Perception Of Environment Research Articles

Effects of environmental art perception on students’ creativity and emotional development

Effects of environmental art perception on students’ creativity and emotional development

Image quality safety model for the safety of the intended functionality in highly automated agricultural machines

Achieving safe and reliable environmental perception is crucial for the success of highly automated or even autonomous agricultural machinery. However, developing such a system is challenging due to the inherent limitations of perception sensors. In certain conditions, these sensors may fail to capture accurate data, leading to erroneous perceptions of the environment and potentially compromising safety. Monitoring the functional insufficiencies of the measurement data is crucial for ensuring the safety and reliability of perception systems.This article introduces ISO standards, which provide guidelines for ensuring functional safety in highly automated mobile machines and vehicles. It also proposes an Image Quality Safety Model (IQSM) for monitoring the safety of the intended functionality in perception systems. The IQSM estimates the confidence level with which a camera can safely perform a specific object detection task. If the confidence level falls below a predefined threshold, the IQSM can trigger actions, alert operators, and prevent potential safety hazards. The IQSM exhibits remarkable performance, achieving a validation accuracy of about 90%, demonstrating its ability to effectively distinguish the safety of the intended functionality under a variety of image quality conditions.

Assessing restorative effects of soundscapes in VR through EEG and HRV

This study aimed to extend the application of soundscape analysis by utilizing electroencephalogram (EEG) analysis as a physiological evaluation tool to determine the actual restorative impact on individual environmental perceptions and psycho-physiological responses stemming from various soundscape experiences. Initially, we constructed three distinct virtual reality (VR) environments: waterfront, urban, and green areas, each accompanied by three content variations. A total of 60 subjects participated in the study. Data were gathered through a survey assessing individual characteristics, psychological restorative responses, and soundscape perceptions. Additionally, quantitative physiological responses such as heart rate variability (HRV) and EEG, were measured. Subsequently, subjects were categorized into restoration and non-restoration groups based on HRV responses using cluster analysis. The analysis revealed positive HRV changes indicative of reduced stress levels. In EEG analysis, differences were observed in network connectivity rather than power spectral density. As a result of connectivity analysis, global efficiency increased overall in the restoration group, and differences in nodal efficiency occurred in a total of eight brain regions, enabling soundscape experience to efficiently process cognitive functions in the cerebrum, thereby having a positive effect on neural communication. This study is significant as it represents the first examination of soundscape restorative responses in terms of brain wave connectivity, underscoring the feasibility of employing physiological evaluations, including brain wave analysis, in the study of soundscapes.

Children’s Exposure Factors and Risk Perception of Sanitation Challenges Along the U.S.–Mexico Border

ABSTRACT In the rural U.S.–Mexico border towns, transboundary sanitary sewage overflows (SSOs) are of concern. The high concentrations of pathogens present in SSOs poses a threat to the shared ecosystem and communities’ health and well-being. Concerns related to an SSO effluent situated adjacent to a school in Naco, Arizona led to an academic-government-school partnership to assess children’s exposure factors, environment and health related risk perceptions, and risk communication preferences. A survey administered to school staff (n = 9) and parents (n = 31) observed a lower hand/object-to-mouth behavior for children ages 4–6 compared to values in the literature, and the need to further assess exposure factors for children over six. While there was a general negative risk perception to SSOs, approximately half of respondents did not have/were not sure of any SSO-related events. Using the Bioregion/One Health and cross-border governance frameworks, this study highlights the governing barriers that exist during SSO events and underscores the need for community participation, effective intervention, and risk communication strategies.

Review on Sensors and Path Planning Algorithms of Automatic Driving

automatic driving developed rapidly in recent years. Environment perception and path planning are two key functions in automatic driving, and sensors and algorithms are the basis for realizing these two functions. Through literature review and comparative analysis, this paper discusses sensors and path planning algorithms in automatic driving. An analysis is conducted on the principles, benefits, limitations, and applications of various sensor types in automatic driving. The RRT algorithm and A* algorithm are also discussed, as well as their advantages and constraints. Finally, this paper proposes suggestions such as deep learning, sensor fusion and cost reduction to update automatic driving. This paper provides a reference for the further development of automatic driving.

