Perception Of Environment Research Articles

Impacts of environmental regulation perceptions on farmers’ intentions to adopt multiple smart hog breeding technologies: Evidence from rural Hubei, China

Smart hog breeding technologies have been viewed as practical solutions for environmental pollution, pork production inefficiency and food insecurity in rural areas. With difficulty in promoting these technologies, little is known about the role environmental regulation plays. Using 703 household survey data from rural Hubei, China, this paper identifies the impacts of different environmental regulation perceptions (i.e., supervisory, incentive and guiding regulation perceptions) on hog breeding farmers' intentions to adopt smart hog breeding technologies and on the intensity of their adoption intentions. Both multivariate probit model and ordered probit model are employed. Results show that smart hog breeding technologies are strong complementary. Farmers' environmental regulation perceptions greatly promote their intentions to adopt smart hog breeding technologies and their adoption intention intensity. Only the intensity of healthy farmers' adoption intentions is promoted by supervisory regulation perception. Compared to those without village cadres in family, the intensity of village-cadre group's adoption intentions is strengthened more by supervisory and guiding regulation perceptions. The greater incentive regulation perception is, the stronger the intensity of large-scale farmers' adoption intentions would be. The impacts of different environmental regulation perceptions on the intensity of adoption intentions are all partially mediated by value perception of smart agriculture. These findings suggest promoting smart hog breeding technologies jointly and underscore the importance of local environmental regulations in accelerating the diffusion of smart hog breeding technologies.

The Influence of Perceptions of the Park Environment on the Health of the Elderly: The Mediating Role of Social Interaction

The aging population has brought increased attention to the urgent need to address social isolation and health risks among the elderly. While previous research has established the positive effects of parks in promoting social interaction and health among older adults, further investigation is required to understand the complex relationships between perceptions of the park environment, social interaction, and elderly health. In this study, structural equation modeling (SEM) was employed to examine these relationships, using nine parks in Wuhan as a case study. The findings indicate that social interaction serves as a complete mediator between perceptions of the park environment and elderly health (path coefficients: park environment on social interaction = 0.45, social interaction on health = 0.46, and indirect effect = 0.182). Furthermore, the results of the multi-group SEM analysis revealed that the mediating effect was moderated by the pattern of social interaction (the difference test: the friend companionship group vs. the family companionship group (Z = 1.965 > 1.96)). Notably, family companionship had a significantly stronger positive impact on the health of older adults compared to friend companionship. These findings contribute to our understanding of the mechanisms through which urban parks support the physical and mental well-being of the elderly and provide a scientific foundation for optimizing urban park environments.

Understanding the influencing factors of tourists’ revisit intention in traditional villages

The intention of tourists to revisit traditional villages plays a significant role in their sustainable development. This study utilizes a cross-sectional survey design and collects 373 valid responses from Chinese tourists via a questionnaire. The questionnaire, based on a Likert 5-point scale, encompasses key constructs such as space Atmosphere, place attachment, perceived interest, experiential marketing, recreation perception, environmental image perception, well-being, information richness, and revisit intention. To ensure the quality of data, reliability and validity assessments were performed, followed by the verification of research hypotheses using Structural Equation Modeling (SEM). The findings indicate that perceived interest and experiential marketing are pivotal variables influencing tourists' revisit intentions. well-being emerges as a crucial driver that enhancing the likelihood of tourists' return visits. Additionally, recreation perception and perceived interest significantly impact well-being, while information richness positively moderates the effects of space atmosphere, place attachment, perceived interest, and environmental image perception on well-being. These findings can be utilized to formulate strategies that influence tourists' intentions to revisit traditional villages.

Sensor-Fused Nighttime System for Enhanced Pedestrian Detection in ADAS and Autonomous Vehicles.

Ensuring a safe nighttime environmental perception system relies on the early detection of vulnerable road users with minimal delay and high precision. This paper presents a sensor-fused nighttime environmental perception system by integrating data from thermal and RGB cameras. A new alignment algorithm is proposed to fuse the data from the two camera sensors. The proposed alignment procedure is crucial for effective sensor fusion. To develop a robust Deep Neural Network (DNN) system, nighttime thermal and RGB images were collected under various scenarios, creating a labeled dataset of 32,000 image pairs. Three fusion techniques were explored using transfer learning, alongside two single-sensor models using only RGB or thermal data. Five DNN models were developed and evaluated, with experimental results showing superior performance of fused models over non-fusion counterparts. The late-fusion system was selected for its optimal balance of accuracy and response time. For real-time inferencing, the best model was further optimized, achieving 33 fps on the embedded edge computing device, an 83.33% improvement in inference speed over the system without optimization. These findings are valuable for advancing Advanced Driver Assistance Systems (ADASs) and autonomous vehicle technologies, enhancing pedestrian detection during nighttime to improve road safety and reduce accidents.

