Improved MobileNet V3-Based Identification Method for Road Adhesion Coefficient.

To enable the timely adjustment of the control strategy of automobile active safety systems, enhance their capacity to adapt to complex working conditions, and improve driving safety, this paper introduces a new method for predicting road surface state information and recognizing road adhesion coefficients using an enhanced version of the MobileNet V3 model. On one hand, the Squeeze-and-Excitation (SE) is replaced by the Convolutional Block Attention Module (CBAM). It can enhance the extraction of features effectively by considering both spatial and channel dimensions. On the other hand, the cross-entropy loss function is replaced by the Bias Loss function. It can reduce the random prediction problem occurring in the optimization process to improve identification accuracy. Finally, the proposed method is evaluated in an experiment with a four-wheel-drive ROS robot platform. Results indicate that a classification precision of 95.53% is achieved, which is higher than existing road adhesion coefficient identification methods.

An optimization algorithm for maximum quasi-clique problem based on information feedback model.

The maximum clique problem in graph theory is a well-known challenge that involves identifying the complete subgraph with the highest number of nodes in a given graph, which is a problem that is hard for nondeterministic polynomial time (NP-hard problem). While finding the exact application of the maximum clique problem in the real world is difficult, the relaxed clique model quasi-clique has emerged and is widely applied in fields such as bioinformatics and social network analysis. This study focuses on the maximum quasi-clique problem and introduces two algorithms, NF1 and NR1. These algorithms make use of previous iteration information through an information feedback model, calculate the information feedback score using fitness weighting, and update individuals in the current iteration based on the benchmark algorithm and selected previous individuals. The experimental results from a significant number of composite and real-world graphs indicate that both algorithms outperform the original benchmark algorithm in dense instances, while also achieving comparable results in sparse instances.

Two-stage multi-objective evolutionary algorithm for overlapping community discovery.

As one of the essential topological structures in complex networks, community structure has significant theoretical and application value and has attracted the attention of researchers in many fields. In a social network, individuals may belong to different communities simultaneously, such as a workgroup and a hobby group. Therefore, overlapping community discovery can help us understand and model the network structure of these multiple relationships more accurately. This article proposes a two-stage multi-objective evolutionary algorithm for overlapping community discovery problem. First, using the initialization method to divide the central node based on node degree, combined with the cross-mutation evolution strategy of the genome matrix, the first stage of non-overlapping community division is completed on the decomposition-based multi-objective optimization framework. Then, based on the result set of the first stage, appropriate nodes are selected from each individual's community as the central node of the initial population in the second stage, and the fuzzy threshold is optimized through the fuzzy clustering method based on evolutionary calculation and the feedback model, to find reasonable overlapping nodes. Finally, tests are conducted on synthetic datasets and real datasets. The statistical results demonstrate that compared with other representative algorithms, this algorithm performs optimally on test instances and has better results.

Multimodal multi-objective optimization algorithm based on hierarchical environment selection strategy.

The article proposes an optimization algorithm using a hierarchical environment selection strategyto solve the deficiencies of current multimodal multi-objective optimization algorithms in obtaining the completeness and convergence of Pareto optimal Sets (PSs). Firstly, the algorithm in this article is framed by a differential evolutionary algorithm (DE) and uses a special crowding distance to design a neighborhood-based individual variation strategy, which also ensures the diversity, and then special crowding distance is used to help populations with non-dominated sorting. In the stage of environmental selection, a strategy of hierarchical selection of individuals was designed, which selects sorted non-dominant ranked individual layer by layer according to the ratio, which allows potential individuals tobe explored. Finally, in the stage of evolution of individuals, the convergence and diversity of populations were investigated, anddifferent mutation strategies were selectedaccording to the characteristics of individuals. DE reproduction strategies are used for iteration, preventing individuals from avoiding premature convergence and ensuring the algorithm's searchability. These strategies help the algorithm to obtain more diverse and uniformly distributed PSs and Pareto Front (PF). The algorithm of this article compares with several other excellent algorithms on 13 test problems, and the test results show that all the algorithms of this article exhibit superior performance.

Study on the precursory characteristics and influencing factors of rockburst in the bifurcation area of coal seam.

