Search Results (13,275)

In recent years, additive manufacturing (AM) has been recognized as a transformative force in the dental industry, with the ability to address escalating demand, expedite production timelines, and reduce labor-intensive processes. Despite the proliferation of three-dimensional printing technologies in dentistry, the absence of well-established post-processing protocols has posed formidable challenges. This comprehensive review paper underscores the critical importance of precision in post-processing techniques for ensuring the acquisition of vital properties, encompassing mechanical strength, biocompatibility, dimensional accuracy, durability, stability, and aesthetic refinement in 3D-printed dental devices. Given that digital light processing (DLP) is the predominant 3D printing technology in dentistry, the main post-processing techniques and effects discussed in this review primarily apply to DLP printing. The four sequential stages of post-processing support removal, washing, secondary polymerization, and surface treatments are systematically navigated, with each phase requiring meticulous evaluation and parameter determination to attain optimal outcomes. From the careful selection of support removal tools to the consideration of solvent choice, washing methodology, and post-curing parameters, this review provides a comprehensive guide for practitioners and researchers. Additionally, the customization of post-processing approaches to suit the distinct characteristics of different resin materials is highlighted. A comprehensive understanding of post-processing techniques is offered, setting the stage for informed decision-making and guiding future research endeavors in the realm of dental additive manufacturing. Full article

This research aims to introduce and explore the theory of neutrosophic soft hyperalgebras (NSHAs), focusing on their core principles and potential applications in decision-making under uncertainty. By defining key operations such as intersection and union, we clarify the foundational characteristics of NSHAs and their relationship to soft hyperalgebras. The concepts of ξβ-identity NSHA and ξ-absolute NSHA are also examined to better understand their properties. The practical relevance of NSHA is demonstrated through applications in various fields, highlighting its adaptability in addressing complex decision-making scenarios. This approach offers a novel, more precise method for navigating uncertainty in areas such as project methodology selection, sensitivity analysis, and AI chatbot selection. Full article

Low Earth orbit (LEO) satellites have unique advantages in navigation because of their high signal intensity and rapid geometric changes in a short period. In order to solve the problem of constellation performance degradation after a potential failure pertaining one or more medium Earth orbit (MEO) navigation satellites, this paper designs the LEO navigation constellation and considers the task requirements of different stages of constellation deployment. Firstly, the LEO navigation constellation is designed by a non-dominated sorting genetic algorithm II (NSGA-II). The average position dilution of precision (PDOP) is 1.676, which is an improvement compared to the average PDOP offered by the four traditional GNSS. Secondly, the staged deployment of constellation takes into account the degradation of constellation performance caused by the failure of MEO navigation satellites, and the Monte Carlo method is used to analyze the case of three simultaneous satellite failures. The results show that a single satellite failure within each orbital plane and adjacent satellites with close phase separation has a great impact on the performance of the MEO navigation constellation. On this basis, a staged deployment strategy was adopted in order to balance cost, risk, and performance. The three phases deploy 66, 156, and 288 satellites, respectively; as a make-up constellation under contingencies, a navigation enhancement constellation, and an independent navigation constellation, the deployment of the staged sub-constellations meets the mission requirements. The constellation design and staged deployment method proposed in this paper can provide reference for the future study of LEO navigation constellations. Full article

Car accidents, a major US public safety issue, demand precise analysis and predictive models for mitigation. This study asks the following question: Can the safest car routes across the US be determined? The paper analyzes historical data to forecast future accidents and calculates the safest route between two locations. The study builds a predictive model utilizing statistical analyses, data mining, and machine learning. A joint probability density function (PDF) is devised to calculate the safest route for risk modeling, factoring in latitude and longitude. The model quantifies accident probabilities in areas and travel routes. Additionally, the safest direction can be determined using the gradient of the joint PDF curve. The predictive model enables policymakers to allocate resources proactively. The safest route selection enables drivers to navigate safer areas and routes, which can reduce the number of accidents. Through its analysis and joint PDF model, this research enriches accident analysis and prevention engineering, potentially fostering safer US roads. Full article

Advances in artificial intelligence (AI) in the medical sector necessitate the development of AI literacy among future physicians. This article explores the pioneering efforts of the AI in Medicine Association (AIM) at Brigham Young University, which offers a framework for undergraduate pre-medical students to gain hands-on experience, receive principled education, explore ethical considerations, and learn appraisal of AI models. By supplementing formal, university-organized pre-medical education with a student-led, faculty-supported introduction to AI through an extracurricular academic association, AIM alleviates apprehensions regarding AI in medicine early and empowers students preparing for medical school to navigate the evolving landscape of AI in healthcare responsibly. Full article

