Real Distances Research Articles

Ocular Accommodative and Pupillary Responses During Fixation on Augmented Reality With a Maxwellian Display.

This study aimed to investigate the changes in ocular refraction and pupillary diameter during fixation on augmented reality (AR) images using a Maxwellian display. Twenty-two healthy young volunteers (average age, 20.7 ± 0.5 years) wore a Maxwellian display device in front of their right eye and fixated on an asterisk displayed on both a liquid-crystal display (real target) and a Maxwellian display (AR target) for 29 seconds (real as a baseline for 3 seconds, AR for 13 seconds, and real for 13 seconds) at distances of 5.0, 0.5, 0.33, and 0.2 meters. A binocular open-view autorefractometer was used to measure the ocular refraction and pupillary diameter of the left eye. Accommodative (5.0 meters, 0.28 ± 0.29 diopter [D]; 0.5 meter, -0.12 ± 0.35 D; 0.33 meter, -0.43 ± 0.57 D; 0.2 meter, -1.20 ± 0.82 D) and pupillary (5.0 meters, 0.07 ± 0.22 mm; 0.5 meter, -0.08 ± 0.17 mm; 0.33 meter, -0.16 ± 0.20 mm; 0.2 meter, -0.25 ± 0.24 mm) responses were negative when the real target distances were farther away. The accommodative response was significantly and positively correlated with the pupillary response during fixation on the AR target (R2 = 0.187, P < 0.001). Fixating on AR images using a Maxwellian display induces accommodative and pupillary responses. Accommodative responses depend on the distance between real objects. Overall, the Maxwellian display does not completely eliminate accommodation in real space.

Deepfake Video Detection Using Facial Feature Points and Ch-Transformer

With the development of Metaverse technology, the avatar in Metaverse has faced serious security and privacy concerns. Analyzing facial features to distinguish between genuine and manipulated facial videos holds significant research importance for ensuring the authenticity of characters in the virtual world and for mitigating discrimination, as well as preventing malicious use of facial data. To address this issue, the Facial Feature Points and Ch-Transformer (FFP-ChT) deepfake video detection model is designed based on the clues of different facial feature points distribution in real and fake videos and different displacement distances of real and fake facial feature points between frames. The face video input is first detected by the BlazeFace model, and the face detection results are fed into the FaceMesh model to extract 468 facial feature points. Then the Lucas-Kanade (LK) optical flow method is used to track the points of the face, the face calibration algorithm is introduced to re-calibrate the facial feature points, and the jitter displacement is calculated by tracking the facial feature points between frames. Finally, the Class-head (Ch) is designed in the transformer, and the facial feature points and facial feature point displacement are jointly classified through the Ch-Transformer model. In this way, the designed Ch-Transformer classifier is able to accurately and effectively identify deepfake videos. Experiments on open datasets clearly demonstrate the effectiveness and generalization capabilities of our approach.

Decomposing geographical judgments into spatial, temporal and linguistic components

When mentally exploring maps representing large-scale environments (e.g., countries or continents), humans are assumed to mainly rely on spatial information derived from direct perceptual experience (e.g., prior visual experience with the geographical map itself). In the present study, we rather tested whether also temporal and linguistic information could account for the way humans explore and ultimately represent this type of maps. We quantified temporal distance as the minimum time needed to travel by train across Italian cities, while linguistic distance was retrieved from natural language through cognitively plausible AI models based on non-spatial associative learning mechanisms (i.e., distributional semantic models). In a first experiment, we show that temporal and linguistic distances capture with high-confidence real geographical distances. Next, in a second behavioral experiment, we show that linguistic information can account for human performance over and above real spatial information (which plays the major role in explaining participants’ performance) in a task in which participants have to judge the distance between cities (while temporal information was found to be not relevant). These findings indicate that, when exploring maps representing large-scale environments, humans do take advantage of both perceptual and linguistic information, suggesting in turn that the formation of cognitive maps possibly relies on a strict interplay between spatial and non-spatial learning principles.