To explore the precursory characteristics and influencing factors of rockburst in the bifurcation area of coal seam, the evolution and expansion of fracture and the energy accumulation and dissipation characteristics of coal-rock parting-coal structure (CRCS) during failure and instability process are explored from a micro-scopic perspective, and the influence of coal and rock parting parameters on the instability is studied. The following four points are addressed: (1) Compared with the single coal structure or the coal- rock combined structure, the CRCS can more directly reflect the geological structure characteristics of the coal seam in the bifurcated area; (2) The failure and instability process of CRCS includes two types of instability: slip and fracture. The slip instability is characterized by low strength and high energy release, which is very difficult to predict. (3) Before the failure of CRCS, there are several precursor signal characteristics, such as the shortened development time of the "stable-fracture-stable" cycle, abnormal slip dislocation of the contact surface, and rapid accumulation of rock fracture energy. (4) The inclination angle of the contact surface affects the instability form, the strength of the rock parting affects the instability state, and the thickness of the rock parting affects the impact tendency. The research results have important theoretical significance for preventing rockburst caused by failure and instability in bifurcated area of coal seam.

Regulation of forkhead box O transcription factor by insulin signaling pathway controls the reproductive diapause of the lady beetle, Coccinella septempunctata.

In biological control programs, knowledge about diapause regulation in natural enemy insects provides important insight for improving long-term storage, transportation, and field adoption of these biological control agents. As a natural predator of agricultural pests, the lady beetle Coccinella septempunctata has been commercially mass-cultured and widely employed in pest management. In some insects, insulin signaling, in conjunction with the downstream transcription factor Forkhead box O (FoxO), are master regulators of multiple physiological processes involved in diapause, but it is unclear whether insulin signaling and FoxO affect the diapause of C. septempunctata. In this study, we use a combination of approaches to demonstrate that insulin signaling and FoxO mediate the diapause response in C. septempunctata. In diapausing beetles, application of exogenous insulin and knocking down expression of CsFoxo with RNA interference (RNAi) both rescued beetles from developmental arrest. In non-diapausing beetles, knocking down expression of the insulin receptor (CsInR) with RNA interference (RNAi) arrested ovarian development and decreased juvenile hormone (JH) content to levels comparable to the diapause state. Taken together, these results suggest that a shutdown of insulin signaling prompts the activation of the downstream FoxO gene, leading to the diapause phenotype.

RNA Interference in the Egg Parasitoid, Trichogramma dendrolimi Matsumura.

The egg parasitoids, Trichogramma spp, are recognized as efficient biological control agents against various lepidopteran pests in agriculture and forests. The immature stages of Trichogramma offspring develop within the host egg, exhibiting remarkable diminutiveness (approximately 0.5 mm in adult length). RNA-interference (RNAi) methodology has emerged as a crucial tool for elucidating gene functions in numerous organisms. However, manipulating RNAi in certain small parasitoid species, such as Trichogramma, has generally posed significant challenges. In this study, we present an efficient RNAi method in Trichogramma denrolimi. The outlined procedure encompasses the acquisition and isolation of individual T. dendrolimi specimens from host eggs, the design and synthesis of double-stranded RNA (dsRNA), the in vitro transplantation and cultivation of T. dendrolimi pupae, the micro-injection of dsRNA, and the subsequent assessment of target gene knockdown through RT-qPCR analysis. This study furnishes a comprehensive, visually detailed procedure for conducting RNAi experiments in T. dendrolimi, thereby enabling researchers to investigate the gene regulation in this species. Furthermore, this methodology is adaptable for RNAi studies or micro-injections in other Trichogramma species with minor adjustments, rendering it a valuable reference for conducting RNAi experiments in other endoparasitic species.

Can carbon emission trading improve carbon emission performance? Evidence from a quasi-natural experiment in China.

Carbon emission trading policies play a key role in reducing carbon emissions through market-based mechanisms. In the context of China's carbon neutrality goals and carbon peaking targets, it is important to predict and evaluate the effectiveness of such policies. The combined impact of carbon trading policies on carbon emission reduction and economic output has not been well investigated in previous studies. In this study, the impact of carbon emission trading policies on regional carbon emission performance was assessed through mechanism analysis and empirical tests. The mechanism analysis showed that carbon emission intensity reduction relied on three mediating effects: technological innovation incentives, industrial structure optimization, and energy substitution. For the empirical test, the multi-time difference-in-differences (DID) method was adopted to study the impact using panel data from 30 provinces in China between 2005 and 2019. Moreover, the specific impact mechanism was further tested using mediating effects. The results showed that China's carbon trading policy has significantly affected the carbon emission performance of the pilot regions, and factors such as GDP per capita, urbanization level, and capital-labor ratio have notably contributed to the reduction of carbon emission intensity. The proportions of the three mediating effects in the total effect were estimated to be 60.98%, 23.17%, and 10.14%, respectively. This study provides an empirical approach to the study of the impact of carbon trading policy on carbon emission reduction and economic output and can serve as a reference for addressing climate change and alleviating conflicts between the environment and economic growth in similar regions.

