Search Results (1,231)

Studies on dental implant abutments’ geometric design and material selection offer significant innovations and results. These studies aim to improve the abutments’ functionality and aesthetic performance, minimize microcavities’ formation, and ensure implant-supported prostheses’ longevity. For example, CAD-CAM fabricated custom abutments have been found to produce a better marginal fit and fewer microgaps than standard abutments. In an in vitro study, transepithelial abutments offered lower microgap values than titanium-based abutments and provided a better fit at the implant–abutment interface. It is known that studies to improve mechanical and biological performance with Polyether Ether Ketone (PEEK) material have been addressed. New materials such as PEEK and zirconia have offered significant advantages in biocompatibility and aesthetics. Along with those studies, different abutment designs are also important. Abutment geometry is optimized to improve stress distribution and minimize peri-implant bone loss. In implant and abutment connections with different angles, mechanical life performances may vary depending on static and dynamic load. These studies emphasize the importance of material research on different types of connections to improve dental implants’ durability, homogeneous load distribution, and reliability. The abutment parts used in implant treatment are insufficient to distribute the load homogeneously against chewing pressure due to their materials and geometry. Non-uniform load distribution damages the abutment and the prosthetic crown, accelerating the wear process. This study aimed to create different abutment designs to improve dental implants’ biomechanical performance and longevity. This study aimed to increase the mechanical durability of the implant–abutment connection by reducing stress concentrations in response to masticatory compression on the abutment in different directions and forces and to guarantee the long-term success of the implant system by providing a more homogeneous stress distribution. It aimed to apply different forces in the axial direction to these models in a simulation environment and to calculate and compare the deformation and stress load distribution. As a method, three-dimensional models of the parts used in implant treatments and forming the implant system were designed. Different abutment designs were created with these models. Taking the current material values used in implant treatments as a reference, finite element analysis (FEA) was performed by applying different axial loads to each implant system model in the ANSYS software (version 24.1). Comparative analysis graphs were prepared and interpreted for the stress values obtained after the applied load. This study evaluated the mechanical performance of different abutment models (A, B, C, D, and E) under a 100 N load using the Kruskal–Wallis test. The Kruskal–Wallis test showed significant differences between the groups (p < 0.001). The greatest difference was observed between models E and A (q′ = 6.215), with a significant difference also found between models C and A (q′ = 3.219, p < 0.005). Regarding stress values, the highest stress on the abutment was observed in Model B (97.4 MPa), while the lowest stress was observed in Model E (9.6 MPa). The crown exhibited the highest stress in Model B (22.7 MPa) and the lowest in Model E (17.3 MPa). The implant stress was highest in Model C (14.8 MPa) and lowest in Model B (11.3 MPa). The stress values for the cortical bone and cancellous bone were quite similar across the models, showing no significant differences. These findings indicate that the abutment design and material selection significantly impact mechanical performance. Among the implant systems created with five different abutment models, in which the existing abutment geometry was also compared, homogeneous and axial distribution of the load on the abutment was achieved, especially with viscoelastic and surface area increased abutment designs. Clinically, the inadequacy and limited mounting surface or geometry of the abutments used in today’s implant treatment applications have led to different design searches. It was concluded that the designs in this study, which are considered alternatives to existing abutment models, contribute positively to the mechanical life of the abutment material, considering the von Mises stresses and directions. This study brings a new perspective to today’s practices and offers an alternative to treatment practices. Full article

The clique transversal problem is a critical concept in graph theory, focused on identifying a minimum subset of vertices that intersects all maximal cliques in a graph. This problem and its variations—such as the k-fold clique, $\left\{k\right\}$-clique, minus clique, and signed clique transversal problems—have received significant interest due to their theoretical importance and practical applications. This paper examines the k-fold clique, $\left\{k\right\}$-clique, minus clique, and signed clique transversal problems on distance-hereditary graphs. Known for their distinctive structural properties, distance hereditary graphs provide an ideal framework for studying these problem variants. By exploring these issues in the context of distance-hereditary graphs, this research enhances the understanding of the computational challenges and the potential for developing efficient algorithms to address these problems. Full article