Simulation of daily soft multifocal contact lenses using SimVis Gekko: from in-vitro and computational characterization to clinical validation

Multifocal contact lenses (MCLs) are one of the solutions to correct presbyopia, but their adoption is not widespread. To address this situation, visual simulators can be used to refine the adaptation process. This study aims to obtain accurate simulations for a visual simulator (SimVis Gekko; 2EyesVision) of daily soft MCL designs from four manufacturers. In-vitro characterization of these MCLs—several powers and additions- was obtained using NIMO TR-1504. From the averaged relative power profiles across powers, phase maps were reconstructed and the Through-Focus Visual Strehl metric was calculated for each MCL design. The SimVis Gekko simulation corresponding to each MCL design was obtained computationally and bench-validated. Finally, the MCL simulations were clinically validated involving presbyopic patients. The clinical validation results show a good agreement between the SimVis Gekko simulations and the real MCLs for through-focus visual acuity (TF-VA) curves and VA at three real distances. All MCL designs showed a partial correlation higher than 0.90 and a Root Mean Square Error below 0.07 logMAR between the TF-VA of simulations and Real MCLs across subjects. The validity of the simulation approach using SimVis Gekko and in-vitro measurements was confirmed in this study, opening the possibility to accelerate the adaptation of MCLs.

GIS TOOLS IN THE CONSERVATION AND SUSTAINABLE DEVELOPMENT NATIONAL PARKS, FORESTS AND RURAL AREAS

The problem of optimising the location of crops/plots in protected areas, national parks, farmland or forests involves trying to reconcile their proper functioning with nature conservation, the preservation of unique ecosystems or sustainable development. Such areas are crucial for the preservation of biodiversity and wildlife. This paper examines and discusses the impact of land consolidation in rural areas using GIS (Geographic Information System) tools. It performs a series of network analyses to determine the real distances (following the road network) and linear distances (rectilinear distances) between habitats and farmland. A tool was also developed to automate this process. The results obtained were visualised using map compositions, tables and graphs. The implemented project had a significant beneficial effect on the change in the land structure in the analysed village. The number of registered plots decreased by 40% (from 1,189 to 711) due to the consolidation process.

The contribution of semantic distance knowledge to size constancy in perception and grasping when visual cues are limited

To achieve a stable perception of object size in spite of variations in viewing distance, our visual system needs to combine retinal image information and distance cues. Previous research has shown that, not only retinal cues, but also extraretinal sensory signals can provide reliable information about depth and that different neural networks (perception versus action) can exhibit preferences in the use of these different sources of information during size-distance computations. Semantic knowledge of distance, a purely cognitive signal, can also provide distance information. Do the perception and action systems show differences in their ability to use this information in calculating object size and distance? To address this question, we presented ‘glow-in-the-dark’ objects of different physical sizes at different real distances in a completely dark room. Participants viewed the objects monocularly through a 1-mm pinhole. They either estimated the size and distance of the objects or attempted to grasp them. Semantic knowledge was manipulated by providing an auditory cue about the actual distance of the object: “20 cm”, “30 cm”, and “40 cm”. We found that semantic knowledge of distance contributed to some extent to size constancy operations during perceptual estimation and grasping, but size constancy was never fully restored. Importantly, the contribution of knowledge about distance to size constancy was equivalent between perception and action. Overall, our study reveals similarities and differences between the perception and action systems in the use of semantic distance knowledge and suggests that this cognitive signal is useful but not a reliable depth cue for size constancy under restricted viewing conditions.

