Illustrating the multi-stakeholder perceptions of environmental pollution based on big data:Lessons from China

The relationship between stakeholders and the environment influences sustainable development and human wellbeing.To illustrate the multi-stakeholder perceptions of environmental pollution in China,we interpreted a feedback loop in the perception-behavior-environment nexus from the perspective of the coupled human-environment system,measured the differences of environmental perceptions among five stakeholders(the public,government,media,companies,and scientists)and regions(including 31 provinces,autonomous regions,and municipalities in China,with exceptions of Taiwan of China,Hong Kong of China,and Macao of China due to a lack of data)using big data,and made a comparison between the perceptions and the actual pollution situation.The results showed that the five stakeholders exhibited similar perceptions of environmental pollution at the national scale,with air pollution being of most concern,followed by water pollution and soil pollution.There were significant spatial differences in environmental perceptions.All stakeholders in the developed regions in eastern China paid relatively high attention to environmental issues,while those in the northwestern regions paid much less attention.There existed a mutual influence and interaction among the different stakeholders.More attention should be paid to air pollution in Xinjiang Uygur Autonomous Region and Ningxia Hui Autonomous Region,water pollution in Hainan Province,Inner Mongolia Autonomous Region,Heilongjiang Province,and Jilin Province,and soil pollution in Hainan Province,Fujian Province,and Jilin Province.This paper provides a research paradigm on multi-stakeholder environmental perceptions based on big data,and the results provide a background reference for regional environmental governance.

Different people,different wild:Comparing and mapping wilderness representation in Wuyishan National Park,China

It is essential to better integrate wilderness representations of different stakeholders into wilderness conservation.The way in which local residents and other stakeholders frame the construction of wilderness of protected areas in developing countries are poorly understood.In these areas,land use policy and decision may lead to conflicts.This study aims to explore existing public wilderness representations using a questionnaire survey(n=514)administered amongst tourists and other stakeholders in the Wuyishan National Park,in southeast China.The spatial differences in public representations of wilderness across different stakeholder groups were compared against expert knowledge.We found that integrated wilderness representation maps of different stakeholder groups were consistent,namely'area where wild animals live','area with no human influence','a barren and lonely area'.However,three sub-representations of the individual stakeholders varied significantly.Moreover,expert-based wilderness mapping did not reflect public representations accurately,and an integrated wilder-ness quality map considering wilderness representations across both stakeholders and experts can better identify detailed wilderness areas.Our study provides new insights and technical support for future exploration of wilder-ness conservation and mapping in China and other countries with insufficient awareness of wilderness values and investigations in a regional scale.

V2I Based Environment Perception for Autonomous Vehicles at Intersections

In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges.V2I(Vehicle-to-Infrastructure)communication is a potential solution to enable cooperative intelligence of vehicles and roads.In this paper,the RGB-PVRCNN,an environment perception framework,is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication technology.This framework integrates vision feature based on PVRCNN.The normal distributions transform(NDT)point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the intersection.The field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.

Potential effects of the nursing work environment on the work-family conflict in operating room nurses

BACKGROUND The nursing working environment is an important subsystem in the hospital environment.A good working environment could have a positive impact on nurses.However,the work-family conflict and unsatisfactory working environment could significantly reduce their working enthusiasm,efficacy as well as the overall quality of the nursing,increase their fatigue,and thereby compromise their career status.AIM To explore the possible status quo and to analyze the correlation between work environment perception and the work-family conflict among nurses in the operating room.METHODS A total of 312 operating room nurses from two first-class hospitals at Grade 2 and two first-class hospitals at Grade 3 in China from May to September 2017 were included in this research using the cluster sampling method.The data,including the general information questionnaire,the practice environment scale of the nursing work index(PES-NWI),and the work-family conflict scale,were systematically collected.Pearson correlation analysis was applied to analyze the correlation between the two scores,with influencing factors analyzed by hierarchical regression analysis.RESULTS A total of 312 questionnaires were issued,and the response rate and effective questionnaire rate were both 96.15%(300/312).The total scores of the PES-NWI scale and the work-family conflict scale were 3.07±0.43(vs maximum up to 4 points)and 52.32±8.79(vs maximum up to 90 points),respectively.The scores of the PES-NWI scale were negatively correlated with that of work-family conflict scale(all P<0.05).The perception of the nursing work environment and the number of night shifts per month were the major factors contributing to the workfamily conflict(all P<0.05).CONCLUSION The nursing work environment and the work-family conflict among nurses in the operating room were both found at a medium level with a negative correlation between the two.

