scholarly journals Nature benefits revisited: Differences in gait kinematics between nature and urban images disappear when image types are controlled for likeability

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256635
Author(s):  
Daria Burtan ◽  
Jeremy F. Burn ◽  
Ute Leonards
Keyword(s):  
Cognitive Processing ◽  
Positive Impact ◽  
A Priori ◽  
Urban Environments ◽  
Gait Velocity ◽  
Visual Discomfort ◽  
Urban Scenes ◽  
Photographic Images ◽  
Gait Kinematics ◽  
Environment Type

Exposure to urban environments requires more cognitive processing than exposure to nature; an effect that can even be measured analysing gait kinematics whilst people walk towards photographic images. Here, we investigated whether differences in cognitive load between nature and urban scenes are still present when scenes are matched for their liking scores. Participants were exposed to images of nature and urban scenes that had been matched a priori for their liking scores by an independent participant sample (n = 300). Participants (N = 44) were either asked to memorise each image during walking or to rate each image for its visual discomfort after each walk. Irrespective of experimental task, liking score but not environment type predicted gait velocity. Moreover, subjective visual discomfort was predictive of gait velocity. The positive impact of nature described in the literature thus might, at least in part, be due to people’s aesthetic preferences for nature images.

scholarly journals The nature effect in motion: visual exposure to environmental scenes impacts cognitive load and human gait kinematics

2021 ◽  
Vol 8 (1) ◽  
pp. 201100
Author(s):  
D. Burtan ◽  
K. Joyce ◽  
J. F. Burn ◽  
T. C. Handy ◽  
S. Ho ◽  
...  
Keyword(s):  
Cognitive Load ◽  
Cognitive Processing ◽  
Reaction Times ◽  
Short Exposure ◽  
Shape Discrimination ◽  
Visual Discomfort ◽  
Image Statistics ◽  
Urban Scenes ◽  
Gait Kinematics ◽  
Nature Scenes

Prolonged exposure to urban environments requires higher cognitive processing resources than exposure to nature environments, even if only visual cues are available. Here, we explored the moment-to-moment impact of environment type on visual cognitive processing load, measuring gait kinematics and reaction times. In Experiment 1, participants ( n = 20) walked toward nature and urban images projected in front of them, one image per walk, and rated each image for visual discomfort. Gait speed and step length decreased for exposure to urban as compared with nature scenes in line with gait changes observed during verbal cognitive load tasks. We teased apart factors that might contribute to cognitive load: image statistics and visual discomfort. Gait changes correlated with subjective ratings of visual discomfort and their interaction with the environment but not with low-level image statistics. In Experiment 2, participants ( n = 45) performed a classic shape discrimination task with the same environmental scenes serving as task-irrelevant distractors. Shape discrimination was slower when urban scenes were presented, suggesting that it is harder to disengage attention from urban than from nature scenes. This provides converging evidence that increased cognitive demands posed by exposure to urban scenes can be measured with gait kinematics and reaction times even for short exposure times.

scholarly journals Seeing nature from low to high levels: Mechanisms underlying the restorative effects of viewing nature images

2021 ◽  
Author(s):  
Claudia Menzel ◽  
Gerhard Reese
Keyword(s):  
Cognitive Processing ◽  
Spatial Information ◽  
Line Drawing ◽  
Mental Image ◽  
Well Being ◽  
Urban Environments ◽  
Urban Scenes ◽  
Line Drawings ◽  
Attention Restoration ◽  
Current Series

Nature can benefit human well-being and cognitive function. Merely watching images of nature compared to urban scenes, which differ in many lower-level processed properties, can have such effects, suggesting that solely the visual input evokes them. In the current series of studies with well-controlled stimuli, we investigated the role of lower- and higher-level processing on restorative effects evoked by nature and urban environments. Therefore, we used not only nature and urban photographs but also 1) versions that lack spatial information but retain certain image properties including those on regularity (i.e., phase-scrambled images), 2) line drawings that contain spatial information and thus allow for higher-level processing while lacking many diagnostic lower-level processed properties, and 3) words that lack any diagnostic image properties but allow higher cognitive processing and provide a mental image of the environment. We examined restorative effects after participants viewed either original, phase-scrambled, or line drawing versions of nature and urban images (Study 1), or nature- and urban-related words (Study 2). Although nature and urban scenes differed in several image properties, their differences did not lead to differences in perceived restoration when presented with phase-scrambled images. However, higher-level processing (i.e., recognizing the presented environment) led to pronounced restoration effects for nature compared to urban stimuli (original images, line drawings, and words). These findings have implications for current theories in the field (i.e., perceptual fluency account, attention restoration theory, and stress recovery theory).