The identification and expression pattern of the sex determination genes and their sex-specific variants in the egg parasitoid Trichogramma dendrolimi Matsumura (Hymenoptera: Trichogrammatidae).

Introduction: Trichogramma wasps are egg parasitoids of agricultural lepidopteran pests. The sex of Trichogramma is determined by its ploidy as well as certain sex ratio distorters, such as the endosymbiotic bacteria Wolbachia spp. and the paternal sex ratio (PSR) chromosome. The sex determination systems of hymenopterans, such as Trichogramma spp., involve cascades of the genes transformer (tra), transformer-2 (tra2), and doublesex (dsx) and are associated with sex-specific tra and dsx splicing. First, these genes and their sex-specific variants must be identified to elucidate the interactions between the sex ratio disorders and the sex determination mechanism of Trichogramma. Methods: Here, we characterized the sex determination genes tra, tra2, and dsx in Trichogramma dendrolimi. Sex-specific tra and dsx variants were detected in cDNA samples obtained from both male and female Trichogramma wasps. They were observed in the early embryos (1-10 h), late embryos (12-20 h), larvae (32 h and 48 h), pre-pupae (96 h), and pupae (144 h, 168 h, 192 h, and 216 h) of both male and female T. dendrolimi offspring. Results: We detected female-specific tra variants throughout the entire early female offspring stage. The male-specific variant began to express at 9-10 h as the egg was not fertilized. However, we did not find any maternally derived, female-specific tra variant in the early male embryo. This observation suggests that the female-specific tra variant expressed in the female embryo at 1-9 h may not have originated from the maternal female wasp. Discussion: The present study might be the first to identify the sex determination genes and sex-specific gene splicing in Trichogramma wasps. The findings of this study lay the foundation for investigating the sex determination mechanisms of Trichogramma and other wasps. They also facilitate sex identification in immature T. dendrolimi and the application of this important egg parasitoid in biological insect pest control programs.

Improving temporal smoothness and snapshot quality in dynamic network community discovery using NOME algorithm.

The goal of dynamic community discovery is to quickly and accurately mine the network structure for individuals with similar attributes for classification. Correct classification can effectively help us screen out more desired results, and it also reveals the laws of dynamic network changes. We propose a dynamic community discovery algorithm, NOME, based on node occupancy assignment and multi-objective evolutionary clustering. NOME adopts the multi-objective evolutionary algorithm MOEA/D framework based on decomposition, which can simultaneously decompose the two objective functions of modularization and normalized mutual information into multiple single-objective problems. In this algorithm, we use a Physarum-based network model to initialize populations, and each population represents a group of community-divided solutions. The evolution of the population uses the crossover and mutation operations of the genome matrix. To make the population in the evolution process closer to a better community division result, we develop a new strategy for node occupancy assignment and cooperate with mutation operators, aiming at the boundary nodes in the connection between the community and the connection between communities, by calculating the comparison node. The occupancy rate of the community with the neighbor node, the node is assigned to the community with the highest occupancy rate, and the authenticity of the community division is improved. In addition, to select high-quality final solutions from candidate solutions, we use a rationalized selection strategy from the external population size to obtain better time costs through smaller snapshot quality loss. Finally, comparative experiments with other representative dynamic community detection algorithms on synthetic and real datasets show that our proposed method has a better balance between snapshot quality and time cost.

Posterior concentration of Wolbachia during the early embryogenesis of the host dynamically shapes the tissue tropism of Wolbachia in host Trichogramma wasps.

