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This paper aims to propose a robust and efficient method for vanishing point detection in unstructured road scenes.

The proposed method includes two main stages: drivable region estimation and vanishing point detection. In drivable region estimation stage, the road image is segmented into a set of patches; then the drivable region is estimated by the patch-wise manifold ranking. In vanishing point detection stage, the LSD method is used to extract the straight lines; then a series of principles are proposed to remove the noise lines. Finally, the vanishing point is detected by a novel voting strategy.

The proposed method is validated on various unstructured road images collected from the real world. It is more robust and more efficient than the state-of-the-art method and the other three recent methods. Experimental results demonstrate that the detected vanishing point is practical for vision-sensor-based navigation in complex unstructured road scenes.

This paper proposes a patch-wise manifold ranking method to estimate the drivable region that contains most of the informative clues for vanishing point detection. Based on the removal of the noise lines through a series of principles, a novel voting strategy is proposed to detect the vanishing point.

This study aims to address the issue of high-precision measurement of AC electric field. An electro-optical sensor with high sensitivity is proposed for this purpose.

The proposed sensor combines electromagnetic induction and fiber Bragg grating (FBG) sensing techniques. It is composed of a sensing probe, a piece or stack of piezoelectric ceramics (PZT) and an FBG. A signal processing circuit is designed to rectify and amplify the induced voltage. The processed signal is applied to the PZT and the deformation of PZT is detected by FBG. Theoretical calculation and simulation are conducted to verify the working principle of the probe. The sensor prototype is fabricated and its performance is tested.

The results of this study show that the sensor has good linearity and repeatability. The sensor sensitivity is 0.061 pm/Vm⁻¹ in the range from 250 to 17,500 V/m, enabling a measurement resolution of electric field strength of 16.3 V/m. The PZT stack is used to enhance the sensor sensitivity and the resolution can be improved up to 3.15 V/m.

A flexure hinge lever mechanism is used to amplify the deformation of PZT for further enhancement of sensitivity. The results show that the proposed sensor has high sensitivity and can be used for the accurate measurement of an electric field. The proposed sensor could have potential use for electric field measurement in the power industry.

This study aims to provide a comprehensive overview of pro-environmental behavior (PEB) research within higher education institutions (HEIs), highlighting current trends and future challenges.

Using 198 journal articles from the Web of Science, the study conducts co-citation, bibliographic coupling and co-word analyses to map influential publications and forecast trends.

The co-citation analysis revealed three distinct clusters: value-driven environmental behavior, intention-based environmental behavior and green organizational practices and employee PEB. The bibliographic coupling and the co-word analysis revealed more nuanced clusters, holistically identifying academic activities towards PEB. The authors conclude that more strategic and PEB-oriented HEI’s actions are crucial due to the social responsibility of the universities for sustainable development.

This paper provides valuable insights into the expanding area of PEB research and climate leadership empowerment within HEIs. The practical implications of this research are significant for HEIs. It guides the creation of effective policies and interventions to foster sustainable behavior and reduce environmental harm. The study shows the development of educational programs and campaigns promoting sustainable practices among individuals and communities, emphasizing the role of HEIs in cultivating a sustainability-conscious generation.

Selective laser sintering (SLS) is an essential technology in the field of additive manufacturing. However, SLS technology is limited by the traditional point-laser sintering method and has reached the bottleneck of efficiency improvement. This study aims to develop an image-shaped laser sintering (ISLS) system based on a digital micromirror device (DMD) to address this problem. The ISLS system uses an image-shaped laser light source with a size of 16 mm × 25.6 mm instead of the traditional SLS point-laser light source.

The ISLS system achieves large-area image-shaped sintering of polymer powder materials by moving the laser light source continuously in the x-direction and updating the sintering pattern synchronously, as well as by overlapping the splicing of adjacent sintering areas in the y-direction. A low-cost composite powder suitable for the ISLS system was prepared using polyether sulfone (PES), pinewood and carbon black (CB) powders as raw materials. Large-sized samples were fabricated using composite powder, and the microstructure, dimensional accuracy, geometric deviation, density, mechanical properties and feasible feature sizes were evaluated.