A deep learning-based binocular perception system

An obstacle perception system for intelligent vehicle is proposed.The proposed system combines the stereo version technique and the deep learning network model,and is applied to obstacle perception tasks in complex environment.In this paper,we provide a complete system design project,which includes the hardware parameters,software framework,algorithm principle,and optimization method.In addition,special experiments are designed to demonstrate that the performance of the proposed system meets the requirements of actual application.The experiment results show that the proposed system is valid to both standard obstacles and non-standard obstacles,and suitable for different weather and lighting conditions in complex environment.It announces that the proposed system is flexible and robust to the intelligent vehicle.

Vehicular Electronic Image Stabilization System Based on a Gasoline Model Car Platform

Noise,vibration and harshness(NVH)problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles.Although high accuracy manufacturing,modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts;off-road condition,obstacles and extreme operating condition could still trigger NVH problems unexpectedly.This paper proposes a vehicular electronic image stabilization(EIS)system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles.Firstly,feature point detection and matching based on an oriented FAST and rotated BRIEF(ORB)algorithm are implemented to match images in the process of EIS.Furthermore,a novel improved random sampling consensus algorithm(i-RANSAC)is proposed to eliminate mismatched feature points and increase the matching accuracy significantly.And an adaptive Kalman filter(AKF)is applied to improve the adaptability of the vehicular EIS.Finally,an experimental platform based on a gasoline model car was established to validate its performance.The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles.

Multi-layer perception approach to identification of compound information

Presents a novel approach of multi layer sensing for perception of high level environmental information related to many conventional physical quantities, such as temperature, humidity and brightness, which focuses on the processing of multi functional variables in a multi layer framework, and consists of multi functional sensing and multi layer fusion. Concerning the first aspect, a CdS and Fe 3O 4 materials based multi function sensor has been developed to measure the three quantities, and provides a possible solution to the sensor multi functional measurement equations, especially when the sensor processes more than three quantities, and proposes ways to evaluate the concerned environment as degree of comfort, Quantity Creditability Tactics (QCT) of multi layer data fusion.

Research Frontier of Street Spatial Form

In order to comprehensively and objectively understand the research status of street form at home and abroad,WOS and CNKI database are as the source,and 176 street form papers retrieved from 2010 to 2021 are as samples to conduct statistical analysis of the data.The main research hotspots are reviewed systematically,and the research trends in the field of planning and architecture are pointed out.The hot topics of street form research in the past ten years are summarized,such as street spatial form characteristics,street form and thermal environment,air quality correlation,street form and walking,safety,vitality,health and other perceived correlations.The research methods are summarized from two levels of street form data collection and analysis.Finally,the current research trend characteristics and future trends are summarized and prospected to provide reference for the current research of street form.

Towards Autonomous Vehicles with Advanced Sensor Solutions

Professional truck drivers are an essential part of transportation in keeping the global economy alive and commercial products moving. In order to increase productivity and improve safety, an increasing amount of automation is implemented in modern trucks. Transition to automated heavy good vehicles is intended to make trucks accident-free and, on the other hand, more comfortable to drive. This motivates the automotive industry to bring more embedded ICT into their vehicles in the future. An avenue towards autonomous vehicles requires robust environmental perception and driver monitoring technologies to be introduced. This is the main motivation behind the DESERVE project. This is the study of sensor technology trials in order to minimize blind spots around the truck and, on the other hand, keep the driver’s vigilance at a sufficiently high level. The outcomes are two innovative truck demonstrations: one R & D study for bringing equipment to production in the future and one implementation to the driver training vehicle. The earlier experiments include both driver monitoring technology which works at a 60% - 80% accuracy level and environment perception (stereo and thermal cameras) whose performance rates are 70% - 100%. The results are not sufficient for autonomous vehicles, but are a step forward, since they are in-line even if moved from the lab to real automotive implementations.

Optical water quality and human perceptions of rivers:an ethnohydrology study

Rivers are revered worldwide for their ecologic,scenic,and recreational value.The capacity to communicate effectively among human groups with vested interest in rivers hinges on understanding the nature of human perceptions of water quality and the extent to which they vary intraculturally.Recognizing the intersection between measured water quality and the characteristics of rivers that influence human perceptions facilitates potential for better communication across disciplines and among stakeholders.We conducted interviews and a pile-sort task with water quality experts and nonexperts.Our analysis suggested human evaluation of water quality is guided by culturally constructed criteria,regardless of respondent expertise,experience,or demographics.Cluster analysis results implied that measured physical and chemical parameters of rivers were directly related to the visible attributes used in human judgments.We suggest that,regardless of variability among individual stakeholders,observable characteristics may be the foundation for a common understanding of water quality in rivers.