Custom Anchorless Object Detection Model for 3D Synthetic Traffic Sign Board Dataset with Depth Estimation and Text Character Extraction

This paper introduces an anchorless deep learning model designed for efficient analysis and processing of large-scale 3D synthetic traffic sign board datasets. With an ever-increasing emphasis on autonomous driving systems and their reliance on precise environmental perception, the ability to accurately interpret traffic sign information is crucial. Our model seamlessly integrates object detection, depth estimation, deformable parts, and text character extraction functionalities, facilitating a comprehensive understanding of road signs in simulated environments that mimic the real world. The dataset used has a large number of artificially generated traffic signs for 183 different classes. The signs include place names in Japanese and English, expressway names in Japanese and English, distances and motorway numbers, and direction arrow marks with different lighting, occlusion, viewing angles, camera distortion, day and night cycles, and bad weather like rain, snow, and fog. This was done so that the model could be tested thoroughly in a wide range of difficult conditions. We developed a convolutional neural network with a modified lightweight hourglass backbone using depthwise spatial and pointwise convolutions, along with spatial and channel attention modules that produce resilient feature maps. We conducted experiments to benchmark our model against the baseline model, showing improved accuracy and efficiency in both depth estimation and text extraction tasks, crucial for real-time applications in autonomous navigation systems. With its model efficiency and partwise decoded predictions, along with Optical Character Recognition (OCR), our approach suggests its potential as a valuable tool for developers of Advanced Driver-Assistance Systems (ADAS), Autonomous Vehicle (AV) technologies, and transportation safety applications, ensuring reliable navigation solutions.

Reassessing urban park accessibility: an improved two-step floating catchment area method based on the physical activity services perspective

Urban parks, as important carrier of outdoor physical activity, can effectively reduce the incidence of chronic diseases and promote public health. The accessibility of parks directly affects residents’ levels of physical activity. However, current accessibility assessments do not consider physical activity needs into account and are unable to adapt to the current developmental needs of a healthy China. We propose a framework for evaluating park accessibility oriented towards a physical activity service perspective. First, we introduced a park attractiveness index based on physical activity preferences. The index encompasses not only objective park features but also subjective environmental perception features, which are evaluated using deep-learning techniques. Furthermore, the size of the park catchment area was correlated with the park attractiveness index, and the likelihood of park selection based on people's physical activity preferences was computed. Fuzhou City's Gulou District was selected for this case study. The results show the following: (1) Our proposed evaluation model can identify more underserved neighborhoods in scenarios geared toward physical activity requirements than traditional approaches. (2) The distribution of parks in the Gulou District was not sufficiently balanced, with the accessibility value at the 90th percentile only slightly higher than the average value of 6.35. There was obvious spatial differentiation, with a pattern of high values in the central area and low values in the surrounding areas. (3) Approximately 12.70 % of the population is located within service-blind zones and the overall level of accessibility is poor. This study offers novel ideas and approaches for optimizing the spatial layout of urban parks within a health-oriented framework. This aids planners and policymakers in enhancing the spatial layout of parks and ensuring equitable access to urban parks that foster physical activity.

Medical students perception of learning environment evidence from Jimma University Medical Centre

This study aims to assess medical students’ perception of the quality of the learning environment at Jimma University Medical Centre (JUMC), Ethiopia using the Dundee Ready Education Environment Measure (DREEM). A cross-sectional survey design was applied to assess students’ perceptions during their clinical attachment wards in JUMC. The DREEM inventory consisted of five sub-domains such as student perception of learning (SPL), student perception of teacher (SPT), student academic self-perception (SAP), student perception of the atmosphere (SPA), and student social self-perception (SSSP) that administered to 323 undergraduate medical students. Descriptive statistics, t-test and ANOVA were computed using SPSS 20 to analyse the data. The overall DREEM mean score was found to be 111.35 and SD of 13.67 from the maximum score of 200. In this study, the overall DREEM mean score of female was significantly higher than the mean of their male counterparts. Despite the ANOVA result across years, first year clinical year I students had higher perception of learning environment as compared to senior batches. In general, the overall means score of students on the DREEM scale revealed that they perceive the learning environment more positively. However, some areas need to be revised across all five domains of the learning environment to make improvements.

