Original Form Research Articles

Analyzing vehicle path optimization using an improved genetic algorithm in the presence of stochastic perturbation matter

By analyzing the influence of stochastic perturbation matters on vehicle path optimization, a perturbation scheduling model for logistics and distribution with a carbon tax mechanism is established under the premise of time window variation and load capacity constraints. Herein, we propose an enhanced Genetic Algorithm (GA) based on a Gaussian matrix mutation (GMM) operator, which maintains the diversity of the population while speeding up the algorithm’s convergence. The model builds a Gaussian probability matrix using the site positional order distribution characteristics implied in the original site data information, and applies the Gaussian probability matrix to individual gene mutations using a roulette-wheel-selection method; thus, the study guarantees the genetic diversity of the population while guiding it to evolve in the high-fitness direction. Finally, an experimental simulation is performed using data obtained from a commercial supermarket, thereby verifying the effectiveness of the proposed algorithm and comparing it with other algorithms. The results reveal that compared with the classical GA, the average convergence speed of the improved GA can be increased by 50–60% and the consumed algorithm time can be reduced by 48% while maintaining the difference in solution accuracy within 1%.

Kinematic Constrained RRT Algorithm with Post Waypoint Shift for the Shortest Path Planning of Wheeled Mobile Robots

This paper presents a rapidly exploring random tree (RRT) algorithm with an effective post waypoint shift, which is suitable for the path planning of a wheeled mobile robot under kinematic constraints. In the growth of the exploring tree, the nearest node that satisfies the kinematic constraints is selected as the parent node. Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. Then, it is proved that the method can obtain an optimal path in terms of the shortest length. The optimized path has good convergence and almost does not depend on the state of the initial random path. The comparative test results show that the proposed method has significant advantages over traditional RRT-based methods in terms of the sampling point number, the tree node number, and the path node number. Subsequently, an efficient method is further proposed to avoid unknown obstacles, which utilizes the original path information and thus effectively improves the new path planning efficiency. Simulations and real-world tests are carried out to demonstrate the effectiveness of this method.

Low-dose radiographic inspection of welding by a novel aperiodic reverse stochastic resonance method

Abstract Low-dose radiographic inspection is a growing trend in industry to minimize radiation risks to humans and the environment. However, reduction in radiation dose often introduces significant noise, which affects image quality and hinders accurate identification of subtle defects. This study addresses this issue by introducing a novel phenomenon called aperiodic reverse stochastic resonance (ARSR), observed in nonlinear systems excited by aperiodic binary signals. ARSR enables simultaneous amplitude amplification and reversal of signals under specific noise conditions. Leveraging ARSR, we propose an image denoising framework for low-dose radiographic inspections. First, a set of projection data is obtained by using Radon transform to reduce the dimensionality of X-ray images from different angles. Then, the projection data is modulated based on the ARSR system. Finally, the image is reconstructed based on the inverse Radon transform. Simulations and experimental comparison results in welding applications validate the effectiveness of the framework, demonstrating significant improvements in image quality for low-dose radiographic defect detection. Unlike advanced methods such as Gaussian filtering, BM3D, and DnCNN, which operate at the pixel level, ARSR performs denoising at the projection data stage, reducing noise impact, preserving original information, and focusing on physical data processing during imaging. This approach enhances the detection of subtle defects, highlighting the potential of stochastic resonance in image processing.

Variational quantum algorithm for designing quantum information maskers

Abstract Since the concept of quantum information masking was proposed by Modi et al. [Phys. Rev. Lett. 120, 230501 (2018)], many interesting and significant results have been reported, both theoretically and experimentally. However, designing a quantum information masker is not an easy task, especially for larger systems. In this paper, we propose a variational quantum algorithm to resolve this problem. Specifically, our algorithm is a hybrid quantum-classical model, where the quantum device with adjustable parameters tries to mask quantum information and the classical device evaluates the performance of the quantum device and optimizes its parameters. After optimization, the quantum device behaves as an optimal masker. The loss value during optimization can be used to characterize the performance of the masker. In particular, if the loss value converges to zero, we obtain a perfect masker that masks completely the quantum information generated by the quantum information source, otherwise, the perfect masker does not exist and the subsystems always contain the original information. Nevertheless, these resulting maskers are still optimal. Quantum parallelism is utilized to reduce quantum state preparations and measurements. Our work paves the way for the wide applications of quantum information masking, and some of the techniques used in this work may have potential applications in quantum information processing.