A Detailed Comparative Analysis of You Only Look Once-Based Architectures for the Detection of Personal Protective Equipment on Construction Sites

For practitioners and researchers, construction safety is a major concern. The construction industry is among the world’s most dangerous industries, with a high number of accidents and fatalities. Workers in the construction industry are still exposed to safety risks even after conducting risk assessments. The use of personal protective equipment (PPE) is essential to help reduce the risks to laborers and engineers on construction sites. Developments in the field of computer vision and data analytics, especially using deep learning algorithms, have the potential to address this challenge in construction. This study developed several models to enhance the safety compliance of construction workers with respect to PPE. Through the utilization of convolutional neural networks (CNNs) and the application of transfer learning principles, this study builds upon the foundational YOLO-v5 and YOLO-v8 architectures. The resultant model excels in predicting six key categories: person, vest, and four helmet colors. The developed model is validated using a high-quality CHV benchmark dataset from the literature. The dataset is composed of 1330 images and manages to account for a real construction site background, different gestures, varied angles and distances, and multi-PPE. Consequently, the comparison among the ten models of YOLO-v5 (You Only Look Once) and five models of YOLO-v8 showed that YOLO-v5x6’s running speed in analysis was faster than that of YOLO-v5l; however, YOLO-v8m stands out for its higher precision and accuracy. Furthermore, YOLOv8m has the best mean average precision (mAP), with a score of 92.30%, and the best F1 score, at 0.89. Significantly, the attained mAP reflects a substantial 6.64% advancement over previous related research studies. Accordingly, the proposed research has the capability of reducing and preventing construction accidents that can result in death or serious injury.

Teaching location planning with the center‐of‐gravity method using real cities and distances

AbstractFacility placement is of strategic importance to most organizations as a well‐placed distribution center minimizes delivery costs and reduces fulfillment lead times, thus improving customer service levels. Because organizations value the location planning process, this teaching brief offers an exercise that analyzes the planning process using the center‐of‐gravity algorithm, a service area map, and real‐world constraints. The objective of the exercise is to identify two locations within a service area that minimize total network distribution costs. Our exercise is intended to complement standard course content and support instructors developing curricula for undergraduate operations management and supply chain management courses. Student‐based survey results indicate that the assignment enhanced classroom engagement and helped students better understand the complexities of location planning.

Modification of the wave algorithm for Euclidean distances calculation in transport applications

Many engineering problems could be either reduced to or directly involve the mathematical problem of finding the shortest path in some confined space with obstacles. To solve this problem, graph theory and dynamic programming offers quite a lot of exact and heuristic algorithms that work with directed and undirected, weighted and unweighted, connected and disconnected graphs. All these methods are based on their own techniques of calculating the distance between the vertices, the choice of which is dictated by the convenience of implementation and methodological considerations. As a result, the resulted paths found by the algorithms usually are characterized by a certain abstract length, which is often difficult to convert into Euclidean one. Nevertheless, spatial problems in engineering practice often require an answer in terms of real physical distances. Specifically, such a task fully arises in engineering applications related to the design of seaports, namely, in the laying of routes of intra-terminal transport, power supply networks, in the design of approach channels. A modification of the most common method for finding the shortest paths, the wave orthogonal-diagonal algorithm, is described in the paper. It allows you to include the geometric characteristics of its individual sections in the calculation of the length of the found route, which makes it possible to accurately estimate the length in the sense of the Euclidean distance.

Fast Three-Dimensional Posture Reconstruction of Motorcyclists Using OpenPose and a Custom MATLAB Script.

Ergonomics focuses on the analysis of the interaction between human beings and their working environment. During the riding of a motorbike, ergonomics studies the rider's posture on the motorbike. An incorrect posture can lead to physical and psychological discomfort, and can affect the perception of risk and the handling of the motorcycle. It is important for motorcyclists to adopt a good riding posture, for their health and road safety. The aim of this work is to propose a fast, cheap, and sufficiently robust method for the 3D reconstruction of the posture assumed by a motorcyclist. The stereo vision and the application of OpenPose made it possible to obtain a 3D reconstruction of the key points, and their evolution over time. The evaluation of the distances between the 3D key points, which represent the length of the various parts of the body, appears to remain sufficiently stable over time, and faithful to the real distances, as taken on the motorcyclist themself. The 3D reconstruction obtained can be applied in different fields: ergonomics, motorsport training, dynamics, and fluid dynamics analysis.