scholarly journals Conditioning and PPP processing of smartphone GNSS measurements in realistic environments

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Ganga Shinghal ◽  
Sunil Bisnath
Keyword(s):  
Single Point ◽  
A Priori ◽  
Density Ratio ◽  
Satellite System ◽  
Quality Parameters ◽  
Urban Environments ◽  
Dual Frequency ◽  
Missing Measurements ◽  
Data Gaps ◽  
Point Positioning

AbstractSmartphones typically compute position using duty-cycled Global Navigation Satellite System (GNSS) L1 code measurements and Single Point Positioning (SPP) processing with the aid of cellular and other measurements. This internal positioning solution has an accuracy of several tens to hundreds of meters in realistic environments (handheld, vehicle dashboard, suburban, urban forested, etc.). With the advent of multi-constellation, dual-frequency GNSS chips in smartphones, along with the ability to extract raw code and carrier-phase measurements, it is possible to use Precise Point Positioning (PPP) to improve positioning without any additional equipment. This research analyses GNSS measurement quality parameters from a Xiaomi MI 8 dual-frequency smartphone in varied, realistic environments. In such environments, the system suffers from frequent phase loss-of-lock leading to data gaps. The smartphone measurements have low and irregular carrier-to-noise (C/N0) density ratio and high multipath, which leads to poor or no positioning solution. These problems are addressed by implementing a prediction technique for data gaps and a C/N0-based stochastic model for assigning realistic a priori weights to the observables in the PPP processing engine. Using these conditioning techniques, there is a 64% decrease in the horizontal positioning Root Mean Square (RMS) error and 100% positioning solution availability in sub-urban environments tested. The horizontal and 3D RMS were 20 cm and 30 cm respectively in a static open-sky environment and the horizontal RMS for the realistic kinematic scenario was 7 m with the phone on the dashboard of the car, using the SwiftNav Piksi Real-Time Kinematic (RTK) solution as reference. The PPP solution, computed using the YorkU PPP engine, also had a 5–10% percentage point more availability than the RTK solution, computed using RTKLIB software, since missing measurements in the logged file cause epoch rejection and a non-continuous solution, a problem which is solved by prediction for the PPP solution. The internal unaided positioning solution of the phone obtained from the logged NMEA (The National Marine Electronics Association) file was computed using point positioning with the aid of measurements from internal sensors. The PPP solution was 80% more accurate than the internal solution which had periodic drifts due to non-continuous computation of solution.

Tourism Marketing Stimulus Characteristics: A Self-Validation Analysis of Iran

2020 ◽  
pp. 004728752098114
Author(s):  
Arghavan Hadinejad ◽  
Anna Kralj ◽  
Noel Scott ◽  
Brent D. Moyle ◽  
Sarah Gardiner
Keyword(s):  
Least Squares ◽  
Cognitive Processing ◽  
Source Credibility ◽  
Positive Impact ◽  
Emotional Arousal ◽  
The Self ◽  
Three Dimensions ◽  
Multigroup Analysis ◽  
Tourism Marketing ◽  
Positive Attitudes

Prior self-validation hypothesis studies suggest that an individual’s attitude toward advertising is affected by the cognitive processing of the message and the valence of emotion. Yet, the effect of emotional arousal and stimulus credibility on attitudes toward a destination has not been discerned. This study employs the self-validation hypothesis to investigate the effect of the three dimensions of thinking on attitudes toward Iran. Participants (N = 416) were randomly assigned to a two (high vs. low arousal) × two (high vs. low source credibility) experiment. The partial least squares multigroup analysis results indicated that the emotionally arousing stimulus led to a greater influence of the amount and confidence of thought on attitudes. The effect of the amount of thought on attitude was greater in response to a credible stimulus. This research advances knowledge by revealing that emotional arousal has a significant positive impact on three dimensions of thinking and positive attitudes.

scholarly journals Bounding the Practical Error of Path Loss Models

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Caleb Phillips ◽  
Douglas Sicker ◽  
Dirk Grunwald
Keyword(s):  
A Priori ◽  
Path Loss ◽  
Urban Environments ◽  
Production Networks ◽  
Modeling Error ◽  
Propagation Models ◽  
Performance Accuracy ◽  
Rural And Urban ◽  
Standard Models ◽  
Loss Models

