposed method, if any, is a critical point of the objective DC function. Some duality relations between the DC problem and its dual are also established. To illustrate the algorithm's effectiveness, some numerical experiments are presented. [ABSTRACT FROM AUTHOR]

In this paper we give a complete characterization of those knotted toroidal sets that can be realized as attractors for discrete or continuous dynamical systems globally defined in R 3 . We also see that the techniques used to solve this problem can be used to give sufficient conditions to ensure that a wide class of subcompacta of R 3 that are attractors for homeomorphisms must also be attractors for flows. In addition we study certain attractor-repeller decompositions of S 3 which arise naturally when considering toroidal sets. [ABSTRACT FROM AUTHOR]

The paper contains a solution to the Riemann problem for the generalized Chaplygin gas with a general source term defined by the Cargo–LeRoux potential. Besides the classical waves (rarefaction waves, shocks, and contact discontinuities), we have used the shadow waves coupled with the rarefaction ones to completely solve the above problem. [ABSTRACT FROM AUTHOR]

To solve the problems of high ore resources loss and natural pillar splitting failure in multi-stope mining of gently inclined thin orebody by traditional room-and-pillar method, the overburden stability control technology by replacing natural pillars with pre-stressed expandable pillars (PEP) is developed in this paper. By means of numerical simulation and physical experimental methods, the distribution characteristics of stress, displacement, and failure zone of surrounding rock in multi-stope mining with and without PEP support were studied. The results demonstrate that the PEP supporting effect can improve the stress environment of surrounding rock compared with the unsupported condition, divide the stress reduction zone of the stope roof, and avoid the overburden failure induced by tensile stress penetration in the shallow-buried stope. Moreover, the PEP support can effectively limit the crack propagation depth and damage degree of the surrounding rock. Deriving from the physical test, compared to the unsupported scenario, the vertical deformation of the stope roof is reduced by about 44% on average with the PEP support, thus preventing overburden collapse caused by large deformation. Besides, the greater the mining depth of the stope, the greater the increase in the bearing capacity of the PEP support. Field industrial test shows that the stope and PEP supports are visually safe and stable after mining. The overburden stability control technology with PEP support proposed in this study can offer a key reference for improving ore resource recovery in multi-stope mining of gently inclined orebody. [ABSTRACT FROM AUTHOR]

The current object detection algorithm suffers from slow detection speed and low object recognition rate when detecting marine benthos due to small objects, severe densities and occlusions. To solve these problems, an improved object detection algorithm based on FCOS, DSE-FCOS (Dilated & SE block reinforced FCOS), is proposed for marine benthos in this paper. In our method, to meet the needs of real-time detection, we only use a single-level feature map for detection, we propose DSE module to solve the problem of insufficient single-scale detection accuracy. The DW (Dual Weighting) label assignment method is introduced and optimized to make it more suitable for the detection of marine benthos, increasing the speed of detection while ensuring accuracy. We performed experiments on the DUO dataset. The results show that our method can improve mAP (mean average precision) by 5.9% on the basis of 13.8% speed improvement compared with FCOS. This improvement reflects that DSE-FCOS is more suitable for the detection of marine benthos. [ABSTRACT FROM AUTHOR]

Detecting small and densely distributed (SDD) objects in large-size images is quite challenging due to the facts that directly inputting such images to the detection network would result in severe geometric deformation and the overcrowded objects would lead to unclear feature expression. In this paper, we propose a two-level data augmentation method, referred to as MultiCut-MultiMix, to solve the problems, in which MultiCut is developed to avoid feature distortion at the physical level and MultiMix is designed to enrich background at the pixel level. Specifically, according to the images size required by the detection network, MultiCut, by appropriately tuning two introduced parameters, cuts large-size images into a series of image chips that are suitable for training, and meanwhile ensuring that the objects at the cutting edge would not lose. Furthermore, to strengthen feature information in these obtained image chips, MultiMix fuses different chips into new ones, in which the chip with most SDD objects will be remained as the major information and the others as the background. The fused chips from MultiMix, together with the original chips from MultiCut, then serve as the new data to train the detection network, by which the dataset is enlarged and thus overfitting can be effectively avoided. Extensive ablation experiments show that, compared with existing approaches, our method usually significantly assists detection networks to identify SDD objects in large-size images. For example, based on the bacterial dataset, 14.69% improvement in the mean average precision is achieved over the classical CutMix on Darknet53. [ABSTRACT FROM AUTHOR]