Experimental study on the hydraulic performance of the horizontal main drain of building drainage systems with solid waste

Abstract Water–air two-phase flow has been the theoretical basis of traditional building drainage systems. However, with the popularization of water saving devices, more research investigations need to be performed on the impact of solid waste on drainage systems. Through experiments under the solid waste scenario, the effects of drainage volume, diameter, and slope of the pipeline on the hydraulic performances of the horizontal main drain were investigated. The results showed that the drainage volume, diameter, and slope of the pipeline did not affect the location of the hydraulic jump on the horizontal main drain. The heights of the hydraulic jump were proportional to the discharge volumes and diameters of the pipeline, but inversely proportional to the slopes of the pipeline. The depth ratio was mainly responsible for the change of positive pressure value in the horizontal main drain. The depth ratios were proportional to the drainage volumes and inversely proportional to the diameters and slopes of the pipeline. The final deposition distances of solid wastes in the horizontal main drain were proportional to the discharge volumes and the pipeline slopes. The self-cleaning lengths of the clear water were 1.2∼1.6 times of the real final deposition distances of solid wastes.

Local wavenumber imaging with low-sampled wavefield for delamination characterization in composite plate

In this paper, a phase unwrapping-based local wavenumber imaging method is proposed for delamination detection in the composite plate. Local wavenumber imaging has shown significant advantages in damage localization and quantification. However, a dense of spatial measurements and a high requirement of sampling intervals are crucial for conventional spatial Fourier transform methods. To address these challenges, a phase unwrapping method adopting low-sampled wavefields for local wavenumber estimation is proposed for damage imaging. Specifically, the wavefield sampling process can divide the detection area into various grids while making each grid consist of four neighbor measurements. Under the far-field assumption, the wave path distances of grid measurements can be approximately obtained by projecting their real distances along the wave vector direction. Meanwhile, by revealing the phase differences of grid measurement pairs with the low-sampled wavefield data, the frequency-related wavenumbers are estimated subsequently. Hence, the local wavenumber can be recovered robustly by averaging the estimated wavenumbers from all measurement pairs in the grid. On this basis, the structural damage can be geometrically imaged and quantitatively evaluated. Both the simulation study and the experimental investigation have shown the efficiency of the phase unwrapping-based method for local wavenumber imaging, which demonstrates the strong capability of damage localization and quantification in composite structures with low-sampled wavefield.

Off-axis representation of hyperbolic mirror shapes for X-ray beamlines.

Mirror-centered, closed-form expressions for hyperbolic surfaces used in X-ray beamlines have been derived. Hyperbolic mirrors create a virtual focus or source point and can be used to lengthen or shorten the effective focal distance of a compound optical system. The derivations here express off-axis segments of a hyperbolic surface in terms of the real and virtual focal distances and the incident glancing angle at the center of the mirror. Conventional mathematical expressions of hyperbolic shapes describe the surfaces in Cartesian or polar coordinates centered on an axis of symmetry, necessitating cumbersome rotation and translation to mirror-centered coordinates. The representation presented here, with zero slope and the origin at the central point, is most convenient for modeling, metrology, aberration correction, and general surface analysis of off-axis configurations. The direct derivation avoids the need for nested coordinate transforms. A series expansion provides a helpful approximation; the coefficients of the implicit equation are also provided.

The experience of calculating distances between different types of settlements in Russia to assess the range of population migration

The problem of measuring distances in migration is not trivial, but important, for example, for delimiting the concepts of population migration and housing mobility. In conditions of limited access to detailed spatial data, researchers solve this issue in different ways. Only a few countries, such as Sweden, have the ability to calculate migration distances between point locations using Euclidean distance (in a straight line). In this article, as applied to Russia, the measure of correspondence between Euclidean distances and real distances along transport routes is investigated. For this purpose, 3407 pairs of distances were calculated for randomly selected 23 municipal districts of Russia directly and along transport routes - roads. Also, 411 pairs of distances were calculated for settlements with no auto connection, using air traffic. These distances give an idea of the migrations of different distances in Russia. As a result, for hypothetical resettlements within the selected municipal districts, real road distances exceed Euclidean distances by 51%. For resettlements between the centers of districts and urban districts within the region, this indicator exceeds 40%, and for interregional resettlements, the differences are 33%. The air service used “straightens” the distances, but even taking it into account, the real distances (mainly long-range, interregional) exceed the Euclidean ones by 28.5%. The calculations performed to give an idea of the deviations of real, transport distances from the relatively easy-to-calculate Euclidean ones, which can be used in the analysis of migration distances in Russia.