We seek to provide practical lower bounds on the prediction accuracy of path loss models. We describe and implement 30 propagation models of varying popularity that have been proposed over the last 70 years. Our analysis is performed using a large corpus of measurements collected on production networks operating in the 2.4 GHz ISM, 5.8 GHz UNII, and 900 MHz ISM bands in a diverse set of rural and urban environments. We find that the landscape of path loss models is precarious: typical best-case performance accuracy of these models is on the order of 12–15 dB root mean square error (RMSE) and in practice it can be much worse. Models that can be tuned with measurements and explicit data fitting approaches enable a reduction in RMSE to 8-9 dB. These bounds on modeling error appear to be relatively constant, even in differing environments and at differing frequencies. Based on our findings, we recommend the use of a few well-accepted and well-performing standard models in scenarios wherea prioripredictions are needed and argue for the use of well-validated, measurement-driven methods whenever possible.

scholarly journals Exploring the Tourism-Poverty Alleviation Nexus in the Brics Groupof Nations

Ekonomika ◽  
2020 ◽  
Vol 99 (1) ◽  
pp. 93-109
Author(s):  
Rufaro Garidzirai ◽  
Tafadzwa Matiza
Keyword(s):  
Poverty Alleviation ◽  
Positive Impact ◽  
A Priori ◽  
Control Variable ◽  
Household Consumption ◽  
Positive Sign ◽  
Series Data ◽  
Long Run ◽  
Short Run ◽  
Tourism Arrivals

The tourism-poverty alleviation nexus is becoming an increasingly significant subject of academic inquiry within the tourism economics discourse. Using time series data from the World Bank (1995–2017) in a P-ARDL model, the present study explores the relationship between tourism (receipts from exports, the travel subsector, hospitality and accommodation subsector) and poverty alleviation (final household consumption) with tourism arrivals as the control variable within the context of the BRICS group. The results suggest that receipts from the travel subsector and exports met the a priori expectation – positively influencing poverty alleviation within BRICS nations in the long run. Contrastingly, receipts from the hospitality and accommodation subsector did not meet the a priori expectation of a positive sign, with the results indicating statistical insignificance in the long run. However, receipts from the hospitality and accommodation were found to only influence poverty alleviation in the short run. Relatedly, the results suggest that increases in consumption associated with growth in tourism arrivals did not influence poverty in the BRICS. The results point to the heterogeneity of the influence of tourism on poverty alleviation, whereby certain dimensions of tourism contribute to poverty alleviation in the long run and others do so in the short run. Based on these findings it is recommended that BRICS countries harness their tourism potential and promote intra-BRICS tourism to maximise the positive impact of travel and tourism export receipts on household consumption, which catalyses poverty alleviation.

scholarly journals URBAN SCENE CLASSIFICATION USING FEATURES EXTRACTED FROM PHOTOGRAMMETRIC POINT CLOUDS ACQUIRED BY UAV

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 511-518 ◽  
Author(s):  
G. G. Pessoa ◽  
R. C. Santos ◽  
A. C. Carrilho ◽  
M. Galo ◽  
A. Amorim
Keyword(s):  
Point Clouds ◽  
Bare Soil ◽  
Urban Environments ◽  
Data Sources ◽  
Classification Error ◽  
Error Matrix ◽  
Urban Scenes ◽  
Optical Images ◽  
Combining Data ◽  
Quantitative Analyses

<p><strong>Abstract.</strong> Images and LiDAR point clouds are the two major data sources used by the photogrammetry and remote sensing community. Although different, the synergy between these two data sources has motivated exploration of the potential for combining data in various applications, especially for classification and extraction of information in urban environments. Despite the efforts of the scientific community, integrating LiDAR data and images remains a challenging task. For this reason, the development of Unmanned Aerial Vehicles (UAVs) along with the integration and synchronization of positioning receivers, inertial systems and off-the-shelf imaging sensors has enabled the exploitation of the high-density photogrammetric point cloud (PPC) as an alternative, obviating the need to integrate LiDAR and optical images. This study therefore aims to compare the results of PPC classification in urban scenes considering radiometric-only, geometric-only and combined radiometric and geometric data applied to the Random Forest algorithm. For this study the following classes were considered: buildings, asphalt, trees, grass, bare soil, sidewalks and power lines, which encompass the most common objects in urban scenes. The classification procedure was performed considering radiometric features (Green band, Red band, NIR band, NDVI and Saturation) and geometric features (Height – nDSM, Linearity, Planarity, Scatter, Anisotropy, Omnivariance and Eigenentropy). The quantitative analyses were performed by means of the classification error matrix using the following metrics: overall accuracy, recall and precision. The quantitative analyses present overall accuracy of 0.80, 0.74 and 0.98 for classification considering radiometric, geometric and both data combined, respectively.</p>

A Rural Perspective on COVID-19 Responses: Access, Interdependence and Community

2021 ◽  
Author(s):  
Margaret Sloan ◽  
Laura Trull ◽  
Maureen Malomba ◽  
Emily Akerson ◽  
Kelly Atwood ◽  
...  
Keyword(s):  
Qualitative Research ◽  
Rural Communities ◽  
A Priori ◽  
Urban Environments ◽  
Rural Populations ◽  
Nonprofit Leadership ◽  
The Press ◽  
Economic Infrastructure ◽  
Nonprofit Education ◽  
Health Complications