Fabric defect detection is an important part of the textile industry, aiming at the problems of many types of fabric defects, small size defects and unbalanced samples, an improved YOLOv5 fabric defect detection algorithm, FD-YOLOv5, was proposed. First, the coordinate attention module is embedded in the YOLOv5 backbone network structure to replace the bottleneck structure in the original network model. While reducing the amount of parameters and calculation, it enhances the ability of the network to extract features and improves the model's ability to detect small target defects. Secondly, a smoother Mish activation function is used in the original model convolution structure for model training, which improves the nonlinear expression ability of the model; the SIoU loss function considering the direction of the anchor box is used to improve the convergence speed and detection accuracy of the model. Finally, combining the focal loss and GHM loss functions as the target confidence loss function to solve the problem of sample imbalance in the fabric defect dataset. The experimental results based on the public fabric defect dataset of Aliyun TianChi shows that the mAP@.5 and mAP@.5:.95 of the improved algorithm are 65.1% and 30.4%, respectively, which are 8.3% and 3.2% higher than the original model, respectively, and the parameter amount, calculation amount and weight of the model are reduced by 8.4%, 11.2% and 14.3%, respectively, compared with the original model. Even compared with the state-of-the-art YOLOv7 model, the mAP@.5 value of the proposed model is improved by 6.5%. Although the FPS value is lower than YOLOv7 model, it also achieves a detection speed of 79 frames per second, which can meet the real-time demand. The experimental results demonstrate the effectiveness of the method in this paper, which can provide a reference for the automatic detection method of fabric defects. [ABSTRACT FROM AUTHOR]

The challenge for practical application of frame structural optimization had previously been investigated by many works, while the mechanical performance requirements such as the displacement, stress, and stability requirements, were often considered separately within optimization, hindering their practical applications. For this purpose, an integrated topology and size optimization strategy of frame structures, in which the structural weight is taken as the objective with the constraints regarding the displacement, stress, as well as stability, is presented in this paper. Different from former researches, each beam is assigned with a topology variable representing the presence of the beam and a size variable correspond to the cross-sectional geometric properties. To achieve an optimized design with standard members, by cooperating the ordered multi-material SIMP (solid isotropic material with penalization) interpolation with the normalized Heaviside functions, the continuous size design variables are projected onto the discrete standard sizes conformed to standard library. Moreover, the comprehensive measure, including the stress relaxation, the pseudobuckling mode treatment scheme, the aggregation constraint, and varying constraint limit schemes, is employed to deal with the multiple constraints in the optimization model. Then, the sensitivities of the objective and constraint functions with respect to topology and size design variables are derived, respectively, and the proposed integrated optimization problem is solved by a nested optimization algorithm. Finally, several numerical examples are presented to demonstrate the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of network selection in dense heterogeneous cellular networks, where multiple base stations (BSs) are available to meet the demand for wireless data. The selection of the most appropriate BS for each user equipment (UE) is a complex task, taking into account factors such as signal strength, interference, and available resources. To solve this problem, we propose a distributed algorithm based on game theory that optimizes network selection by considering the physical rate of UEs towards various BSs. The algorithm clusters UEs based on their physical rate and formulates the network selection problem as a convex optimization problem. We propose a distributed algorithm based on game theory that can reach optimal solution and speeds up convergence by simultaneously changing the BS for a set of UEs belonging to the same cluster. Simulation results are used to verify the performance of the proposed algorithm and explore the convergence speed. Specifically, in our simulations, the Modified BIR algorithm achieves convergence almost two times faster than the BIR algorithm, while the cluster-based BIR algorithm converges around 10 times faster than the original BIR algorithm. These findings highlight the significant improvement in network selection performance achieved by our proposed algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the increase in health awareness has made fitness activities more and more popular in families. The lack of professional guidance and real-time feedback during home fitness training is often a limiting factor. In order to solve this problem, a home fitness assistance system based on Android mobile network and optical sensing system is proposed in this study. The purpose of the system is to provide personalized fitness guidance and real-time feedback to help users effectively conduct fitness training in a home environment. In order to achieve this goal, this paper uses the advantages of Android mobile network to realize real-time interaction and communication between users and the system. A fitness detection system based on light sensing technology is designed, which can accurately monitor the user's posture and movement, and provide corresponding feedback. Through the analysis of the experimental results, the home fitness assistance system has high accuracy and efficiency in providing personalized fitness guidance and real-time feedback. Users can choose the right training plan according to their needs and abilities, and get professional guidance and real-time feedback in the training process, so as to achieve better training results. [ABSTRACT FROM AUTHOR]

Stochastic Gradient Descent (SGD) is one of the many iterative optimization methods that are widely used in solving machine learning problems. These methods display valuable properties and attract researchers and industrial machine learning engineers with their simplicity. However, one of the weaknesses of this type of methods is the necessity to tune learning rate (step-size) for every loss function and dataset combination to solve an optimization problem and get an efficient performance in a given time budget. Stochastic Gradient Descent with Polyak Step-size (SPS) is a method that offers an update rule that alleviates the need of fine-tuning the learning rate of an optimizer. In this paper, we propose an extension of SPS that employs preconditioning techniques, such as Hutchinson's method, Adam, and AdaGrad, to improve its performance on badly scaled and/or ill-conditioned datasets. [ABSTRACT FROM AUTHOR]