NGC 3314a/b and NGC 3312: Ram pressure stripping in Hydra I cluster substructure

Cluster substructure and ram pressure stripping in individual galaxies are among the primary pieces of evidence for the ongoing growth of galaxy clusters as they accrete galaxies and groups from their surroundings. We present a multiwavelength study of the center of the Hydra I galaxy cluster, including exquisite new MeerKAT H I and DECam Hα imaging which reveal conclusive evidence for ram pressure stripping in NGC 3312, NGC 3314a, and NGC 3314b through compressed H I contours, well-defined H I tails, and ongoing star formation in the stripped gas. In particular, we quantify the stripped material in NGC 3312, and NGC 3314a, which makes up between 8% and 35% of the gas still in the disk, is forming stars at ∼0.5 M⊙ yr−1, and extends ∼30−60 kpc from the main disk. The estimated stellar mass in the tails is an order of magnitude less than the H I mass. A fourth “ring” galaxy at the same velocity does not show signs of ram pressure in H I. In addition, we used the H I and stellar morphologies, combined with a Beta model of the hot intracluster medium, to constrain the real distances of the galaxies to the cluster center, and we used the chance alignment of NGC 3314b behind NGC 3314a to break the degeneracy between whether the galaxies are in front or in back of the cluster. The drag seen in the H I tails supports our preferred scenario that NGC 3312 and NGC 3314a are moving toward us as part of a foreground substructure which has already passed its pericenter and is on “out fall” from the cluster. The high surviving H I content of the galaxies may suggest that the substructure or intragroup medium can protect them from the harshest effects of ram pressure, or that the galaxies are in fact on more tangential orbits.

TNDP: Tensor-Based Network Distance Prediction With Confidence Intervals

The knowledge of network distances, in the form of delay or latency, for example, is beneficial to a number of distributed applications. Notice that it is difficult and expensive to implement global network measurements to obtain network distance, a feasible idea is to predict unknown distances by introducing network coordinates with limited network measurements. The existing solutions always represent the unknown network distances in a rather unique number. However, research and applications indicate that the real network distances are hard to be accurately figured out and changes subtly in an interval over time with the dynamic network environments. Accordingly, this article proposes a tensor-based network distance prediction (TNDP) approach to represent network distance with confidence intervals, by exploiting the random distance tensor and distributed matrix factorization. With a small set of network measurements among the nodes selected randomly, a distance matrix tensor has been established and factorized into the product of two location matrixes with the adaptive SGD-based learning solution. By introducing the important training determinants, including weight matrix, regularization coefficient, and minibatch gradient descent with the exponential decay rates, the unknown distances among nodes can be accurately inferred in the forms of confidence intervals, with quick convergence and less overfitting. Extensive experimental simulations on a wide variety of available data sets demonstrate that TNDP is superior to other approaches in terms of accuracy for network distance prediction.

Perceived vs. Actual Distance to Transit in Santiago, Chile

To plan public transport supply and to increase transit patronage, it is indispensable to be aware of people’s perception of the actual supply, that is, how extensive and accurate people’s knowledge is regarding the concrete features of transport supply, such as the locations of transit stops, timetables, and fare structures. This paper addresses the gap between the perceived and real distances to transit stops. The comparison between the real and perceived supply shows a considerably accurate perception. Nevertheless, a pattern is apparent. Metro supply has a higher overestimation rate than buses, i.e., walking distances are perceived as significantly shorter than they actually are. These results suggest that the transport mode is correlated with perception. This mode has the best reputation in the studied city, in contradistinction to buses, whose reputation was heavily damaged by the public transit reform occurring at the time of the research.