The experimental results demonstrate that the ISLS system is feasible and can print large-sized parts with good dimensional accuracy, acceptable geometric deviations, specific small-scale features and certain density and mechanical properties.

This study has achieved the transition from traditional point sintering mode to image-shaped surface sintering mode. It has provided a new approach to enhance the system performance of traditional SLS.

In large-scale environments, LIO-SAM (Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping) exhibits poor robustness due to the accumulation of errors caused by factors such as the prevalence of similar surroundings and the lack of features in certain open areas. Therefore, the purpose of this study is to optimize the loop detection module of LIO-SAM to reduce error accumulation and enhance mapping and localization performance.

Based on the LIO-SAM framework, the LinK3D (Linear Keypoints Representation for 3D LiDAR Point Cloud) feature extraction algorithm is integrated in the front end, while the BoW3D (Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM) loop detection algorithm is integrated in the back end. The features extracted by LinK3D serve as the range factors for the LiDAR, the BoW3D generates loop closure factors and these, along with inertial measurement unit (IMU) preintegration factors and global positioning system (GPS) factors, are added to the factor graph of LIO-SAM. This addition of constraints enhances the mapping and localization effects, optimizing the overall mapping and localization performance.

Based on the electrically controlled car, experiments were conducted in the experimental scenario proposed in this paper. Compared to LIO-SAM, the method presented in this paper significantly reduces cumulative errors. While ensuring real-time performance, it demonstrates superior mapping and localization effects.

This paper proposes and validates a method that integrates LinK3D, BoW3D and LIO-SAM, named LB-LIOSAM, which enhances the accuracy of feature extraction, optimizes the loop detection module of LIO-SAM and improves its mapping and localization performance in specific environmental scenarios.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

With multiple-related organizations, worldwide infections, deep economic recession and public disorder, and large consumption amount of anti-epidemic resources, the coronavirus disease 2019 (COVID-19) has been defined as a public health emergency of international concern (PHEIC). Nowadays, Wuhan has recovered from the pandemic disaster and reentered normalization. The purposes of this study are to (1) summarize organization collaboration patterns, successful experience and latent defects under across-stage evolution of Wuhan's cooperation governance mode against the pandemic, and on the basis, (2) reveal how the COVID-19 development trends and organizations' collaborative behaviors affected each other.

Detailed content analysis of online news reports covering COVID-19 prevention and control measures on the website of Wuhan Municipal Government was adopted to identify organizations and their mutual collaborative interrelationships. Four complex network (CN) models of organization collaboration representing the outbreak, preliminary control, recession and normalization stages, respectively, were established then. Time-span-based dynamic parameter analyses of the proposed networks, comprising network cohesiveness analysis and node centrality analysis, were undertaken to indicate changes of global and local characteristics in networks.

First, the definite collaborative status of Wuhan Headquarters for Pandemic Prevention and Control (WHPPC) has persisted throughout the period. Medical institutions and some other administrations were the most crucial participants collaborating with the WHPPC. Construction-industry organizations altered pandemic development trends twice to make the situation controllable. Media, large-scale enterprises, etc. set about underscoring themselves contributions since the third stage. Grassroots cadres and healthcare force, small and medium-sized enterprises (SMEs), financial institutions, etc. were essential collaborated objects. Second, four evolution mechanisms of organization collaboration responding to the COVID-19 in Wuhan has been proposed.

First, universality of Wuhan-style governance experience may be affected. Second, the stage-dividing process may not be the most appropriate. Then, data source was single and link characteristics were not considered when modeling.

This study may offer beneficial action guidelines to governmental agencies, the society force, media, construction-industry organizations and the market in other countries or regions suffering from COVID-19. Other organizations involved could also learn from the concluded organizations' contributions and four evolution mechanisms to find improvement directions.

This study adds to the current theoretical knowledge body by verifying the feasibility and effectiveness of investigating cooperation governance in public emergencies from the perspectives of analyzing the across-stage organization collaboration CNs.