Self-supervised image change detection method based on lightweight capsule network

In response to the significant impact of speckle noise on the accuracy of SAR image change detection, the high complexity of existing capsule network based image change detection methods, and the loss of a large amount of original image information in training samples, this paper proposes a self supervised image change detection method based on the Light Capsule Network (SCapsNet). First, the log ratio operator difference graph is generated, and the "pseudo label" of training samples with high confidence is obtained through the maximum inter class variance method and fuzzy C-means clustering method, which lays the foundation for self-supervised learning; Secondly, construct a three channel training sample based on the difference map of two temporal SAR images and logarithmic ratio operators to maximize the preservation of sample information; Then, SCapsNet is designed to extract training sample features through single scale convolution, and a single scale capsule network is used to mine spatial relationships between features; Finally, comparative experiments and ablation experiments were set up and tested on 5 real SAR Datasets. The experimental results show that the The advantage of this method is to improve the operational efficiency of the method while reducing model complexity, obtain stronger robust features, suppress the adverse impact of speckle noise on change detection performance, and improve change detection performance.

Image segmentation of tunnel water leakage defects in complex environments using an improved Unet model

Computer vision technology provides an intelligent means for detecting tunnel water leakage areas. However, the accuracy of defect feature extraction and segmentation is limited by factors such as insufficient lighting and environmental interference inside tunnels. To address the problem, this paper proposes a tunnel water leakage area segmentation network model called Customized Side Guided-Unet (CSG-Unet), using Unet as the baseline model. The main contributions are: (1) To improve the accuracy of water leakage area extraction, a customized side guided term is introduced to direct the net’s attention to the changes in light and shade within the image. A parallel attention network module is designed to extract internal information from the guided term. Subsequently, a strengthened channel attention module aggregates the guided term and the original information to achieve accurate segmentation of water leakage areas; (2) To address the scarcity of tunnel water leakage area datasets, a basic dataset is constructed by collecting data from open-source datasets and manually gathered data in tunnels. On this basis, perspective transformation is used to change the camera viewpoint, gaussian noise is randomly added to the images in the dataset to simulate images taken in dimly lit scenes, thereby expanding the dataset and enhancing the network’s generalization. The CSG-Unet network was trained using the constructed training set, achieving a mean Intersection over Union (mi IoU) of 85.54%, a mean Dice coefficient (mi Dice) of 85.26%, and a mean Pixel Accuracy (mi PA) of 90.85%. Compared to its baseline network, U-Net (tiny), these metrics show an improvement of over 3.2% in each indicator. Finally, a visual comparison between the improved network and the baseline network further confirms that the proposed model can effectively adapt to the segmentation of water leakage areas in complex environments.

Dehazing network based on residual context attention and cross-layer fusion

Current image dehazing algorithms based on deep learning usually use traditional convolutional layers when extracting features, which easily causes the loss of image details and edge information, ignores the position information of the image when extracting features, and ignores the original information of the image when fusing features, and cannot restore high-quality haze-free images with complete structure and clarity. To address this problem, a dehazing algorithm based on residual context attention and cross-layer feature fusion is proposed. Firstly, the residual group structure is obtained by serializing the proposed residual context blocks, and the features of the first two layers of the network, i.e., the shallow layers, are extracted to obtain rich context information of the shallow layers; secondly, coordinate attention is introduced to establish an attention map with position information, and it is applied to the residual context feature extraction and placed in the third layer of the network, i.e., the deep layer, to extract deeper semantic information; then, in the middle layer of the network, feature information from different resolution streams is fused across layers to enhance the information exchange between the shallow and deep layers to achieve the purpose of feature enhancement; finally, the features with rich semantic information obtained by the network are aggregated with the original input, thereby improving the restoration effect. Experimental results on the RESIDE dataset and the Haze4K dataset show that the proposed algorithm achieves good results in both visual effects and objective indicators.