Spatial-temporal fusion graph framework for trajectory similarity computation

Trajectory similarity computation is an essential operation in many applications of spatial data analysis. In this paper, we study the problem of trajectory similarity computation over spatial network, where the real distances between objects are reflected by the network distance. Unlike previous studies which learn the representation of trajectories in Euclidean space, it requires to capture not only the sequence information of the trajectory but also the structure of spatial network. To this end, we propose GTS, a brand new framework that can jointly learn both factors so as to accurately compute the similarity. It first learns the representation of each point-of-interest (POI) in the road network along with the trajectory information. This is realized by incorporating the distances between POIs and trajectory in the random walk over the spatial network as well as the loss function. Then the trajectory representation is learned by a Graph Neural Network model to identify neighboring POIs within the same trajectory, together with an LSTM model to capture the sequence information in the trajectory. On the basis of it, we also develop the GTS+ extension to support similarity metrics that involve both spatial and temporal information. We conduct comprehensive evaluation on several real world datasets. The experimental results demonstrate that our model substantially outperforms all existing approaches.

Possible origin of the spatiotemporal evolution of the 2008 earthquake swarm in Northwest-Bohemia, Czech Republic

The phenomenon ‘earthquake swarm’ is well known from numerous locations on Earth but its mechanism and trigger process are still an enigma. The influence of fluids on fluid-driven seismicity is generally well accepted but the temporal behaviour and the migration of activated phases is still an open question. We attempt to answer these open questions by combining the results of research on induced-seismicity with new reliable analyses of full moment tensors (FMTs) of the swarm earthquakes of 2008 to 2018 in Northwest-Bohemia, Czech Republic by Vavryčuk et al. (2017, 2021). The FMTs reveal evidence of compressive fracturing as the prevailing focal mechanism during the swarms. Our proposed hypotheses considered these earthquakes as the origin of subsequent pore pressure pulses in the respective active swarm phase. The diffusion of these pore pressure pulses, which could trigger the nearby earthquakes as well as the successive swarm phase in the vicinity after a respective diffusion process in space and time, are the target of this studies. Missing information regarding the real diffusion distances are compiled by a strict statistical approach of a one-to-one analysis of the swarm earthquakes. The analyses suggest that parts of the swarm earthquakes could be triggered by pore pressure diffusion with an estimated diffusivity range of approximately D = 0.01 to 3.0 m2/s. In exceptional cases, which make about 5% of all cases, we have to consider also diffusivities up to 15 m2/s. Examples of hydraulic diffusivities observed in our region of interest confirm this range. Such a process can also be assumed during further swarms in the years 2011 to 2018 because of the continuous and predominant occurrence of compressive fracturing as a focal mechanism beside pure shear and tensile fracturing. Our results could explain for the first time the missing link regarding the temporal and spatial migration of the subsequent swarm phases in our studied region at Nový Kostel (Northwest-Bohemia, Czech Republic).

Exploring the potential of aggregated traffic models for estimating network-wide emissions

This paper explores the potential of aggregated traffic models based on the Macroscopic Fundamental Diagram for building a network-wide monitoring system of travel emissions. Such a system consists of two layers. In a bottom layer, an aggregated traffic model predicts the network dynamics. In a top layer, an emission model estimates the total exhaust emissions. This paper discusses how to properly calibrate the aggregated traffic models of the bottom layer, to then estimate the total network emissions. We focus on the calibration of travel distances and the network partition definition. We propose a methodology that utilizes the concept of the detour ratio as a proxy to model real travel distances within the aggregated traffic models. This methodology increases the effectiveness of aggregated traffic models for predicting network-wide emissions in realistic scenarios. We also show that the definition of the network partitioning can significantly influence the total network emissions estimation. • We leverage our knowledge of the MFD dynamics for estimating total network emissions. • A microscopic simulation on the city of Innsbruck, Austria, serves as our ground-truth. • We utilize the COPERT IV model to estimate the emissions. • The results show the importance of the calibration of travel distances and network partitioning. • We propose a methodology based on the detour ratio to determine the travel distances.