Public perception of environmental issues across socioeconomic characteristics:A survey study in Wujin,China

In developing countries,there is controversy over the correct perception regarding environmental and developmental issues.Few studies have examined the perception of low-income nationals in regards to social and environmental issues.This paper looks at the relationship between socio-demographic factors and the groups’perceived priority regarding environmental and social issues in Wujin County.The results indicated that most residents,specifically the young,government employed and the urban community consider environmental issues to be serious,especially in relation to air pollution and water pollution.Furthermore,many residents feel it is important to rank environmental problems that are related to other social and economic issues,and that environmental protection must be set as a priority in Wujin County.Compared to social issues,environmental concern was greater among the young,government employed,and the urban community,because of their higher education and affluence.In addition,66.2%of residents consider environmental protection to be more important than economic development.Thus,environmental protection must be set as a high priority in Wujin County,in order to face the many social and environmental challenges inherent in development.

Effects of social capital,risk perception and awareness on environmental protection behavior

Introduction:Environmental protection is an essential issue for sustainable development,and its execution power mainly comes from individual environmental protection behavior.This study investigates the resident environmental protection behaviors and influencing factors in the ecologically fragile areas of western China based on a total of 1062 households by adopting the participatory assessment method.Then,this study used the structural equation model to empirically analyze the impact of social capital,environmental risk perception,and environmental awareness on residents’environmental protection behavior.Outcomes:The results showed that environmental awareness is the basis for practicing environmental protection behavior,consistent with the existing research;social capital has a significant impact on residents’environmental protection behavior,with an impact coefficient of 0.347.Hence,environmental risk perception has an indirect influence on environmental protection behavior through environmental awareness.Besides,compared with urban residents,rural residents’environmental awareness and social capital have a stronger role in environmental protection behavior.Conclusions:The study clarified the influence path of residents’environmental protection behavior,thus effectively providing a decision-making basis for the government to guide residents in the environmental governance system.

3D Environmental Perception Modeling in the Simulated Autonomous-Driving Systems

Self-driving vehicles require a number of tests to prevent fatal accidents and ensure their appropriate operation in the physical world.However,conducting vehicle tests on the road is difficult because such tests are expensive and labor intensive.In this study,we used an autonomous-driving simulator,and investigated the three-dimensional environmental perception problem of the simulated system.Using the open-source CARLA simulator,we generated a CarlaSim from unreal traffic scenarios,comprising 15000 camera-LiDAR(Light Detection and Ranging)samples with annotations and calibration files.Then,we developed Multi-Sensor Fusion Perception(MSFP)model for consuming two-modal data and detecting objects in the scenes.Furthermore,we conducted experiments on the KITTI and CarlaSim datasets;the results demonstrated the effectiveness of our proposed methods in terms of perception accuracy,inference efficiency,and generalization performance.The results of this study will faciliate the future development of autonomous-driving simulated tests.

The mechanism of environmental D-xylose perception by the bacterial membrane two-component complex XylFII-LytS

D-xylose,the main building block of plant biomass,is a pentose sugar that can be used by bacteria as a carbon source for bio-based fuel and chemical production through fermentation.In bacteria,the first step for D-xylose metabolism is signal perception at the membrane.Scientists previously identified a threecomponent system in Firmicutes bacteria comprising a membrane-associated sensor protein(XylFII)。

Direct and cross-generational effects of reproduction on fitness and behavioral variability in male-biased environments

Population structure determines individuals’interactions and trade-offs with evolutionary consequences.Male-biased populations increase intrasexual competition and intersexual harassment,reducing female resource acquisition,and thus,resources availability for the following generation.We analyzed direct and cross-generational effects of male harassment in two generations of damselflies(Odonata).We exposed adult females to treatments with different sex-ratio and density(balanced and male-biased)to modify the male harassment level.We analyzed female fecundity,fertility,and number of faecal deposits as an indirect measure of resources acquisition.We studied female flight performance after repeated exposures to males.We analyzed survivorship,development,exploration,thigmotaxis,and feeding latency of larvae produced by the experimental females.In both generations,we analyzed four metrics of behavior:mean value,interindividual differences in plasticity,intra-individual unpredictability,and repeatability.Mating duration increased in male-biased treatment,whereas female resources acquisition and fertility decreased.Females that mated longer showed higher fecundity when they were exposed to balanced treatment,but not if they were exposed to male-biased treatment.Females from the male-biased treatment showed interindividual differences in plasticity and no repeatability in flight performance.Offspring showed balanced sex-ratio and similar survivorship,development,and feeding latency independently of the parental treatment;however,females exposed to male-biased treatment produced offspring with higher differences in exploration plasticity and daughters less explorative and with higher unpredictable thigmotaxis.We propose prolonged copulation as courtship at balanced sex-ratio but a cost to females under male-biased sex-ratio.Cross-generational effects in behavioral variability may be a mechanism to cope with predicted future environments.