A New Path to Language Education Innovation Based on Networked Smart Language Laboratories

This paper discusses the importance of networked intelligent language laboratory as a new path for language education innovation. It elaborates on its background, technical support (Internet of Things, big data, cloud computing, artificial intelligence technology), advantages (resource sharing and utilization efficiency improvement, personalized learning support, enhanced teaching interaction and feedback, environmental perception and intelligent management), innovative applications (diversified teaching models, intelligent learning assessment, integration of language practice and cultural experience), analyzes the challenges in construction (technical security and privacy protection, teacher role transformation and capacity improvement, education resource integration and quality assurance), and summarizes the research results and looks forward to future development trends, including continuous technological innovation, expanding and deepening applications, and strengthening globalization and cross-cultural communication and cooperation.

Multi-Sensor Information Fusion Technology in Robots

Multi-sensor information fusion is a technology that collects and processes data from multiple sensors to obtain more comprehensive and accurate information. In the field of robotics, multi-sensor information fusion technology is frequently applied to robots since the use of a single sensor in robots is often limited, this will also improve the accuracy and robustness of robot environment perception. After researching and organizing multi-sensor information fusion technology, robotics technology and other related materials, this paper provides a comprehensive introduction to the application of multi-sensor information fusion technology in the field of robotics, describes the logical structure of multi-sensor information fusion technology in robotics, explains the algorithms of multi-sensor information fusion technology that are mainly applied to robots. Finally, this paper summarizes and exemplifies the main applications of multi-sensor information fusion technology in the field of robotics.

A Review of Gait Coordination Control Methods for Lower Limb Exoskeleton Robots Based on Visual Models

The ongoing evolution of exoskeleton technology has rendered the exclusive reliance on a single vision technology an impractical approach to addressing the complexities of diverse environments and individual requirements. This paper therefore explores the potential of combining traditional visual models with movement intention prediction based on surface electromyography (sEMG) and virtual reality (VR) technology in the context of gait control. The study concluded that traditional visual models are capable of providing accurate environmental perception and gait planning; however, they are deficient in forecasting user intentions. Surface electromyography (sEMG) technology can supplement this deficiency with muscle activity signals, enabling the system to respond dynamically to user needs. Concurrently, virtual reality (VR) technology furnishes users with a more personalized training program through immersive environmental simulation and feedback. The combination of these three technologies not only enhances the system's real-time responsiveness but also exhibits commendable adaptability in complex terrain and rehabilitation training. This paper examines the three fundamental technologies of lower limb exoskeleton robots in gait control and their fusion applications, with the objective of enhancing the system's intelligence and adaptability, providing a direction for the future development of lower limb exoskeleton technology, and highlighting the challenges and potential of multimodal data fusion and computational optimisation.

Research Status and Prospect of the Key Technologies for Environment Perception of Intelligent Excavators

With the urgent need of the industry and the continuous development of artificial intelligence, research into intelligent excavators has achieved certain progress. However, intelligent excavators often face strong vibrations, dense dust, and complex objectives. These have brought severe challenges to environmental perception, and are important research difficulties that must be overcome in realizing the practical engineering applications of intelligent excavators. Many researchers have studied these problems in reducing vibration and dust noise for light detection and ranging (LiDAR) scanners, multi-sensor information fusion, and the segmentation and recognition of 3D scenes. This paper reviews the research status of these key technologies and discusses their development trends.

EFL Students’ Perception Towards Kahoot! as A Gamification Tool in English Learning

Technology in education forces teachers to modify their teaching strategies by implementing gamification as a tool in teaching and learning process. Kahoot is a popular gamification tool in English learning. Using a qualitative research design, this study aimed to determine the EFL students' perception of the gamification tool in an English learning environment. Thirty-six (36) students of 10th grade from a public senior high school in Semarang, Central Java, Indonesia. The participants were asked to complete a closed-ended questionnaire using Google Forms to gain information about their perceptions of the educational environment. The findings revealed that the students generally have a positive perception of Kahoot! as a gamification tool in the English learning process. The interactive and competitive nature of the quizzes increases students' motivation and engagement in the learning process, which can enhance students' engagement and motivation in the classroom. The students generally perceive Kahoot! as an effective, fun, and effective educational environment that enhances students' learning experience and creates a vibrant and engaging classroom environment. By combining motivation, collaboration, competition, fun, interest, excitement, and an energetic environment, Kahoot! can enhance the English learning experience, promote interaction and collaboration, and offer customizable features.

Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental Perception

Underwater simultaneous localization and mapping (SLAM) has significant challenges due to the complexities of underwater environments, marked by limited visibility, variable conditions, and restricted global positioning system (GPS) availability. This study provides a comprehensive analysis of sensor fusion techniques in underwater SLAM, highlighting the amalgamation of proprioceptive and exteroceptive sensors to improve UUV navigational accuracy and system resilience. Essential sensor applications, including inertial measurement units (IMUs), Doppler velocity logs (DVLs), cameras, sonar, and LiDAR (light detection and ranging), are examined for their contributions to navigation and perception. Fusion methodologies, such as Kalman filters, particle filters, and graph-based SLAM, are evaluated for their benefits, limitations, and computational demands. Additionally, innovative technologies like quantum sensors and AI-driven filtering techniques are examined for their potential to enhance SLAM precision and adaptability. Case studies demonstrate practical applications, analyzing the compromises between accuracy, computational requirements, and adaptability to environmental changes. This paper proceeds to emphasize future directions, stressing the need for advanced filtering and machine learning to address sensor drift, noise, and environmental unpredictability, hence improving autonomous underwater navigation through reliable sensor fusion.

Psychometric Properties of Turkish Adaptation of the Environmental Risk Coping Scale

Objective: This study aims to adapt Environmental Risk Coping Scale into Turkish and examine its psychometric properties. Method: For this aim, the psychometric properties of the scale adapted into Turkish were tested in a sample of 230 participants living in 6 cities with high earthquake risk in Türkiye. Participants responded to the Environmental Risk Coping Scale, Environmental Risk Perception Scale, questions on Present Fatalistic and Future Time Orientation, and a demographic information form (i.e., age, gender, education level, city of residence, and homeowner/renter status, past earthquake experience, extent of earthquake damage). Results: The findings of the analyses (i.e., confirmatory factor analysis, reliability analysis) showed that this 12-item scale is reliable and valid in the Turkish sample. Specifically, desirable fit indices (χ2 / sd = 2.06, p < .001, CFI = .96, TLI = .95, RMSEA = .07, 90 % CI [.05, .09], SRMR = .04) demonstrated that the confirmatory factor analysis confirmed the two-factor structure (problem focused and emotion focused coping strategies) and Cronbach α values (.89 for 9-item problem focused coping strategies factor and .72 for 3-item emotion focused coping strategies factor) indicate that the internal consistency of the scale is high. In addition, the sub-dimensions of the Turkish version of the scale were correlated with variables such as risk perception, present-fatalistic time orientation, future time orientation, and demographic factors such as age, earthquake experience, and the extent of damage in earthquake(s) in line with the literature. Conclusion: The Turkish adaptation of the Coping with Environmental Risk Scale was found to be a reliable and valid scale in the Turkish sample.

Bio-Inspired P-type TeSeOx Synaptic Transistor Based on Multispectral Sensing for Neuromorphic Visual Multilevel Nociceptor.

The development of neuromorphic color vision has significant research implications in the fields of machine vision and artificial intelligence. By mimicking the processing mechanisms of energy-efficient biological visual systems, it offers a unique potential for real-time color environment perception and dynamic adaptability. This paper reports on a multispectral color sensing synaptic device based on a novel p-type TeSeOx transistor, applied to a neuromorphic visual multilevel nociceptor. Due to the intrinsic properties of TeSeOx, its narrow bandgap allows for multi-wavelength (405, 532, 655 nm) response, and its oxide semiconductor-based persistent photoconductivity converts optical signals into stored electrical signals, successfully emulating key synaptic characteristics such as excitatory postsynaptic current (EPSC), multi-pulse facilitation, and the transition from short-term to long-term memory. Additionally, it simulates learning, forgetting, and relearning behaviors, as well as image memory under tricolor light. Finally, using optical signals as a pain stimulus, the fundamental functions of a nociceptor are realized, including "threshold," "non-adaptation," "relaxation," and "nociceptive sensitization". More importantly, by using tricolor light, multilevel pain perception is acheived. These results have the potential to advance fields such as autonomous driving, machine vision, and intelligent alert systems.

