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Traditional visual simultaneous localization and mapping (SLAM) systems are primarily based on the assumption that the environment is static, which makes them struggle with the interference caused by dynamic objects in complex industrial production environments. This paper aims to improve the stability of visual SLAM in complex dynamic environments through semantic segmentation and its optimization.

This paper proposes a real-time visual SLAM system for complex dynamic environments based on YOLOv5s semantic segmentation, named YLS-SLAM. The system combines semantic segmentation results and the boundary semantic enhancement algorithm. By recognizing and completing the semantic masks of dynamic objects from coarse to fine, it effectively eliminates the interference of dynamic feature points on the pose estimation and enhances the retention and extraction of prominent features in the background, thereby achieving stable operation of the system in complex dynamic environments.

Experiments on the Technische Universität München and Bonn data sets show that, under monocular and Red, Green, Blue - Depth modes, the localization accuracy of YLS-SLAM is significantly better than existing advanced dynamic SLAM methods, effectively improving the robustness of visual SLAM. Additionally, the authors also conducted tests using a monocular camera in a real industrial production environment, successfully validating its effectiveness and application potential in complex dynamic environment.

This paper combines semantic segmentation algorithms with boundary semantic enhancement algorithms to effectively achieve precise removal of dynamic objects and their edges, while ensuring the system's real-time performance, offering significant application value.

This paper aims to investigate the effect of different wing height layouts on the aerodynamic performance and flow structure of high-speed train, in a train-wing coupling method with multiple tandem wings installed on the train roof.

The improved delayed detached eddy simulation method based on shear stress transport k- ω turbulence model has been used to conduct computational fluid dynamics simulation on the train with three different wing height layouts, at a Reynolds number of 2.8 × 10⁶. The accuracy of the numerical method has been validated by wind tunnel experiments.

The wing height layout has a significant effect on the lift, while its influence on the drag is weak. There are three distinctive vortex structures in the flow field: wingtip vortex, train body vortex and pillar vortex, which are influenced by the variation in wing height layout. The incremental wing layout reduces the mixing and merging between vortexes in the flow field, weakening the vorticity and turbulence intensity. This enhances the pressure difference between the upper and lower surfaces of both the train and wings, thereby increasing the overall lift. Simultaneously, it reduces the slipstream velocity at platform and trackside heights.

This paper contributes to understanding the aerodynamic characteristics and flow structure of a high-speed train coupled with wings. It provides a reference for the design aiming to achieve equivalent weight reduction through aerodynamic lift synergy in trains.

The purpose of this study is to characterize the galvanic corrosion behavior of a simulated X80 pipeline steel welded joint (PSWJ) reconstructed by the wire beam electrode (WBE) and numerical simulation methods.

The galvanic corrosion of an X80 PSWJ was studied using WBE and numerical simulation methods. The microstructures of the coarse-grained heat affected zone, fine-grained heat affected zone and intercritical heat affected zone were simulated in X80 pipeline steel via Gleeble thermomechanical simulation processing.

Comparing the corrosion current density of coupled and isolated weld metal (WM), base metal (BM) and heat-affected zone (HAZ), the coupled WM exhibited a higher corrosion current density than isolated WM; the coupled BM and HAZ exhibited lower corrosion current densities than isolated BM and HAZ. The results exhibited that the maximum anodic galvanic current fitted the Gumbel distribution. Moreover, the numerical simulation results agreed well with the experimental data.

This study provides insight into corrosion evaluation of heterogeneous welded joints by a combination of experiment and simulation. The method of reconstruction of the welded joint has been proven to be a feasible approach for studying the corrosion behavior of the X80 PSWJ with high spatial resolution.

Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

This paper aims to explore how the term “mental health literacy” (MHL) is defined and understand the implications for public mental health and educational interventions.

An extensive search was conducted by searching PubMed, ERIC, PsycINFO, Scopus and Web of Science. Keywords such as “mental health literacy” and “definition” were used. The publication year ranges from 1997 to 2023.

In total, 17 papers met the inclusion criteria. Three distinct themes characterise the definitions of MHL: cognitive understanding; affective empowerment; and behavioural change. In the mental health educational context, inspiration, transformation and practice can be viewed as three stages for curriculum development.

Despite conducting a systematic literature review, there remains the possibility that certain relevant studies may not have been identified. Specifically, the selection criteria prioritise papers that explicitly addressed MHL.

Traditional educational approaches prioritise knowledge dissemination and attitude change; integrating MHL into mental health well-being curriculum advocates for action-oriented intervention to address mental health challenges.

Understanding different definitions and concepts in a field can be valuable for consolidating shared understandings, highlighting tensions and contradictions, and enhancing communication among researchers. Ironically, perhaps, the process also highlights the provisional nature of these definitions.

The exploration of MHL with three themes of definitions sheds light on the understanding of this concept. Three core themes in MHL definitions guide future curriculum development. This study underscores the importance of prioritise action-oriented intervention in mental health education, emphasising the need to move beyond knowledge dissemination towards transformative practices that promote holistic mental well-being.

Using a mobile phone is increasingly becoming recognized as very dangerous while driving. With a smartphone, users feel connected and have access to information. The inability to access smartphone has become a phobia, causing anxiety and fear. The present study’s aims are as follows: first, quantify the association between nomophobia and road safety among motorists; second, determine a cut-off value for nomophobia that would identify poor road safety so that interventions can be designed accordingly.

Participants were surveyed online for nomophobia symptoms and a recent history of traffic contraventions. Nomophobia was measured using the nomophobia questionnaire (NMP-Q).

A total of 1731 participants responded to the survey; the mean age was 33 ± 12, and 43% were male. Overall, 483 (28%) [26–30%] participants received a recent traffic contravention. Participants with severe nomophobia showed a statistically significant increased risk for poor road safety odds ratios and a corresponding 95% CI of 4.64 [3.35-6.38] and 4.54 [3.28-6.29] in crude and adjusted models, respectively. Receiver operator characteristic (ROC)-based analyses revealed that NMP-Q scores of = 90 would be effective for identifying at risk drivers with sensitivity, specificity and accuracy of 61%, 75% and 72%, respectively.

Nomophobia symptoms are quite common among adults. Severe nomophobia is associated with poor road safety among motorists. Developing screening and intervention programs aimed at reducing nomophobia may improve road safety among motorists.

The purpose of this study is to examine how the implementation of edge computing can enhance the progress of the circular economy within supply chains and to address the challenges and best practices associated with this emerging technology.

This study utilized a streamlined evaluation technique that employed Latent Dirichlet Allocation modeling for thorough content analysis. Extensive searches were conducted among prominent publishers, including IEEE, Elsevier, Springer, Wiley, MDPI and Hindawi, utilizing pertinent keywords associated with edge computing, circular economy, sustainability and supply chain. The search process yielded a total of 103 articles, with the keywords being searched specifically within the titles or abstracts of these articles.

There has been a notable rise in the volume of scholarly articles dedicated to edge computing in the circular economy and supply chain management. After conducting a thorough examination of the published papers, three main research themes were identified, focused on technology, optimization and circular economy and sustainability. Edge computing adoption in supply chains results in a more responsive, efficient and agile supply chain, leading to enhanced decision-making capabilities and improved customer satisfaction. However, the adoption also poses challenges, such as data integration, security concerns, device management, connectivity and cost.

This paper offers valuable insights into the research trends of edge computing in the circular economy and supply chains, highlighting its significant role in optimizing supply chain operations and advancing the circular economy by processing and analyzing real time data generated by the internet of Things, sensors and other state-of-the-art tools and devices.