3D semantic map construction based on point cloud and image fusion

Accurate and robust positioning and mapping are the core functions of autonomous mobile robots,and the ability to analyse and understand scenes is also an important criterion for the intelligence of autonomous mobile robots.In the outdoor environment,most robots rely on GPS positioning.When the signal is weak,the positioning error will interfere with the mapping results,making the semantic map construction less robust.This research mainly designs a semantic map construction system that does not rely on GPS signals for large outdoor scenes.It mainly designs a feature extraction scheme based on the sampling characteristics of Livox-AVIA solid-state LiDAR.The factor graph optimisation model of frame pose and inertial measurement unit(IMU)pre-integrated pose,using a sliding window to fuse solid-state LiDAR and IMU data,fuse laser inertial odometry and camera target detection results,refer to the closest point distance and curvature for semantic information.The point cloud is used for semantic segmentation to realise the construction of a 3D semantic map in outdoor scenes.The experiment verifies that laser inertial navigation odometry based on factor map optimisation has better positioning accuracy and lower overall cumulative error at turning,and the 3D semantic map obtained on this basis performs well.

A Review of Testing Object‑Based Environment Perception for Safe Automated Driving

Safety assurance of automated driving systems must consider uncertain environment perception.This paper reviews litera-ture addressing how perception testing is realized as part of safety assurance.The paper focuses on testing for verification and validation purposes at the interface between perception and planning,and structures the analysis along the three axes(1)test criteria and metrics,(2)test scenarios,and(3)reference data.Furthermore,the analyzed literature includes related safety standards,safety-independent perception algorithm benchmarking,and sensor modeling.It is found that the realiza-tion of safety-oriented perception testing remains an open issue since challenges concerning the three testing axes and their interdependencies currently do not appear to be sufficiently solved.

Driving Space for Autonomous Vehicles

Driving space for autonomous vehicles(AVs)is a simplified representation of real driving environments that helps facilitate driving decision processes.Existing literatures present numerous methods for constructing driving spaces,which is a fundamental step in AV development.This study reviews the existing researches to gain a more systematic understanding of driving space and focuses on two questions:how to reconstruct the driving environment,and how to make driving decisions within the constructed driving space.Furthermore,the advantages and disadvantages of different types of driving space are analyzed.The study provides further understanding of the relationship between perception and decision-making and gives insight into direction of future research on driving space of AVs.

Camera-Radar Fusion Sensing System Based on Multi-Layer Perceptron

Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems,such as the choice of sensors and fusion methods.To solve these issues,we proposed a machine learning-based fusion sensing system that uses a camera and radar,and that can be used in intelligent vehicles.First,the object detection algorithm is used to detect the image obtained by the camera;in sequence,the radar data is preprocessed,coordinate transformation is performed,and a multi-layer perceptron model for correlating the camera detection results with the radar data is proposed.The proposed fusion sensing system was verified by comparative experiments in a real-world environment.The experimental results show that the system can effectively integrate camera and radar data results,and obtain accurate and comprehensive object information in front of intelligent vehicles.

Speech endpoint detection in low-SNRs environment based on perception spectrogram structure boundary parameter

The Perception Spectrogram Structure Boundary（PSSB）parameter is proposed for speech endpoint detection as a preprocess of speech or speaker recognition.At first a hearing perception speech enhancement is carried out.Then the two-dimensional enhancement is performed upon the sound spectrogram according to the difference between the determinacy distribution characteristic of speech and the random distribution characteristic of noise.Finally a decision for endpoint was made by the PSSB parameter.Experimental results show that,in a low SNR environment from-10 dB to 10 dB,the algorithm proposed in this paper may achieve higher accuracy than the extant endpoint detection algorithms.The detection accuracy of 75.2%can be reached even in the extremely low SNR at-10 dB.Therefore it is suitable for speech endpoint detection in low-SNRs environment.