Urging people to avoid stockpiling was a common declaration made by governments during the COVID-19 pandemic outbreak, yet empty supermarket shelves and supply shortages of basic products were observed worldwide. This study aims to (a) identify the factors that activate consumer personal norms towards socially responsible behaviours, specifically resisting stockpiling, and (b) examine how fear moderates the link between personal norms and consumer engagement in stockpiling during public crises.

The study recruited a sample of US consumers who were responsible for household grocery shopping during the COVID-19 pandemic. A total of 593 individuals participated in the study, and the collected data were analysed using structural equation modelling.

The results show that awareness of the negative consequences of stockpiling and a sense of personal responsibility for those consequences activate personal norms towards responsible shopping during public crises. However, perceived fear has the opposite effect, encouraging stockpiling. In addition, fear weakens the negative relationship between personal norms and stockpiling.

This study extends the norm activation model and indicates that personal norms may not always promote responsible behaviours when fear is high. It is unique in that it sheds light on non-mainstream responsible consumption behaviours (e.g. resisting stockpiling), and the interaction between consumption and social responsibility.

The purpose of this study is to investigate how the digital competence of academicians influences students’ engagement in learning activities in the face of the pandemic outbreak. In addition to this, the paper investigates how digital competence influences each dimension of student engagement (cognitive, affective and behavioural).

A cross-sectional, quantitative and explanatory research design was used to conduct the study. Data were gathered with an adopted questionnaire administered to a randomly selected sample of 500 university faculty members who were not digitally literate prior to the outbreak of the pandemic. Apart from the goodness of data tests, inferential statistics were applied to test hypotheses.

Results indicate a significant influence of teachers’ digital competence on student engagement and the pandemic outbreak positively moderates the relationship. Digital competence equally influences all three dimensions of student engagement.

The outbreak of COVID-19 made the adoption of digital life more compulsive and the nations with already available digital infrastructure and digital competence effectively minimized the adverse effect of social distancing as a result of the pandemic outbreak. Findings emphasize practitioners to focus on the digital capacity building of academicians and the provision of digital infrastructure to facilitate student engagement.

Society is transforming into a hi-tech lifestyle and technological advancement is penetrating almost every sphere of life at an unprecedented pace. From the digitalization of day-to-day affairs to e-governance, the adoption of technology is becoming a new normal. The outbreak of the pandemic overtook academic institutions equally. So, the social distancing compelled academicians and other stakeholders of universities to switchover from in-campus classes to online classes. The findings enrich the existing body of literature by explaining how digital competence has a determining role in ensuring student engagement amid the COVID-19 outbreak.

This research is a seminal work, as it tests the influence of digital competence on student engagement with the moderating role of the pandemic outbreak. To the best of the author’s knowledge, existing literature does not present this kind of research.

Based on the stimulus-organism-response (S-O-R) model, this study examines how safety measures, related assistance and tangible benefits affect consumers' emotional and cognitive states, leading to behavioural responses in an uncertain store environment.

The proposed model was tested with the survey data collected from grocery shoppers in Japan and Croatia (n = 314 in each country) and analysed using structural equation modelling.

Safety measures and related assistance decreased perceived threat in Croatia, enhanced arousal in both countries and caused fear in Japan. Tangible benefits reduced fear in Japan and increased arousal in Croatia. In a crisis, perceived threats push unplanned buying and motivate consumers to protect themselves. Arousal drives unplanned buying but diverts consumers from health-focussed behaviour. Loyalty can be gained if fear is controlled.

To retain consumers, retailers should secure a safe shopping environment that reduces fear and provides enough benefits to outweigh the threat.

Using the S-O-R framework, this study enriches the literature on consumer behaviour in a pandemic by contributing new insights into (1) the impact of safety measures and tangible benefits as stimuli, (2) the organismic response through affective and cognitive states, (3) health-focussed behaviour as a novel outcome and (4) comparing the effects in the two countries.