Introduction: The bacterial endosymbiont, Wolbachia spp. induce thelytokous parthenogenesis in certain parasitoid wasps, such as the egg parasitoid wasps Trichogramma spp. To complete the cycle of vertical transmission, Wolbachia displays efficient transovarial transmission by targeting the reproductive tissues and often exhibits strong tissue-specific tropism in their host. Method: The present study aimed to describe the basic Wolbachia distribution patterns that occur during the development of Wolbachia-infected, thelytokous Trichogramma dendrolimi, and T. pretiosum. We used fluorescence in situ hybridization (FISH) to investigate Wolbachia signal dynamics during early embryogenesis (from 30 to 120 min). Wolbachia titers and distributions from the embryo to adult stages of Trichogramma after early embryogenesis were detected by absolute quantitative polymerase chain reaction (AQ-PCR) and FISH. The symmetry ratios (SR) of the Wolbachia signals were calculated using the SR odds ratios in the anterior and posterior parts of the host. The SR was determined to describe Wolbachia tropism during early embryogenesis and various developmental stages of Trichogramma. Results: Wolbachia was concentrated in the posterior part of the embryo during early embryogenesis and the various developmental stages of both T. dendrolimi and T. pretiosum. Wolbachia density increased with the number of nuclei and the initial mitotic division frequency during early embryogenesis. The total Wolbachia titer increased with postembryogenesis development in both T. dendrolimi and T. pretiosum. However, the Wolbachia densities relative to body size were significantly lower at the adult and pupal stages than they were at the embryonic stage. Discussion: The present work revealed that posterior Wolbachia concentration during early host embryogenesis determined Wolbachia localization in adult wasps. By this mechanism, Wolbachia exhibits efficient vertical transmission across generations by depositing only female Wolbachia-infected offspring. The results of this study describe the dynamics of Wolbachia during the development of their Trichogramma host. The findings of this investigation helped clarify Wolbachia tropism in Trichogramma wasps.

A task scheduling algorithm with deadline constraints for distributed clouds in smart cities.

Computing technologies and 5G are helpful for the development of smart cities. Cloud computing has become an essential smart city technology. With artificial intelligence technologies, it can be used to integrate data from various devices, such as sensors and cameras, over the network in a smart city for management of the infrastructure and processing of Internet of Things (IoT) data. Cloud computing platforms provide services to users. Task scheduling in the cloud environment is an important technology to shorten computing time and reduce user cost, and thus has many important applications. Recently, a hierarchical distributed cloud service network model for the smart city has been proposed where distributed (micro) clouds, and core clouds are considered to achieve a better network architecture. Task scheduling in the model has attracted many researchers. In this article, we study a task scheduling problem with deadline constraints in the distributed cloud model and aim to reduce the communication network's data load and provide low-latency services from the cloud server in the local area, hence promoting the efficiency of cloud computing services for local users. To solve the task scheduling problem efficiently, we present an efficient local search algorithm to solve the problem. In the algorithm, a greedy search strategy is proposed to improve the current solutions iteratively. Moreover, randomized methods are used in selecting tasks and virtual machines for reassigning tasks. We carried out extensive computational experiments to evaluate the performance of our algorithm and compared experimental results with Swarm-based approaches, such as GA and PSO. The comparative results show that the proposed local search algorithm performs better than the comparative algorithms on the task scheduling problem.

Ecological Factors Associated with the Distribution of Bemisia tabaci Cryptic Species and Their Facultative Endosymbionts.

The sweetpotato whitefly, Bemisia tabaci species complex, comprises at least 44 morphologically indistinguishable cryptic species, whose endosymbiont infection patterns often varied at the spatial and temporal dimension. However, the effects of ecological factors (e.g., climatic or geographical factors) on the distribution of whitefly and the infection frequencies of their endosymbionts have not been fully elucidated. We, here, analyzed the associations between ecological factors and the distribution of whitefly and their three facultative endosymbionts (Candidatus Cardinium hertigii, Candidatus Hamiltonella defensa, and Rickettsia sp.) by screening 665 individuals collected from 29 geographical localities across China. The study identified eight B. tabaci species via mitochondrial cytochrome oxidase I (mtCOI) gene sequence alignment: two invasive species, MED (66.9%) and MEAM1 (12.2%), and six native cryptic species (20.9%), which differed in distribution patterns, ecological niches, and high suitability areas. The infection frequencies of the three endosymbionts in different cryptic species were distinct and multiple infections were relatively common in B. tabaci MED populations. Furthermore, the annual mean temperature positively affected Cardinium sp. and Rickettsia sp. infection frequencies in B. tabaci MED but negatively affected the quantitative distribution of B. tabaci MED, which indicates that Cardinium sp. and Rickettsia sp. maybe play a crucial role in the thermotolerance of B. tabaci MED, although the host whitefly per se exhibits no resistance to high temperature. Our findings revealed the complex effects of ecological factors on the expansion of the invasive whitefly.

Statistical modeling: Assessing the anatomic variability of knee joint space width.