In this paper, we study the black box optimization problem under the Polyak–Lojasiewicz (PL) condition, assuming that the objective function is not just smooth, but has higher smoothness. By using "kernel-based" approximations instead of the exact gradient in the Stochastic Gradient Descent method, we improve the best-known results of convergence in the class of gradient-free algorithms solving problems under the PL condition. We generalize our results to the case where a zeroth-order oracle returns a function value at a point with some adversarial noise. We verify our theoretical results on the example of solving a system of nonlinear equations. [ABSTRACT FROM AUTHOR]

This paper is a review of the results on the real-time optimal control problem for linear systems obtained by the Minsk school by mathematical methods of optimal control. We consider optimal control problems for dynamical objects, their deterministic mathematical models and perfect measurements of states, objects with disturbances and imperfect measurements of the observed input and output signals, the problem of optimal decentralized control of groups of interconnected dynamical objects, and application of real-time optimal control problems and the control principle to solving stabilization problems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for solving the system of fuzzy relation equations (SFRE) with extended max–min composition for inverse inference problems. The properties of interval and constrained solutions with granular and relational structure of the solution set are investigated. The extended max–min SFRE can be represented in the form of the max–min subsystems aggregated using the min operator or dual min–max subsystems aggregated using the max operator. When decomposing the SFRE, the set of solutions can be decomposed into the lower and upper subsets bounded by the same aggregating solutions. Each lower (upper) subset is defined by the unique greatest (least) or aggregating solution and the set of minimal (maximal) solutions. Following Bartl et al. (Fuzzy Sets Syst 187:103–112, 2012), to avoid excessive granularity and ensure interpretability of the interval solutions when restoring causes through observed effects, the constraints in the form of linguistic modifiers are imposed on the measures of causes significances. The interval solutions are modeled by the complete crisp solutions, that is, the maximum solutions for the vectors of binary weights of the linguistic modifiers. The search for approximate solutions of the SFRE amounts to solving the optimization problem using the genetic algorithm. Due to the properties of the solution set, the genetic search for the lower and upper subsets is parallelized for each aggregating solution. The developed method makes it possible to simplify the search for the solution set based on the constraints on accuracy (interpretability) of the applied problem. [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]

The spatially misaligned data problem occurs when data at different spatial scales need to be combined. Bayesian hierarchical models such as melding and downscaler approaches are suitable to solve this problem but their application is limited when MCMC is used for inference due to the high computational cost. The use of INLA and SPDE represents an alternative to MCMC for Bayesian inference that enables faster inference for latent Gaussian models. In this paper, we describe how INLA and SPDE can be adapted to fit Bayesian melding and downscaler models to combine spatially misaligned data. We assess the performance of the models using simulated and real data in a range of scenarios and sampling strategies and compare the suitability of the models in each situation. We also show how to obtain fine particulate matter (PM 2.5 ) predictions in the UK at a continuous surface and at policy-relevant spatial scales by combining spatially misaligned monitoring station data, satellite-derived indicators, road information, and environmental factors. [ABSTRACT FROM AUTHOR]

In this overview, I consider the gregariousness of human nature and use it as context for the five articles in this special issue on suicidality. I point out that these five papers represent the diversity in concepts and methods characteristic of a young science, decades away from maturity. Nevertheless, I contend that there are commonalities in suicidality specifically and human experience more generally, and that themes like human sociality may serve as a heuristic in understanding them and in preventing the deaths by suicide. [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]

Quantum approximate optimization algorithms are hybrid quantum-classical variational algorithms designed to approximately solve combinatorial optimization problems such as the MAX-CUT problem. In spite of its potential for near-term quantum applications, it has been known that quantum approximate optimization algorithms have limitations for certain instances to solve the MAX-CUT problem, at any constant level p. Recently, the recursive quantum approximate optimization algorithm, which is a non-local version of quantum approximate optimization algorithm, has been proposed to overcome these limitations. However, it has been shown by mostly numerical evidences that the recursive quantum approximate optimization algorithm outperforms the original quantum approximate optimization algorithm for specific instances. In this paper, we analytically prove that the recursive quantum approximate optimization algorithm is more competitive than the original one to solve the MAX-CUT problem for complete graphs with respect to the approximation ratio. [ABSTRACT FROM AUTHOR]