Separation of Macro- and Micro-Texture to Characterize Skid Resistance of Asphalt Pavement.

The skid resistance of asphalt pavement is an important factor affecting road safety. However, few studies have characterized the contribution of the macro- and micro-texture to the skid resistance of asphalt pavement. In this paper, the generalized extreme studentized deviate (GESD) and neighboring-region interpolation algorithm (NRIA) were used to identify and replace outliers, and median filters were used to suppress noise in texture data to reconstruct textures. On this basis, the separation of the macro- and micro-texture and the Monte Carlo algorithm were used to characterize the skid resistance of asphalt pavement. The results show that the GESD method can accurately identify outliers in the texture, and the median filtering can eliminate burrs in texture data while retaining more original detail information. The contribution of the macro-texture on the skid resistance is mainly attributed to the frictional resistance caused by the adhesion and elastic hysteresis, and the main contribution of the micro-texture is a micro-bulge cutting part in the friction mechanism. This investigation can provide inspiration for the interior mechanism and the specific relationship between the pavement textures and the skid resistance of asphalt pavement.

Origin and preservation mechanisms of organic matter in carbonate concretions from Lower Cambrian black shales in South China

Carbonate concretions are widely used in paleoclimate, paleoenvironmental, and paleontological studies, and even in the study of potential life on Mars. These petrological, elemental, isotopic, and lipid biomarker signals in Meso-Cenozoic carbonate concretions (relatively low thermal maturity) can effectively preserve details of seawater conditions and benthic ecosystems during the deposition of their host sediments/rocks. However, such research on Precambrian-Cambrian carbonate concretions under highly mature conditions remains scarce, and the ability of these ancient carbonate concretions to retain their original biogenic information remains uncertain. To achieve that, this study examines two Cambrian carbonate concretions, using samples from the center, transition, and rim of each, and their adjacent host black shales from the Lower Cambrian Qiongzhusi Formation in the Yangtze Block, South China. Organic and inorganic geochemical analyses were combined to elucidate the origin and preservation process of organic matter (OM) in these ancient Cambrian carbonate concretions. The results show that the thermal maturity of OM within these concretions (1.8 % EqVRo) is relatively low compared to their adjacent host shales (2.9 % EqVRo). Hopanes and steranes are detectable in both free and calcite-occluded hydrocarbons within these concretions, with concentrations of individual compounds ranging from 0.001 to 0.800 μg/g TOC, whereas kerogen-bound hydrocarbons lack detectable biomarkers. The results indicate that the two Cambrian carbonate concretions were formed mainly within the iron reduction and bacterial sulfate reduction zones, extending to depths of 10 to 38 m below the sediment-water interface. The OM within these concretions mainly inherited the initial unaltered signature of OM from the Qiongzhusi host shale. The carbonate concretions protected the internal OM from further thermal and secondary (e.g., biodegradation) alteration processes and might also prevent the formation of the conventional macromolecular skeletal kerogen manifested by the absence of bound biomarkers. The biomarkers, in both free and occluded forms, in the Cambrian carbonate concretions still retained their original source information, providing valuable insights into ancient biogeochemical processes during sediment burial and ancient seawater chemistry during the early Cambrian.