Accurately identifying the dynamic interaction effects and network structure characteristics of air pollution is essential for effective collaborative governance. This study investigates the spatial dynamic interactions of air pollution among 30 cities in the Central Plains Urban Agglomeration using convergent cross mapping. Social network analysis is applied to assess the overall and node characteristics of the spatial interaction network, while key driving factors are analyzed using an exponential random graph model. The findings reveal that air pollution levels in the Central Plains Urban Agglomeration initially increase before they decrease, with heavily polluted cities transitioning from centralized to sporadic distribution. Among the interactions, Heze’s air pollution impact on Kaifeng was the strongest, while Xinxiang’s impact on Changzhi was the weakest. The emission and receiving effects peaked during 2010–2012. The air pollution interactions among cities exhibit significant network characteristics, with block model results indicating that emitting and receiving relationships are primarily concentrated in the bidirectional spillover plate. Natural factors such as temperature and precipitation significantly influence the spatial interaction network. Economic and social factors like economic level and industrial sector proportion also have a significant impact. However, population density does not influence the spatial interaction network. This study contributes to understanding the spatial network of air pollution, thereby enhancing strategies for optimizing regional collaborative governance efforts to address air pollution. Full article

Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively reflect a person’s emotional state and have been widely studied in emotion recognition. However, the effective feature fusion and discriminative feature learning from EEG–fNIRS data is challenging. In order to improve the accuracy of emotion recognition, a graph convolution and capsule attention network model (GCN-CA-CapsNet) is proposed. Firstly, EEG–fNIRS signals are collected from 50 subjects induced by emotional video clips. And then, the features of the EEG and fNIRS are extracted; the EEG–fNIRS features are fused to generate higher-quality primary capsules by graph convolution with the Pearson correlation adjacency matrix. Finally, the capsule attention module is introduced to assign different weights to the primary capsules, and higher-quality primary capsules are selected to generate better classification capsules in the dynamic routing mechanism. We validate the efficacy of the proposed method on our emotional EEG–fNIRS dataset with an ablation study. Extensive experiments demonstrate that the proposed GCN-CA-CapsNet method achieves a more satisfactory performance against the state-of-the-art methods, and the average accuracy can increase by 3–11%. Full article

Passenger behavior prediction aims to track passenger travel patterns through historical boarding and alighting data, enabling the analysis of urban station passenger flow and timely risk management. This is crucial for smart city development and public transportation planning. Existing research primarily relies on statistical methods and sequential models to learn from individual historical interactions, which ignores the correlations between passengers and stations. To address these issues, this paper proposes DyGPP, which leverages dynamic graphs to capture the intricate evolution of passenger behavior. First, we formalize passengers and stations as heterogeneous vertices in a dynamic graph, with connections between vertices representing interactions between passengers and stations. Then, we sample the historical interaction sequences for passengers and stations separately. We capture the temporal patterns from individual sequences and correlate the temporal behavior between the two sequences. Finally, we use an MLP-based encoder to learn the temporal patterns in the interactions and generate real-time representations of passengers and stations. Experiments on real-world datasets confirmed that DyGPP outperformed current models in the behavior prediction task, demonstrating the superiority of our model. Full article

This paper introduces an advanced observer-based control strategy designed for fractional multi-agent systems operating in hostile environments. We take into account the dynamic nature of the agents with nonlinear delayed dynamics and consider external disturbances affecting the system. The manuscript presents an improved observation-based control approach tailored for fractional-order multi-agent systems functioning in challenging conditions. We also establish various applicable conditions governing the creation of observers and disturbance compensation controllers using the fractional Razmikhin technique, signed graph theory, and matrix transformation. Furthermore, our investigation includes observation-based control on switching networks by employing a typical Lyapunov function approach. Finally, the effectiveness of the proposed strategy is demonstrated through the analysis of two simulation examples. Full article

Background: The strength of historical buildings built in different centuries with various materials and construction techniques and harboring many structural problems depends on the structural system, geometrical condition, and material properties. Sungurlu clock tower, whose system and geometry are in good condition, has been damaged under environmental and climatic effects, earthquakes, and other loads, and has survived to the present day by preserving its structural integrity to a great extent with the repairs it has undergone. Methods: In addition to static analysis, the robustness and durability of the design of the tower were tested by dynamic analysis with the SAP2000 program. In the model that will represent the actual system behavior of the tower, the lengths of the elements; nodal points; bearings; joints; shapes such as bars, shells, and plates; characteristic values of the materials to be used; as well as the system, element sections, and all loads and combinations of masses or dynamic forces acting on the system are defined. Results: In the reports presented visually, the moment, shear force, axial forces, and other forces to which the tower was exposed after the architectural and structural problems were eliminated were seen in a diagram. Since the effects of the damage could not be predicted, in this study, to measure the reaction of the building against earthquakes and other loads, the numerical model representing its original condition was prepared and analyzed according to the theoretical method and assumptions made by the restitution, survey, and static observation reports. Conclusions: With this program, which allows for the preparation of this model, it was concluded that the loads coming to the structure according to the principles of ductility, rigidity, and strength could be safely transferred to the ground without causing damage to the structural system and its elements. From the deformation, stress, velocity, acceleration, and reaction force graphs obtained, it was understood that the tower exhibited the expected structural behavior under its own weight and live loads. The stress and reaction force graphs showed that the structural materials are adequate for the resistance of the structure and system against the existing loads and possible earthquakes. Full article