Examining the relationship between proposed anti-LGBTQ+ legislation and LGBTQ+ college student mental health: findings from the Healthy Minds Study, 2021–2022

Objective: This study aimed to investigate the association between proposed anti-LGBTQ+ legislation and depressive symptoms among LGBTQ+ college students. Participants: Participants (N = 72,135) included LGBTQ+ college students (N = 21,466) from over 530 colleges and universities, including technical and community institutions, across the United States who took part in the Healthy Minds Study (HMS). Methods: Data on proposed anti-LGBTQ+ legislation introduced in 2021–2022 were collected from all 50 US states, categorized, and analyzed. Individual-level data for 2021–2022 were obtained from the HMS, and a multilevel analysis was conducted to assess the association between proposed anti-LGBTQ+ legislation and depressive symptoms while considering individual factors. Results: A significant positive association between proposed anti-LGBTQ+ legislation and increased depressive symptoms among LGBTQ+ college students was found. This association remained significant after controlling for individual-level stressors and identities, including a sense of belonging, campus environment perceptions, first-generation student status, and transgender or gender non-conforming identity (TGNC). Conclusions: The findings underscore the need for mental health support and inclusive policies for LGBTQ+ college students, particularly in regions where anti-LGBTQ+ legislation is proposed or enacted, to mitigate the potential negative impact of multiple factors on their mental well-being.

Neural single-shot GHz FMCW correlation imaging

Depth sensing is essential for 3D environmental perception across application domains, including autonomous driving, topographical mapping, and augmented and virtual reality (AR/VR). Traditional correlation time-of-flight (ToF) methods, while are able to produce dense high-resolution depth maps, are plagued by phase wrapping artifacts which limit their effective depth range. Though multi-frequency methods can help reduce this problem by simultaneously solving for phase wrap counts in multiple wavelengths, this requires multiple measurements per pixel, necessitating additional hardware and imaging time. We introduce a 3D imaging method that requires a single per-point measurement by combining frequency-modulated continuous wave (FMCW) operation, all-optical correlation ToF imaging, and a specialized frequency-decoding network. Our system performs all-optical correlation imaging at GHz rates. The method is validated through both simulations and real-world experiments, comparing favorably to existing methods in all experiments.

Monocular Vision Sensor-Based Indoor Road and Stair Detection

Road detection is an essential component of indoor robot navigation. Vision sensors have great advantages in road detection as they can provide rich information in terms of environmental perception. In this paper, a monocular vision sensor-based method for indoor road and stair detection is proposed, which detects feasible areas in indoor environments very fast without paying attention to detailed features of walls or other obstacles. More specifically, for a given indoor road image captured by an on-board vision sensor, the simple linear iterative clustering (SLIC) algorithm-based approach for efficient image segmentation is introduced. Then, according to the DBSCAN algorithm, the generated superpixels are clustered to form large areas of view. The initial road area is obtained through a safe window on the middle bottom of the image. In order to achieve a more accurate road segmentation, the initial image is processed by the binary search, edge detection based on the Canny operator and straight-line detection and location based on the Hough transform, which integrates edge and stair information into road detection. Several experiments are performed to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method could accurately detect road information and staircase information in images and succeeds in addressing the indoor road-detection problem.

Impact of seasonal changes in urban green spaces with diverse vegetation structures on college students' physical and mental health

Based on the perceptions of college student participants in winter and summer, the effects of different vegetation structures within landscapes (single-layer woodland, tree-shrub-grass composite woodlands, tree-grass composite woodland, and single-layer grassland) and concrete squares without plants were investigated, and the skin conductivity level (SCL) and environmental perception recovery score (PRS) associated with landscape types were calculated. The results indicated that seasonal differences in landscape perception significantly affected college student participants' PRS but not their SCL scores, both in winter and summer. Viewing single-layer and tree-shrub-grass composite woodlands in summer, as well as single-layer woodland in winter, enhanced the environmental perception of the college student participants. The restorative effects of the four vegetation types in green spaces were ranked as follows: single-layer woodland, tree-shrub-grass composite woodlands, single-layer grassland, and tree-grass composite woodlands and concrete squares without plants. These findings underscore the importance of considering seasonal variations when choosing plant species for landscaping purposes, with evergreen single-layer woodland being a suitable choice for winter urban landscapes. This provides a scientific basis for assessing landscape perception and preferences in the future.

Virtual Reality Technology in Analysis of the Sarek National Park Soundscape in Sweden

The paper presents an in-depth analysis of the soundscape within Sarek National Park, the oldest national park in Europe, situated in Lapland, northern Sweden. The comprehensive acoustic measurements, ambisonic recordings, and 360X video recordings were carried out during a scientific expedition in the summer of 2020. The aim of the paper is to show the soundscape analysis of carefully selected characteristic locations in various parts of the valley. The paper extensively discusses the findings derived from the recorded data using both classical acoustic methods and the soundscape approach. The classic acoustic parameters, commonly employed in environmental acoustics as well as eco-acoustic indices such as: ACI (acoustic complexity index), ADI (acoustic diversity index), AEI (acoustic evenness index), NDSI (normalized difference soundscape index), BIO (energy level of biophony), amplitude index (M), and total entropy (H) were calculated. To gain further insights, listening tests, facilitated through virtual reality tools, were conducted, enabling participants to engage in soundwalk experiences. By employing a combination of traditional acoustic methods and innovative soundscape approaches, the paper presents a holistic evaluation of the auditory environment in Sarek National Park. The main contribution of the presented research is providing new data from the unique, geographically inaccessible region of the world, the Sarek National Park. This research not only enriches our understanding of the national park’s soundscape but also offers valuable insights into the interaction between the natural environment and human perception of sound.