Unmanned Surface Vehicles (USVs) are rapidly becoming mission-indispensable for a variety of naval operations, from search and rescue to environmental monitoring and surveillance. Path planning lies at the heart of the operational effectiveness of USVs, since it represents the key technology required to enable the vehicle to transit the unpredictable dynamics of the marine environment in an efficient and safe way. The paper develops a critical review of the most recent advances in USV path planning and a novel classification of algorithms according to operational complexity: Basic Pathfinders, Responsive Pathfinders, and Advanced Strategic Pathfinders. Each category can adapt to different requirements, from environmental predictability to the desired degree of human intervention, and from stable and controlled environments to highly dynamic and unpredictable conditions. The review includes current methodologies and points out the state-of-the-art algorithmic approaches in their experimental validations and real-time applications. Particular attention is paid to the description of experimental setups and navigational scenarios showing the realistic impact of these technologies. Moreover, this paper goes through the key, open challenges in the field and hints at the research direction to leverage in order to enhance the robustness and adaptability of path planning algorithms. This paper, by offering a critical analysis of the current state-of-the-art, lays down the foundation of future USV path planning algorithms. Full article

In recent years, navigating rotor drones in complex and dynamic environments has been a significant challenge. This paper proposes an improved path planning method by integrating the enhanced Informed-RRT* algorithm with the Dynamic Window Approach (DWA) algorithm. Firstly, the probability weight function for sampling direction is dynamically adjusted based on the Euclidean distance between the start and goal points to obtain higher-quality sampling points. Secondly, the Artificial Potential Field (APF) concept is introduced during the generation of new nodes, applying the improved APF method to the elliptical sampling area to guide the direction of new node generation. This significantly reduces the number of redundant nodes, enhances the convergence speed, and improves the obstacle avoidance efficiency. Then, a polynomial fitting method is employed to optimize the path, yielding a smoother trajectory. Finally, the improved DWA algorithm is integrated. Initially, the Informed-RRT* algorithm generates an optimal path from the starting point to the goal point in the global space, which is then provided as an initial reference path to the DWA algorithm. During actual operation, the DWA algorithm dynamically adjusts control inputs based on the current position, speed, and reference path of the drone, ensuring that the drone can avoid dynamic obstacles and unknown static obstacles in real time while staying as close to the reference path as possible. Full article

Underwater navigation practice shows that the long baseline survey has the characteristics of coplanar configuration, flat geometry, and large refraction error, which brings challenges to underwater positioning. To address this challenge, this paper proposes a high-precision real-time range-difference location algorithm based on underwater long baseline measurement. Firstly, the system error sources of long baseline positioning are analyzed in detail, the propagation models of different system errors are constructed, and the effects of system error sources on the rangefinder are described. Secondly, the limitations of traditional range iterative location algorithms and geometric analytic location algorithms in long baseline locations are analyzed. Then, using the strategy of converting the long baseline range information into the distance difference information, a high-precision real-time distance difference location algorithm based on long baseline measurement is presented. Finally, the feasibility of the algorithm is analyzed from the perspective of precision analysis. Numerical simulation results show that compared with the two traditional long-baseline positioning algorithms, the proposed algorithm has higher positioning accuracy and potential application value in the field of underwater real-time positioning. Full article

With global warming and the gradual melting of Arctic sea ice, the navigation duration of the Northeast Passage (NEP) is gradually increasing. The dynamic changes in sea ice concentration (SIC) during navigation time are a critical factor affecting the navigation of the passage. This study uses multiple linear regression and random forest to analyze the navigation windows of the NEP from 1979 to 2022 and examines the critical factors affecting the dynamic changes in the SIC. The results suggest that there are 25 years of navigable windows from 1979 to 2022. The average start date of navigable windows is approximately between late July and early August, while the end date is approximately early and mid-October, with considerable variation in the duration of navigable windows. The explanatory power of RF is significantly better than MLR, while LMG is better at identifying extreme events, and RF is more suitable for assessing the combined effects of all variables on the sea ice concentration. This study also found that the 2 m temperature is the main influencing factor, and the sea ice movement, sea level pressure and 10 m wind speed also play a role in a specific period. By integrating traditional statistical methods with machine learning techniques, this study reveals the dynamic changes of the SIC during the navigation period of the NEP and identifies its driving factors. This provides a scientific reference for the development and utilization of the Arctic Passage. Full article

In order to explore the effect of loneliness on work engagement and the role of executive function in Chinese seafarers, two studies were carried out. Study 1 conducted a questionnaire survey of 1231 active Chinese seafarers to examine the relationship between seafarers’ loneliness, work engagement, and executive functions. Study 2 involved 177 seafarers as participants and created scenarios of seafarer work loneliness and non-loneliness using a recall paradigm, measuring components of executive function such as inhibitory control, working memory, and cognitive flexibility, as well as willingness to engage in work. The findings indicate that seafarers’ sense of loneliness can significantly negatively predict their work engagement, with inhibition control, working memory, and cognitive flexibility playing mediating roles. This provides new references and insights for alleviating the negative impact of loneliness on seafarers and enhancing their work engagement through cognitive approaches. Full article