Multi-spectroscopic characterization system for cultural heritage materials analysis (SYSPECTRAL): Conception and example

A comprehensive understanding of chemical composition of cultural heritage materials usually requires several complementary analytical techniques. Given the fragility and value of artworks, minimizing or avoiding sampling and performing in situ analysis under ambient light is an important goal. This article outlines a novel prototype designed to merge LIBS, laser-induced fluorescence spectroscopy (LIF), Raman spectroscopy using a single pulsed laser, and reflectance spectroscopy in a multi-spectroscopic characterization system for cultural heritage analysis (SYSPECTRAL). The aim is to analyze cultural heritage materials in their original place, obtaining both elemental and molecular information at such same point that is not always insured with several separated experimental settings. The SYSPECTRAL system focuses on compactness, mobility, and ease of operation. Software designed for the prototype controls multi-spectroscopic measurements, allows for image capture, precise localization, and data acquisition. Reflectance spectra examined the material and colors at the surface, and the LIBS-LIF-Raman package examines the stratigraphic structure of a multi-layered painted sample.

Enhancing group decision-making: Maximum consensus aggregation for fuzzy cross-efficiency under hesitant fuzzy linguistic information

Group decision-making (GDM) is essential as it recognizes the inherent complexity of many decision scenarios, which frequently require the collective wisdom and knowledge of multiple decision-makers (DMs) to be effectively resolved. The proposed method aims to develop fuzzy data envelopment analysis (DEA) cross-efficiency models tailored to address GDM challenges, wherein attribute values are provided by DMs using hesitant fuzzy linguistic term sets (HFLTSs). For this purpose, we initially transform HFLTSs into their corresponding fuzzy envelopes, defined as trapezoidal fuzzy numbers (TrFNs). This conversion strategy effectively minimizes the loss in assessments based on HFLTSs while retaining the inherent ambiguity of the original information. Building upon this foundation, we develop fuzzy cross-efficiency models by leveraging the α-level sets of fuzzy envelopes. These models are designed to handle fuzzy input and output variables under various α-level sets, which are capable of considering all possible attribute values for each alternative. Following this, we implement a maximum consensus model using fuzzy cross-efficiency to assign weights to DMs. These weights facilitate the aggregation of individual fuzzy cross-efficiency intervals obtained from DMs’ assessments into collective ones, which serve to rank alternatives. Finally, we showcase the effectiveness and superiority of our proposal through numerical validation and comparative analysis.

The country-of-origin dilemma: domestic bias or need for transparency? Assessing consumer’s value of information for country-of-origin labelling in Italy

PurposeThe main objective of this study is to analyze consumers’ willingness to pay (WTP) for country-of-origin (COO) labels of two processed food products, disentangling the value of information (VOI) that consumers place on COO information from the value they place on specific countries of origin.Design/methodology/approachA convenience sample of 96 university students completed a face-to-face experiment that included a multiple price list and a discrete choice experiment. Data are analyzed employing interval censored regressions, and random parameter logit models.FindingsOur results indicate that, on average, consumers place a higher value on origin information when a country name is explicitly mentioned. Furthermore, COO information is, on average, more relevant to consumers for products with low involvement than for products with high involvement. Finally, the effect of ethnocentrism is heterogeneous across product categories.Research limitations/implicationsMandatory COO labeling may or may not reinforce domestic bias, depending on the rationale behind consumer support. If consumers are driven by blind ethnocentrism, it may lead to market inefficiencies. However, if they use COO as a cue to align their stated preferences with their choices, it will not. For this alignment to occur, consumers must be fully informed about product attributes, which requires new and smart methods of communicating product attributes.Originality/valueThe present study contributes to the literature on COO food labels, being the first in Italy to disentangle the VOI consumers place on origin information itself from the value they place on specific countries of origin. In addition, it is the first study that applies this methodology across different product categories, each of which has a different level of cultural sensitivity to consumers.