Population-based knee joint space width (JSW) assessments are promising for the prevention and early diagnosis of osteoarthritis. This study aimed to establish the statistical shape and alignment model (SSAM) of knee joints for assessing anatomic variation in knee JSW in the healthy Chinese male population. CT scans of asymptomatic knee joints of healthy male participants (n = 107) were collected for manual segmentation to create mesh samples. The as-scanned positional error was reduced by a standard processing flow of deformable mesh registration. Principal component analysis (PCA) was performed to create a tibiofemoral SSAM that was trained on all mesh samples. The anatomic variability of the JSW in the healthy Chinese male population was then assessed using the SSAM with regression analysis and 3D analysis by color-coded mapping. Almost all PCA modes had a linear influence on the anatomic variation of the medial and lateral JSW. The JSW variability within the SSAM was mainly explained by mode 1 (45.1 % of variation), demonstrating that this mode had the greatest influence on JSW variation. 3D assessment of the JSW showed that the minimum medial JSW varied from 2.76 to 3.23 mm, and its site shifted a short distance on the medial tibial plateau. The root-mean-square fitting and generalization errors of the SSAM were below 1 mm. This study will benefit the design and optimization of prosthetic devices, and may be applicable to the prevention and early diagnosis of osteoarthritis.

Wolbachia-infected Trichogramma dendrolimi is outcompeted by its uninfected counterpart in superparasitism but does not have developmental delay.

BACKGROUND: Wolbachia infection increases the superparasitism frequency of Trichogramma females and provides an opportunity for horizontal intraspecific transmission. However, superparasitism may lead to interstrain competition between Wolbachia-infected Trichogramma offspring and their uninfected counterparts. This study investigated the outcome of interstrain intrinsic competition between Wolbachia-infected thelytokous strain (W) and uninfected bisexual strain (B) of Trichogramma dendrolimi. To determine the developmental rate of both strains, the sizes of immature stages of T. dendrolimi offspring at different times after parasitisation were measured in single parasitism and superparasitism conditions. RESULTS: The results reflect increased superparasitism by Wolbachia-infected females compared with uninfected females. Trichogramma females did not discriminate between host eggs previously parasitised by either B or W females. When the first oviposition was performed by B females, the B offspring outcompeted W offspring deposited later. Although when W offspring was deposited 8 h earlier than the B offspring, it gained no advantage over B offspring. Regardless of parasitism conditions, differences in the development rate between W and B offspring were not significant. CONCLUSION: The results reconfirmed that W females presented a higher tendency for superparasitism than B females, and showed that B offspring outcompeted W offspring even when the latter was deposited 8 h earlier. The inferiority of Wolbachia-infected Trichogramma compared with their uninfected counterparts is not due to the developmental delay. This study provides insights into the effects of intrinsic competition on the control efficacy of Wolbachia-infected Trichogramma against pests in biological control programs. © 2022 Society of Chemical Industry.

Initial Solution Generation and Diversified Variable Picking in Local Search for (Weighted) Partial MaxSAT.

The (weighted) partial maximum satisfiability ((W)PMS) problem is an important generalization of the classic problem of propositional (Boolean) satisfiability with a wide range of real-world applications. In this paper, we propose an initialization and a diversification strategy to improve local search for the (W)PMS problem. Our initialization strategy is based on a novel definition of variables' structural entropy, and it aims to generate a solution that is close to a high-quality feasible one. Then, our diversification strategy picks a variable in two possible ways, depending on a parameter: continuing to pick variables with the best benefits or focusing on a clause with the greatest penalty and then selecting variables probabilistically. Based on these strategies, we developed a local search solver dubbed ImSATLike, as well as a hybrid solver ImSATLike-TT, and experimental results on (weighted) partial MaxSAT instances in recent MaxSAT Evaluations show that they outperform or have nearly the same performances as state-of-the-art local search and hybrid competitors, respectively, in general. Furthermore, we carried out experiments to confirm the individual impacts of each proposed strategy.

Comparative Analysis of the Performance of Complex Texture Clustering Driven by Computational Intelligence Methods Using Multiple Clustering Models.

Traditional texture cluster algorithms are frequently used in engineering; however, despite their widespread application, these algorithms continue to suffer from drawbacks including excessive complexity and limited universality. This study will focus primarily on the analysis of the performance of a number of different texture clustering algorithms. In addition, the performance of traditional texture classification algorithms will be compared in terms of image size, clustering number, running time, and accuracy. Finally, the performance boundaries of various algorithms will be determined in order to determine where future improvements to these algorithms should be concentrated. In the experiment, some traditional clustering algorithms are used as comparative tools for performance analysis. The qualitative and quantitative data both show that there is a significant difference in performance between the different algorithms. It is only possible to achieve better performance by selecting the appropriate algorithm based on the characteristics of the texture image.