Much of the press on the pandemic has been focused on urban environments where the virus was quick to spread and the numbers of cases are high. Beyond the greater risk for COVID-19-related health complications, rural populations are particularly susceptible to disruptions in the economic infrastructure of their communities. This study explores the impacts of COVID-19 on rural communities and the responses of nonprofit and other community infrastructures. Using a strengths-based approach and mixedmethods design, this qualitative research asked rural residents and nonprofit leaders about their needs, challenges, and assets as a result of COVID-19. Themes relative to access, interdependence, and community emerged from a priori categories. The research offers implications for both nonprofit education and rural nonprofit leadership.

scholarly journals Innovative Technologies of the Electoral Process in the Light of the Rule of Law

2021 ◽  
Vol 3 (3) ◽  
pp. 124-150
Author(s):  
Evgeniy I. Kolyushin ◽  
Keyword(s):  
Rule Of Law ◽  
Positive Impact ◽  
A Priori ◽  
Electoral Process ◽  
Innovative Technologies ◽  
The Rule Of Law ◽  
Artificial Intelligence Technology ◽  
Traditional Approaches ◽  
Technical Solutions ◽  
Electoral Law

Introduction. In the electoral process, innovative technologies are legitimately and/or are de facto introduced. These are complex tools, the result of the integration of artificial intelligence, technology and human behaviour in elections. The article analyses the influence of these technologies on the electoral process from the standpoint of ensuring the rule of law. Theoretical Basis. Methods. In a State governed by the rule of law, the rule of law should fully apply to all stages of the electoral process. The paradigm (sample) of traditional approaches assumes direct physical commission of electoral actions by citizens, political parties, and other election participants, based on the presumption: “here and now”. The introduction of innovative technologies in the electoral process entails not only a change in forms, but also, as the quantitative growth increases, a change in the paradigm of the electoral process from traditional to digital, but at the same time the rule of law must be maintained. Results. The analysis of the functioning of innovative technologies shows their multi- vector and contradictory impact on the electoral process: from creating convenience for participants to complicating the structure, replacing electoral actions with transactions. Currently, there is no understanding of the legal significance of transactions in the electoral process, their relationship to electoral actions. The practice of Russian elections follows the path of changing the electoral process not by law, but by technical solutions that are a priori considered legitimate or do not allow their assessment from the position of the rule of law. As a result, the rule of electoral law is either ignored or is simply purely formal. The well-known principles of electoral law exist as if in parallel, without having a real impact on the introduction and regulation of innovative technologies of the electoral process. It is necessary to include innovative technologies in the subject of electoral law. A serious contribution to the solution of this problem would be the legislative consolidation of the principle of security of the electoral process. In this case, we are not talking about security in relation to public order. The content of the new principle should be a broad set of obligations of the state as an organiser of elections, including the obligation to fully verify these technologies, their transparency, new rights and guarantees of election participants, and public control. Discussion and Conclusion. The predominant use of innovative technologies aimed at providing voting services does not always have a positive impact on the integrity of the electoral process and the operation of the principles of electoral law. It is advisable to take care of the introduction and use of innovative technologies to serve them at all stages of the electoral process with unconditional guarantees of the rule of law.

scholarly journals Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Shaobo Wang ◽  
Pan Zhao ◽  
Biao Yu ◽  
Weixin Huang ◽  
Huawei Liang
Keyword(s):  
Neural Network ◽  
Knowledge Base ◽  
Prior Knowledge ◽  
Prediction Models ◽  
Short Term Memory ◽  
A Priori ◽  
Urban Environments ◽  
Intelligent Vehicles ◽  
Trajectory Prediction ◽  
Reasoning System

An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers. The existing prediction models cannot fully combine the prior and posterior knowledge in the driving scene and perform well only in a specific traffic scenario. This paper presents a long short-term memory (LSTM) neural network driven by knowledge. First, a driving knowledge base is constructed to describe the prior knowledge about a driving scenario. Then, the prediction reference baseline (PRB) based on driving knowledge base is determined by using the rule-based online reasoning system. Finally, the future trajectory of the target vehicle is predicted by an LSTM neural network based on the prediction reference baseline, while the predicted trajectory considers both posterior and prior knowledge without increasing the computation complexity. The experimental results show that the proposed trajectory prediction model can adapt to different driving scenarios and predict trajectories with high accuracy due to the unique combination of the prior and posterior knowledge in the driving scene.

Sign in / Sign up

Export Citation Format

Share Document