Guest Editorial Special Issue on Smartness and Robustness of Spatial Environment Perception in Automated Systems

Guest Editorial Special Issue on Smartness and Robustness of Spatial Environment Perception in Automated Systems

Resilience of Spatial Environment Perception Toward Fully Automated Driving: A Review

Resilience of Spatial Environment Perception Toward Fully Automated Driving: A Review

Spatial Environment Perception and Sensing in Automated Systems: A Review

Spatial Environment Perception and Sensing in Automated Systems: A Review

The Effectof Integrated Turkish and Science Activities in Pre-School Education on Children's Environmental Perception

Purpose of the Study: The purpose of this study is to determine the effect of integrated Turkish and science activities in the preschool period on children's environmental perception. Materials and Methods: In the qualitative dimension of the mixed method, a phenomenological design was employed, while in the quantitative dimension, a quasi-experimental design was used. Thestudy conductedwith 20 children.In the experimental procedure, children in both the experimental and control groups were asked to draw an environment picture, and one-on-one interviews were conducted with the children regarding their drawings. The drawings were evaluated using DAET-R and were attempted to be transformed into qualitative form with yellow and blue color tones. Findings: In the final tests, experimental groups’ figures increased in the drawings. It was found that the final test drawings of the children in the experimental group were related to the illustrated books used in the integrated activities. Key Points: As a result of the study, it was determined that the change in environmental perception of the children in the experimental group, where integrated Turkish and science activities developed with illustrated children's books were applied, progressed more prominently compared to the children in the control group.

The Effect of Integrated Turkish and Science Activities in Pre-School Education on Children's Environmental Perception

The Effect of Integrated Turkish and Science Activities in Pre-School Education on Children's Environmental Perception

Environmental and climate perceptions: analysis of venezuelan and haitian refugees in dourados

Environmental and climate perceptions: analysis of venezuelan and haitian refugees in dourados

Wavelet-powered hierarchical frequency filtering framework for autonomous vehicle sensors fault diagnosis and correction under open environments

The reliable operation of autonomous vehicles heavily depends on the functionality of numerous sensors that facilitate environmental perception and vehicle control. However, sensor malfunctions can significantly undermine the dependability and safety of autonomous vehicles. Despite the significant advancements made in sensor fault diagnosis through deep learning, the technology's susceptibility to noise and limited frequency analysis capacity renders it less effective in addressing frequency aliasing and noise interference issues. In addition, repairing incorrect sensor data holds greater significance for autonomous vehicles than merely identifying sensor failures. To this end, this paper proposes a wavelet-powered hierarchical frequency filtering framework (WavePHF) for autonomous vehicle sensors' fault diagnosis and correction. First, this study designs a multi-sensor fault identification and correction joint architecture to detect sensor faults and repair fault signals in time. Second, this study integrates the discrete wavelet transform (DWT) algorithm into the deep model to significantly enhance the model's frequency analysis capabilities and suppress frequency aliasing and noise interference. In particular, DWT is deeply integrated into the convolutional modules' parameter updating and optimization process, thereby facilitating mutual learning and promotion. Third, this study proposes an adaptive frequency weighting block for low-frequency features and an adaptive frequency discarding block for high-frequency features to achieve hierarchical frequency filtering in a coarse-to-fine manner and efficiently identify fault categories and restore pure signals. The WavePHF is evaluated on a real autonomous driving dataset. Experiments show that WavePHF has excellent autonomous vehicle sensors fault diagnosis and correction capabilities and competitive performance in different urban scenarios.

The impacts of environmental education on homeschoolers’ attitudes toward nature

ABSTRACT Previous research indicates that learners appear to connect with nature and have heightened environmental perceptions after participating in environmental education programs (EEPs). Positive changes in attitudes toward nature are important because they can precede environmentally responsible behaviors, such as individuals’ leadership on environmental issues. Much of the existing EEP research has been conducted in school settings. This exploratory study aimed to contribute to the existing literature by examining changes in homeschoolers’ attitudes toward nature after participating in an EEP. Data was collected through a pre- and post-test quantitative instrument and two qualitative mechanisms and analyzed using independent-samples t-tests and paired-samples t-tests, and content analysis, respectively. Results showed greater connection to nature and positive changes in environmental perceptions for the homeschool participant sample. However, statistically significant changes were limited, and quantitative measures showed little change in homeschoolers’ eco-affinity or behavioral intentions. Recommendations for continued research with the homeschooled community are discussed.