Dual-referenced assistive network for action quality assessment

Action quality assessment (AQA) aims to evaluate the performing quality of a specific action. It is a challenging task as it requires to identify the subtle differences between the videos containing the same action. Most of existing AQA methods directly adopt a pretrained network designed for other tasks to extract video features, which are too coarse to describe fine-grained details of action quality. In this paper, we propose a novel Dual-Referenced Assistive (DuRA) network to polish original coarse-grained features into fine-grained quality-oriented representations. Specifically, we introduce two levels of referenced assistants to highlight the discriminative quality-related contents by comparing a target video and the referenced objects, instead of obtrusively estimating the quality score from an individual video. Firstly, we design a Rating-guided Attention module, which takes advantage of a series of semantic-level referenced assistants to acquire implicit hierarchical semantic knowledge and progressively emphasize quality-focused features embedded in original inherent information. Subsequently, we further design a couple of Consistency Preserving constraints, which introduce a set of individual-level referenced assistants to further eliminate score-unrelated information through more detailed comparisons of differences between actions. The experiments show that our proposed method achieves promising performance on the AQA-7 and MTL-AQA datasets.

3D human model guided pose transfer via progressive flow prediction network

Human pose transfer is to transfer a conditional person image to a new target pose. The difficulty lies in modeling the large-scale spatial deformation from the conditional pose to the target one. However, the commonly used 2D data representations and one-step flow prediction scheme lead to unreliable deformation prediction because of the lack of 3D information guidance and the great changes in the pose transfer. Therefore, to bring the original 3D motion information into human pose transfer, we propose to simulate the generation process of real person image. We drive the 3D human model reconstructed from the conditional person image with the target pose and project it to the 2D plane. The 2D projection thereby inherits the 3D information of the poses which can guide the flow prediction. Furthermore, we propose a progressive flow prediction network consisting of two streams. One stream is to predict the flow by decomposing the complex pose transformation into multiple sub-transformations. The other is to generate the features of the target image according to the predicted flow. Besides, to enhance the reliability of the generated invisible regions, we use the target pose information which contains structural information from the flow prediction stream as the supplementary information to the feature generation. The synthesized images with accurate depth information and sharp details demonstrate the effectiveness of the proposed method.

A SILICA TRUNK PASSING THROUGH RESIN, ARRIVING TO SLAG, AFTER ALL AN ANTHROPOGENIC ARTIFICIAL FULGURITE: THE CURUÇÁ FULGURITE

In 2013, a peculiar and large-sized sample was brought to the Geosciences Museum of the Federal University of Pará, thought to look like a tree trunk, resin or even slag. The Curuçá region was confirmed as the area of origin, but the specific location was partially mysterious. . The first studies show that it consists simply of SiO2 in the form of cristobalite and quartz. The origin and occurrence form were soon discovered, which, combined with new mineralogical, chemical and microchemical data, demonstrated that it was an anthropogenic artificial type II fulgurite. The Curuçá fulguri

Masking quantum information in multipartite systems via Fourier and Hadamard Matrices

Abstract Quantum information masking (QIM) is a crucial technique for protecting quantum data from being accessed by local subsystems. In this paper, we introduce a novel method for achieving 1-uniform QIM in multipartite systems utilizing a Fourier matrix. We further extend this approach to construct an orthogonal array with the aid of a Hadamard matrix, which is a specific type of Fourier matrix. This allows us to explore the relationship between 2-uniform QIM and orthogonal arrays. Through this framework, we derive two distinct 2-uniform quantum states, enabling the 2-uniform masking of original information within multipartite systems. Furthermore, we prove that the maximum number of quantum bits required for achieving a 2-uniformly masked state is $2^{n}-1$, and the minimum is $2^{n-1}+3$. Moreover, our scheme effectively demonstrates the rich quantum correlations between multipartite systems and has potential application value in quantum secret sharing.