Percepción sobre la salinización del suelo en una zona vitícola de Argentina

Salt-affected soils occupy 1,000 million hectares and concentrate in arid and semi-arid regions, affecting between 20 and 30% of irrigated lands. Primary salinization is derived from natural conditions, while secondary salinization is the product of anthropic factors. Salinized agricultural plots can be abandoned and excluded from the productive system. In this context, productive restoration is an opportunity to recover these sites. Our objective is to inquire about the environmental perception of the social actors of salinization. We aim at transversally incorporating the social component in productive restoration planning. Our study case was the East Zone of the North Oasis of Mendoza, where almost half of the irrigated area is cultivated by vines, almost a quarter is abandoned land, and the rest includes olive trees, fruit trees, and vegetables. We conducted semi-structured interviews to identify the main lines of perception, which were analyzed to recognize strengths, weaknesses, opportunities, and threats for the design of productive restoration projects at a local scale. Strategic, efficient, and sustainable planning must include the social dimension.

A Novel Multi-Sensor Nonlinear Tightly-Coupled Framework for Composite Robot Localization and Mapping.

Composite robots often encounter difficulties due to changes in illumination, external disturbances, reflective surface effects, and cumulative errors. These challenges significantly hinder their capabilities in environmental perception and the accuracy and reliability of pose estimation. We propose a nonlinear optimization approach to overcome these issues to develop an integrated localization and navigation framework, IIVL-LM (IMU, Infrared, Vision, and LiDAR Fusion for Localization and Mapping). This framework achieves tightly coupled integration at the data level using inputs from an IMU (Inertial Measurement Unit), an infrared camera, an RGB (Red, Green and Blue) camera, and LiDAR. We propose a real-time luminance calculation model and verify its conversion accuracy. Additionally, we designed a fast approximation method for the nonlinear weighted fusion of features from infrared and RGB frames based on luminance values. Finally, we optimize the VIO (Visual-Inertial Odometry) module in the R3LIVE++ (Robust, Real-time, Radiance Reconstruction with LiDAR-Inertial-Visual state Estimation) framework based on the infrared camera's capability to acquire depth information. In a controlled study, using a simulated indoor rescue scenario dataset, the IIVL-LM system demonstrated significant performance enhancements in challenging luminance conditions, particularly in low-light environments. Specifically, the average RMSE ATE (Root Mean Square Error of absolute trajectory Error) improved by 23% to 39%, with reductions from 0.006 to 0.013. At the same time, we conducted comparative experiments using the publicly available TUM-VI (Technical University of Munich Visual-Inertial Dataset) without the infrared image input. It was found that no leading results were achieved, which verifies the importance of infrared image fusion. By maintaining the active engagement of at least three sensors at all times, the IIVL-LM system significantly boosts its robustness in both unknown and expansive environments while ensuring high precision. This enhancement is particularly critical for applications in complex environments, such as indoor rescue operations.

Influence of the Nursing Work Environment on Job Satisfaction in Male Nurses: The Mediating Effect of Social Support.

Although the proportion of male nurses in the nursing profession has risen in recent years, their turnover rate remains higher than their female counterparts. Male nurses tend to report lower levels of job satisfaction. However, there has been a dearth of research examining the relation among perceptions of work environment, social support, and job satisfaction in male nurses. The aim of this study was to investigate the associations among work environment, social support, and job satisfaction in male nurses in Korea. In addition, the potential mediating effect of social support on the relationship between work environment and job satisfaction was explored. A cross-sectional design was employed to investigate the research questions. The study was conducted in a metropolitan area in South Korea, and self-administered questionnaires were distributed to participants. Hierarchical multiple regression analysis was utilized to explore the potential associations among work environment, social support, and job satisfaction. Work environment was found to correlate positively with job satisfaction (r = .53, p < .001) and social support (r = .57, p < .001) in the participants, whereas a positive correlation was also found between social support and job satisfaction (r = .57, p < .001). Social support was shown to partly mediate the relationship between work environment and job satisfaction. The findings of this study suggest that social support partially mediates the relationship between work environment and job satisfaction in male nurses as well as confirm the importance of both work environment and social support as factors that can positively influence job satisfaction in this group.