In this study, several 3-dimensional (3-D) parameter estimation and localization algorithms for wireless near-field (NF) sources are proposed employing the uniform circular array (UCA) structure. In the single-base-station case, the algebraic relation is demonstrated between the azimuth angle under the far-field (FF) assumption and the actual NF source firstly. Secondly, two groups of antenna pairs are selected with distances less than half the wavelength, which are called short baselines in the interferometer method. The foregoing short-baseline method is qualified to localize an NF source. In addition, a long-baseline method is also proposed with further research. Two groups of antenna pairs with distances greater than half the wavelength are selected as two long baselines. In the multiple-base-stations case, another two novel algorithms are also proposed. The first one is the centroid algorithm, which is based on the centroid calculation of three estimated source locations. And the second one is the perpendicular foot algorithm, which takes the perpendicular foot within three estimated source locations as the final positioning location. Simulation results illustrate that the proposed algorithms can achieve higher localization accuracy than the conventional 3-D Root MUSIC method. Moreover, the long-baseline method performs better than the short-baseline method. And it is also shown that the proposed perpendicular foot algorithm shows better performance than the proposed centroid algorithm. Full article

3Cat-4 is the fourth member of the CubeSat series of UPC’s NanoSat Lab, and it was selected by the ESA Academy’s Fly Your Satellite! program in 2017. This mission aims at demonstrating the capabilities of nano-satellites, and in particular those based in the 1-Unit CubeSat standard, for challenging Earth Observation (EO) using Global Navigation Satellite System - Reflectometry (GNSS-R) and L - band microwave radiometry, as well as for Automatic Identification Systems (AIS). The following study presents the results of the thermal analysis carried out for this mission, evaluating different scenarios, including the most critical cases at both high and low temperatures. The results consider different albedos and orbital parameters in order to establish the optimal temperatures to achieve the best mission performance within the nominal temperatures, and in all operational modes of the satellite. Simulation results are included considering the thermal performance of other materials, such as Kapton, as well as the redesign of the optical properties of the satellite’s solar panels. The correlation with the thermal model and the TVAC test campaign was conducted at the ESA ESEC-GALAXIA facilities in Belgium. Full article

Assessment of the influence of vibration isolator parameters on the distribution of the system’s natural frequencies is a significant task in the design of vibration isolation systems. The root method was used to determine the natural frequencies of the controlled vibration isolator. For a certain feedback structure of a controlled electrodynamic type vibration isolator, the need for a consistent selection of parameters has been justified. A mathematical solution has been proposed for the approximate determination of the roots of the characteristic equation of the controlled vibration isolator, which enables the analytical assessment of the influence of the vibration isolator parameters on the distribution of its natural frequencies. The research has been conducted in relative parameters, which makes it possible to generalize the results. The specificity of the inertial dynamic vibration isolator, which in some cases is associated with the implementation of anti-resonance conditions, can lead to the fact that resonant frequencies can occur on both sides of the tuning frequency of the vibration isolator. The use of an elastic suspension on flat springs to protect navigation equipment from vibration allows reduction in the intensity of translational vibration, while not changing the orientation of the device relative to the Earth. The implementation of an elastic suspension according to the scheme of the inverted pendulum allows an increase in the effectiveness of vibration isolation, under the conditions of a controlled change of the vibration isolator parameters and due to the use of feedback. The results of this research can be used in precision systems, such as vibration isolators, laser processing equipment, ultraprecision measurements or medical devices. Full article

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting. Full article

Recently, indoor positioning has been one of the hot topics in the field of navigation and positioning. Among different solutions on indoor positioning, positioning with acoustic signals has its promise due to its relatively high accuracy in the line of sight scenarios, low cost, and ease of being implemented in smartphones. In this work, a novel acoustic positioning method, called RATBILS, is proposed, in which encoded chirp acoustic signals are modulated and transmitted by different acoustic base stations. The smartphones receive the signals and perform the following three steps: (1) preprocessing; (2) time of arrival (TOA) estimation; and (3) time difference of arrival (TDOA) calculation and location estimation. In the preprocessing stage, we use band pass filters to filter out low-frequency noise from the environment. At the same time, we perform a signal decoding function in order to lock onto the positioning source. In the TOA estimation stage, we conduct both coarse and fine detection to enhance the accuracy and robustness of TOA estimation. The primary goal of coarse detection is to establish a noise range for fine detection. The main objective of fine detection is to emphasize the intensity of the first arrival diameter and resistance with multipath and non-line-of-sight (NLOS) caused by human body obstruction. In the TDOA calculation and location estimation stage, we estimate the TDOA based on the TOA estimation and then use the TDOA results for position estimation. In order to evaluate the performance of the proposed RATBILS system, two indoor field tests are carried out. The test results show that the RATBILS system achieves a positioning error of 0.23 m at 92% in region 1 of scene 1 and is superior to the traditional threshold method. The RATBILS system achieves a positioning error of 0.56 m at 92% in region 2 of scene 1 and is superior to the traditional threshold method. In scene 2, the maximum average positioning error was 1.26 m, which is better than the 3.33 m and 3.87 m of the two traditional threshold methods. Full article