Joint contrastive self-supervised learning and weak-orthogonal product quantization for fast image retrieval

Hashing and quantization methods based on deep learning are being widely used in large-scale image retrieval. Most methods use traditional supervised models to train models. Although these methods achieve excellent performance, they rely too much on expensive label information and do not fully utilize data resources. These methods overly encourage binary codes to retain original information, which may lead to models building too much useless information at the expense of discriminative information that is more important for retrieval. To address these issues, we propose a self-supervised method that does not require labeled information, called Contrastive Self-Supervised Weak-Orthogonal Product Quantization (CSWPQ). The method is trained in a label-free self-supervised manner to reduce the need for labels by maximizing the similarity of different views of the same image and minimizing the similarity of different image views, which not only improves the generalization ability of the model but also enhances the robustness. We introduce a quantified contrastive learning method that is different from traditional contrastive learning to learn more discriminative image representations by constructing quantified image features of different views of the image. In addition, in order to reduce the quantization error caused by product quantization, we impose a weak-orthogonal constraint to increase the diversity of representations, reduce the information loss and computational overhead in the quantization process, and improve the computational speed. Extensive experiments on CIFAR-10, NUS-WIDE and FLICKR25K datasets show that our proposed method achieves better performance.

Methods of Translating Names of Material Culture Objects into Russian (Translation of Yuan Ke’s “Myths of Ancient China”)

This article analyzes the existing translation of Yuan Ke’s “Myths of Ancient China” (1960), carried out by E. I. Lubo-Lesnichenko and E. V. Puzitsky (1965), focusing on the strategies employed by the translators in conveying Chinese names into Russian in instances of cultural gaps. The aim of the article is to summarize the strategies adhered to by the authors in translating Chinese mythological narratives and to identify the most suitable methods for rendering names of material culture objects into Russian. An assessment is provided regarding the accuracy of translations of fragments of myths that mention concepts such as “tripod,” “jade hairpin,” “jade tablet,” “Jai-gong, Palace of Abstinence,” and others. The analysis reveals that the methods utilized by translators of ancient Chinese myths primarily include phonetic transliteration, literal translation, addition, omission, and annotation. However, the application of these strategies sometimes results in distortion and omission of the original information. This article proposes translational strategies, within the framework of foreignization, that should be adhered to when translating Chinese mythological narratives: addition, omission, transfer, substitution, division, combination, and transliteration.

On the Inference of Original Graph Information from Graph Embeddings

Graph embedding converts a graph data into a low dimensional space to preserve the original graph information. However, graph data can be reconstructed by malicious adversaries to train machine learning models from graph embeddings. This paper studies to what extent an adversary (without the original graph data) can recover the original graph data from graph embeddings. To quantify the original graph information leakage from graph embeddings, we develop a deep neural network model InferNet that can be used by adversaries to infer the original graph information from an adversary-accessible graph embedding database. More specifically, we propose the data-free reversed knowledge distillation technique to support the InferNet training even if the original graph dataset is absent. To ensure the performance of InferNet, we design two cycle-consistency loss functions to have an interactive training of InferNet over three series of datasets. To further enhance the performance of InferNet, we provide a joint training algorithm that simultaneously trains the pseudo-sample generator and InferNet, which significantly reduces the storage space. We evaluate the performance of InferNet on three datasets, and the intensive experiments demonstrate that InferNet can infer the original graph information from the graph embedding dataset with high accuracy.

EMFSA: Emoji-based multifeature fusion sentiment analysis

Short texts on social platforms often suffer from insufficient emotional semantic expressions, sparse features, and polysemy. To enhance the accuracy achieved by sentiment analysis for short texts, this paper proposes an emoji-based multifeature fusion sentiment analysis model (EMFSA). The model mines the sentiments of emojis, topics, and text features. Initially, a pretraining method for feature extraction is employed to enhance the semantic expressions of emotions in text by extracting contextual semantic information from emojis. Following this, a sentiment- and emoji-masked language model is designed to prioritize the masking of emojis and words with implicit sentiments, focusing on learning the emotional semantics contained in text. Additionally, we proposed a multifeature fusion method based on a cross-attention mechanism by determining the importance of each word in a text from a topic perspective. Next, this method is integrated with the original semantic information of emojis and the enhanced text features, attaining improved sentiment representation accuracy for short texts. Comparative experiments conducted with the state-of-the-art baseline methods on three public datasets demonstrate that the proposed model achieves accuracy improvements of 2.3%, 10.9%, and 2.7%, respectively, validating its effectiveness.