Examining the Impact of Online Help-Seeking Strategies on Promoting EFL learners’ Self-Regulated and Collaborative Learning Skills, Language Learning Outcomes, and Perceptions in CALL Environments

This research examines the impact of online help-seeking strategies—both adaptive and peer-based—on promoting self-regulated and collaborative learning skills, as well as language learning outcomes, among Chinese EFL learners in computer-assisted language learning (CALL) environments. Employing a mixed-methods approach, the study triangulates various data strands from 92 randomly selected EFL learners to explore how these strategies influence their learning outcomes. Through methodological triangulation and the execution of one-way ANOVA, the research demonstrates the superior effectiveness of combining adaptive and peer help-seeking strategies, compared to their isolated use, in enhancing self-regulated and collaborative learning skills and language learning outcomes. The findings reveal a significant positive correlation between learners’ interactive engagement with help-seeking strategies and their overall CALL outcomes, indicating that such strategies support the development of both collaborative and self-regulated learning skills. This study provides theoretical and practical insights into CALL, offering evidence-based guidance for researchers and practitioners on the nuanced impact of integrating dynamic and flexible help-seeking strategies to enhance EFL learners’ skills and outcomes.

Prediction and Elimination of Physiological Tremor During Control of Teleoperated Robot Based on Deep Learning.

Currently, teleoperated robots, with the operator's input, can fully perceive unknown factors in a complex environment and have strong environmental interaction and perception abilities. However, physiological tremors in the human hand can seriously affect the accuracy of processes that require high-precision control. Therefore, this paper proposes an EEMD-IWOA-LSTM model, which can decompose the physiological tremor of the hand into several intrinsic modal components (IMF) by using the EEMD decomposition strategy and convert the complex nonlinear and non-stationary physiological tremor curve of the human hand into multiple simple sequences. An LSTM neural network is used to build a prediction model for each (IMF) component, and an IWOA is proposed to optimize the model, thereby improving the prediction accuracy of the physiological tremor and eliminating it. At the same time, the prediction results of this model are compared with those of different models, and the results of EEMD-IWOA-LSTM presented in this study show obvious superior performance. In the two examples, the MSE of the prediction model proposed are 0.1148 and 0.00623, respectively. The defibrillation model proposed in this study can effectively eliminate the physiological tremor of the human hand during teleoperation and improve the control accuracy of the robot during teleoperation.

Exploring the Relationship Between Identity, Physical Activity, and Accessibility Perceptions.

Physical activity (PA) has a well-documented, positive effect on overall health and wellbeing. Additionally, addressing activity-friendly infrastructure in communities can promote PA in everyday life. A universal design (UD) lens on PA promotes accessibility to the broadest audience and contributes to equal benefit of increased PA. This study explores the intersectionality of age, gender, and disability in United States-based gender minority adults using multiple regression analysis. A statistically significant relationship emerged between participants' (n = 125) disability status and their corresponding perceptions of accessible design in their communities. However, gender, age, and PA did not have statistical significance in the regression model. The results reported in this paper are specific to the unique challenges these populations face in accessing inclusive and accessible community environments. Few health disparities studies have combined multiple elements of one's identity with measurements of PA to examine environmental perceptions among multiply marginalized communities. This research seeks to address this research gap.

Research on multi-scale fusion image enhancement and improved YOLOv5s lightweight ROV underwater target detection method

Underwater target detection technology is very important for Marine resources detection and underwater environment perception. The problems of low-quality underwater images, a large amount of calculation, and low accuracy of target detection models still need to be solved. Firstly, a new underwater image enhancement algorithm based on multi-scale fusion is proposed. The algorithm combines the improved white balance algorithm and the improved CLAHE (Contrast Limited Adaptive Histogram Equalization) and realizes the multi-scale fusion strategy by introducing different weight maps, aiming to improve the visual effect and quality of underwater images. Secondly, to further improve the accuracy and efficiency of underwater target detection, an improved YOLOv5s lightweight underwater target detection algorithm is proposed. The algorithm combines the Ghost convolution module, EMA(Efficient Multi-Scale Attention) mechanism, and CARAFE (Content-Aware ReAssembly of FEatures) up-sampling mode. The comparison on the URPC(Underwater Robot Perception Challenge) public dataset shows that the proposed algorithm has achieved significant improvement in terms of model size, parameter amount, calculation amount, and mAP@0.5. Thirdly, to verify the effectiveness of the image enhancement algorithm and underwater target detection algorithm proposed in this paper, the ROV(Remote Operated Vehicle) is used to conduct experiments in the experimental pool. By designing underwater target detection experiments before and after image enhancement, the results show that the enhanced image significantly improves the detection ability of underwater targets. Finally, underwater target detection software is developed to provide strong support for the application of underwater-related fields.