Popular Secp256k1 and Schnorr algorithms offer strong security in current Blockchains. However, they are vulnerable to quantum attacks. To solve this problem, several quantum-resistant algorithms have been proposed. However, the performance evaluations and tangible analyses of these algorithms on current Blockchains have not been studied yet. In this context, a performance analysis of quantum-resistant algorithms on a Blockchain can provide valuable insight into the efficiency of quantum-resistant algorithms in real-world scenarios. To address this need, we prototyped and analyzed a quantum-resistant Blockchain using the Falcon algorithm. Falcon is selected because it provides smaller signature and key size compared to Crystals-Dilithium and Sphincs+. We then measured in real-time the key size, transaction signature size, and transaction verification time. The paper also discusses the potential scalability limitations of the proposed quantum-resistant Blockchain and suggests an approach to select quantum-resistant algorithms based on different Blockchain use cases. Our approach and benchmark results have implications for the future development and adoption of quantum-resistant Blockchains. [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]

In this paper, we focus on using the low-rank factorization approach to solve the SDP relaxation of a graph equipartition problem, which involves an additional spectral upper bound over the traditional linear SDP. We discuss the equivalence between the decomposed problem and the original SDP problem. We also derive a sufficient condition, under which a second order stationary point of the non-convex problem is also a global minimum. Moreover, the constraints of the non-convex problem involve an algebraic variety with conducive geometric properties which we analyse. We also develop a method to escape from a non-optimal singular point on this variety. This allows us to use Riemannian optimization techniques to solve the SDP problem very efficiently with certified global optimality. [ABSTRACT FROM AUTHOR]

A palindromic substring T[i..j] of a string T is said to be a shortest unique palindromic substring (SUPS) in T for an interval [p, q] if T[i..j] is a shortest palindromic substring such that T[i..j] occurs only once in T, and [i, j] contains [p, q]. The SUPS problem is, given a string T of length n, to construct a data structure that can compute all the SUPSs for any given query interval. It is known that any SUPS query can be answered in O (α) time after O(n)-time preprocessing, where α is the number of SUPSs to output (Inoue in J Discrete Algorithms 52-53:122–132, 2018). In this paper, we first show that α is at most 4, and the upper bound is tight. We also show that the total sum of lengths of minimal unique palindromic substrings of string T, which is strongly related to SUPSs, is O(n). Then, we present the first O(n)-bits data structures that can answer any SUPS query in constant time. Also, we present an algorithm to solve the SUPS problem for a sliding window that can answer any query in O (log log W) time and update data structures in amortized O (log σ + log log W) time, where W is the size of the window, and σ is the alphabet size. Furthermore, we consider the SUPS problem in the after-edit model and present an efficient algorithm. Namely, we present an algorithm that uses O(n) time for preprocessing and answers any k SUPS queries in O (log n log log n + k log log n) time after single character substitution. Finally, as a by-product, we propose a fully-dynamic data structure for range minimum queries (RmQs) with a constraint where the width of each query range is limited to poly-logarithmic. The constrained RmQ data structure can answer such a query in constant time and support a single-element edit operation in amortized constant time. [ABSTRACT FROM AUTHOR]

In the field of fault isolation and clearance in flexible DC power grids, hybrid DC circuit breakers (HDCCB) have been widely used because of their advantages. However, their development is limited by high construction costs and inapplicability in some fault cases. In addition, adaptive reclosing capability is required for overhead DC lines with high fault probability. Therefore, this paper proposes a new type of multi-port hybrid DC circuit breaker (MP-HDCCB) with current limiting, breaking, and self-charging capabilities. The capacitor polarity reversal circuit is designed to reduce the influence of capacitor pre-charging on the normal operation of the system and to solve the problem of two consecutive line faults occurring in a short period of time. In addition, the circuit breaker can limit the current and break in the face of a single line fault, multiple line faults at the same time, and bus faults. It also has the ability of adaptive reclosing and normal operation of the line after a bus fault. Based on the proposed topology, a 500 kV four-terminal DC system is designed, and a model is established in PSCAD/EMTDC to verify the capabilities of the circuit breaker. Results show that the circuit breaker has adaptive reclosing capability and can realize fault current limiting and breaking under different fault conditions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sufficient condition at which a neural network can approximate a set of optimization algorithm solutions; we establish under which conditions a neural network can replace an optimization algorithm to solve a problem with the objective of safely deploying that network in a system where online solutions are necessary to simplify the hardware or allowing the processor to solve the optimization problem on time. To that end, first, we define the family of optimization problems to be addressed; then, we construct a vector with the parameters on which the solution depends, in order to propose a function based on the first-order Karush–Kuhn–Tucker conditions to find conditions under which the inverse of the proposed function maps the problem minimizer with respect to the constructed vector, we provide the sufficiency proof of, both, existence and feasibility of approximation by a neural network regarding the inverse function. Two case studies are proposed, one numerical case showing how a neural network can solve an optimization problem faster than popular solvers to illustrate how it can be implemented in applications where the computation time is tight, and the other case is a Model Predictive Control implementation with the optimization problem solver replaced by a neural network which allows a hardware downgrade; both cases are presented with time statistics comparisons. [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]