In the context of highly competitive markets, organizations face dynamic challenges, requiring effective solutions to maintain and enhance their competitive standing. Performance measurement, supported by advanced information systems, is critical for organizational improvement. This study involves the implementation of key performance indicators (KPIs) within an automotive components company. Insights from employees across various departments were gathered for the development and deployment of 22 new KPIs across the Purchasing, Sales, Logistics, Quality, Human Resources, Occupational Health and Safety, Research and Development, and Finance departments of the company. The new indicators implemented were applied to all the group’s companies and standardized throughout the companies’ group. As a result, the implementation of new indicators and the consultation of graphs and visual elements present in the dashboards developed using Power BI enabled senior managers to make detailed and precise analyses, which led to faster and more considered decisions. It also enabled senior managers to make comparisons between the results of the group’s different companies by looking at dynamic, interactive graphs. The methodologies and tools discussed (KPIs and dashboards) have broader applications across different industries, highlighting the relevance and versatility of KPIs and dashboards in organizational performance management. Full article

This paper presents the mathematical and physical analysis, as well as distinct types of exact wave solutions, of an important fluid flow dynamics model called the truncated M-fractional (1+1)-dimensional nonlinear Estevez–Mansfield–Clarkson (EMC) equation. This model is used to explain waves in shallow water, fluid dynamics, and other areas. We obtain kink, bright, singular, and other types of exact wave solutions using the modified extended direct algebraic method and the improved $(G^{\prime }/G)$-expansion method. Some solutions do not exist. These solutions may be useful in different areas of science and engineering. The results are represented as three-dimensional, contour, and two-dimensional graphs. Stability analysis is also performed to check the stability of the corresponding model. Furthermore, modulation instability analysis is performed to study the stationary solutions of the corresponding model. The results will be helpful for future studies of the corresponding system. The methods used are easy and useful. Full article

Mobile robots’ efficient path planning has long been a challenging task due to the complexity and dynamism of environments. If an occupancy grid map is used in path planning, the number of grids is determined by grid resolution and the size of the actual environment. Excessively high resolution increases the number of traversed grid nodes and thus prolongs path planning time. To address this challenge, this paper proposes an efficient path planning algorithm based on laser SLAM and an optimized visibility graph for mobile robots, which achieves faster computation of the shortest path using the optimized visibility graph. Firstly, the laser SLAM algorithm is used to acquire the undistorted LiDAR point cloud data, which are converted into a visibility graph. Secondly, a bidirectional A* path search algorithm is combined with the Minimal Construct algorithm, enabling the robot to only compute heuristic paths to the target node during path planning in order to reduce search time. Thirdly, a filtering method based on edge length and the number of vertices of obstacles is proposed to reduce redundant vertices and edges in the visibility graph. Additionally, the bidirectional A* search method is implemented for pathfinding in the efficient path planning algorithm proposed in this paper to reduce unnecessary space searches. Finally, simulation and field tests are conducted to validate the algorithm and compare its performance with classic algorithms. The test results indicate that the method proposed in this paper exhibits superior performance in terms of path search time, navigation time, and distance compared to D* Lite, FAR, and FPS algorithms. Full article

The accurate assessment of node influence is of vital significance for enhancing system stability. Given the structural redundancy problem triggered by the network topology deviation when an empirical network is copied, as well as the dynamic characteristics of the empirical network itself, it is difficult for traditional static assessment methods to effectively capture the dynamic evolution of node influence. Therefore, we propose a heuristic-based spatiotemporal feature node influence assessment model (HEIST). First, the zero-model method is applied to optimize the network-copying process and reduce the noise interference caused by network structure redundancy. Second, the copied network is divided into subnets, and feature modeling is performed to enhance the node influence differentiation. Third, node influence is quantified based on the spatiotemporal depth-perception module, which has a built-in local and global two-layer structure. At the local level, a graph convolutional neural network (GCN) is used to improve the spatial perception of node influence; it fuses the feature changes of the nodes in the subnetwork variation, combining this method with a long- and short-term memory network (LSTM) to enhance its ability to capture the depth evolution of node influence and improve the robustness of the assessment. Finally, a heuristic assessment algorithm is used to jointly optimize the influence strength of the nodes at different stages and quantify the node influence via a nonlinear optimization function. The experiments show that the Kendall coefficients exceed 90% in multiple datasets, proving that the model has good generalization performance in empirical networks. Full article