Wolbachia-Driven Memory Loss in a Parasitic Wasp Increases Superparasitism to Enhance Horizontal Transmission.

Horizontal transmission of the endosymbiont, Wolbachia, may occur during superparasitism when parasitoid females deposit a second clutch of eggs on a host. Wolbachia may increase the superparasitism tendency of Trichogramma wasps by depriving their memory. To test this hypothesis, we investigated the effects of conditioning experience and memory inhibitors (actinomycin D [ACD] and anisomycin [ANI]) on memory capacity, and expressions of memory-related genes (CREB1 and PKA), and superparasitism frequency of Wolbachia-infected (TDW) and uninfected (TD) lines of Trichogramma dendrolimi after conditioning with lemon or peppermint odor. We detected the presence of Wolbachia in eggs, larvae, pre-pupae, pupae, and adults of Trichogramma by using fluorescence in situ hybridization. The results showed that TDW females had a more reduced memory capacity than TD females after conditioning. Compared with TD females, TDW females showed a higher proportion of superparasitism and a downregulation of CREB1 and PKA genes after conditioning. TD females fed ACD or ANI showed a higher tendency for superparasitism and a downregulation of CREB1 and PKA, along with memory loss after conditioning than TD females fed honey solution only. The presence of Wolbachia was detected in the anterior region of the larva, pre-pupa, and pupa, but was not found in the head of the adult. The results provide evidence of host behavioral manipulation of Wolbachia by depriving memory of host Trichogramma wasps based on Poulin' s criteria. These host behavioral changes led by Wolbachia may be caused by the virulence of Wolbachia on the nervous system of the host. IMPORTANCE The endosymbiotic bacteria, Wolbachia, live widely within cells of arthropods. Wolbachia are not only transmitted vertically from host mother to offspring, but are also transmitted horizontally among host individuals. Horizontal transmission is expected to occur during superparasitism when host parasitoid females deposit a clutch of eggs on a host previously parasitized by the same parasitoid species. Thus, a question is proposed regarding whether superparasitism behavior is a behavior modification induced by the symbiont to favor symbiont transmission. This study highlights behavioral mechanisms of Wolbachia-induced superparasitism in Trichogramma wasps and the manipulation of symbionts on host parasitoids.

Tourism cloud management system: the impact of smart tourism.

This study investigates the possibility of supporting tourists in a foreign land intelligently by using the Tourism Cloud Management System (TCMS) to enhance and better their tourism experience. Some technologies allow tourists to highlight popular tourist routes and circuits through the visualisation of data and sensor clustering approaches. With this, a tourist can access the shared data on a specific location to know the sites of famous local attractions, how other tourists feel about them, and how to participate in local festivities through a smart tourism model. This study surveyed the potential of smart tourism among tourists and how such technologies have developed over time while proposing a TCMS. Its goals were to make physical/paper tickets redundant via the introduction of a mobile app with eTickets that can be validated using camera and QR code technologies and to enhance the transport network using Bluetooth and GPS for real-time identification of tourists' presence. The results show that a significant number of participants engage in tourist travels, hence the need for smart tourism and tourist management. It was concluded that smart tourism is very appealing to tourists and can improve the appeal of the destination if smart solutions are implemented. This study gives a first-hand review of the preference of tourists and the potential of smart tourism.

Comparison and Analysis of Several Clustering Algorithms for Pavement Crack Segmentation Guided by Computational Intelligence.

Cracks are one of the most common types of imperfections that can be found in concrete pavement, and they have a significant influence on the structural strength. The purpose of this study is to investigate the performance differences of various spatial clustering algorithms for pavement crack segmentation and to provide some reference for the work that is being done to maintain pavement currently. This is done by comparing and analyzing the performance of complex crack photos in different settings. For the purpose of evaluating how well the comparison method works, the indices of evaluation of NMI and RI have been selected. The experiment also includes a detailed analysis and comparison of the noisy photographs. According to the results of the experiments, the segmentation effect of these cluster algorithms is significantly worse after adding Gaussian noise; based on the NMI value, the mean-shift clustering algorithm has the best de-noise effect, whereas the performance of some clustering algorithms significantly decreases after adding noise.