The rapid development of artificial intelligence (AI) and 5G paradigm brings infinite possibilities for data annotation for new applications in the industrial Internet of Things (IIoT). However, the problem of data annotation consistency under distributed architectures and growing concerns about issues such as data privacy and cybersecurity are major obstacles to improving the quality of distributed data annotation. In this paper, we propose a reputation-based asynchronous federated learning approach for digital twins. First, this paper integrates digital twins into an asynchronous federated learning framework, and utilizes a smart contract-based reputation mechanism to enhance the interconnection and internal interaction of asynchronous mobile terminals. In addition, in order to enhance security and privacy protection in the distributed smart annotation system, this paper introduces blockchain technology to optimize the data exchange, storage, and sharing process to improve system security and reliability. The data results show that the consistency of our proposed FedDTrep distributed intelligent labeling system reaches 99%. Full article

Accurate traffic flow forecasting is a crucial component of intelligent transportation systems, playing a pivotal role in enhancing transportation intelligence. The integration of Graph Neural Networks (GNNs) and Transformers in traffic flow forecasting has gained significant adoption for enhancing prediction accuracy. Yet, the complex spatial and temporal dependencies present in traffic data continue to pose substantial challenges: (1) Most GNN-based methods assume that the graph structure reflects the actual dependencies between nodes, overlooking the complex dependencies present in the real-world context. (2) Standard time-series models are unable to effectively model complex temporal dependencies, hindering prediction accuracy. To tackle these challenges, the authors propose a novel Transformer-based Spatiotemporal Graph Diffusion Convolution Network (TSGDC) for Traffic Flow Forecasting, which leverages graph diffusion and transformer to capture the complexity and dynamics of spatial and temporal patterns, thereby enhancing prediction performance. The authors designed an Efficient Channel Attention (ECA) that learns separately from the feature dimensions collected by traffic sensors and the temporal dimensions of traffic data, aiding in spatiotemporal modeling. Chebyshev Graph Diffusion Convolution (GDC) is used to capture the complex dependencies within the spatial distribution. Sequence decomposition blocks, as internal operations of transformers, are employed to gradually extract long-term stable trends from hidden complex variables. Additionally, by integrating multi-scale dependencies, including recent, daily, and weekly patterns, accurate traffic flow predictions are achieved. Experimental results on various public datasets show that TSGDC outperforms conventional traffic forecasting models, particularly in accuracy and robustness. Full article

In the rapidly evolving online marketplace, accurately predicting the demand for pre-owned items presents a significant challenge for sellers, impacting pricing strategies, product presentation, and marketing investments. Traditional demand prediction methods, while foundational, often fall short in addressing the dynamic and heterogeneous nature of e-commerce data, which encompasses textual descriptions, visual elements, geographic contexts, and temporal dynamics. This paper introduces a novel approach utilizing the Graph Neural Network (GNN) to enhance demand prediction accuracy by leveraging the spatial relationships inherent in online sales data, named SGNN. Drawing from the rich dataset provided in the fourth Kaggle competition, we construct a spatially aware graph representation of the marketplace, integrating advanced attention mechanisms to refine predictive accuracy. Our methodology defines the product demand prediction problem as a regression task on an attributed graph, capturing both local and global spatial dependencies that are fundamental to accurate predicting. Through attention-aware message propagation and node-level demand prediction, our model effectively addresses the multifaceted challenges of e-commerce demand prediction, demonstrating superior performance over traditional statistical methods, machine learning techniques, and even deep learning models. The experimental findings validate the effectiveness of our GNN-based approach, offering actionable insights for sellers navigating the complexities of the online marketplace. This research not only contributes to the academic discourse on e-commerce demand prediction but also provides a scalable and adaptable framework for future applications, paving the way for more informed and effective online sales strategies. Full article

SecuriDN v. $0.1$ is a tool for the representation of the assets composing the IT and the OT subsystems of Distributed Energy Resources (DERs) control networks and the possible cyberattacks that can threaten them. It is part of a platform that allows the evaluation of the security risks of DER control systems. SecuriDN is a multi-formalism tool, meaning that it manages several types of models: architecture graph, attack graphs and Dynamic Bayesian Networks (DBNs). In particular, each asset in the architecture is characterized by an attack graph showing the combinations of attack techniques that may affect the asset. By merging the attack graphs according to the asset associations in the architecture, a DBN is generated. Then, the evidence-based and time-driven probabilistic analysis of the DBN permits the quantification of the system security level. Indeed, the DBN probabilistic graphical model can be analyzed through inference algorithms, suitable for forward and backward assessment of the system’s belief state. In this paper, the features and the main goals of SecuriDN are described and illustrated through a simplified but realistic case study. Full article