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Copyright of Trabalhos em Lingüística Aplicada is the property of Universidade Estadual de Campinas - Portal de Periodicos Eletronicos Cientificos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Driving a motorized wheelchair is not without risk and requires high cognitive effort to obtain good environmental perception. Therefore, people with severe disabilities are at risk, potentially lowering their social engagement, and thus, affecting their overall well-being. Therefore, we designed a cooperative driving system for obstacle avoidance based on a trained reinforcement learning (RL) algorithm. The system takes the desired direction and speed from the user via a joystick and the obstacle distribution from a LiDAR placed in front of the wheelchair. Considering both inputs, the system outputs a pair of forward and rotational speeds that ensure obstacle avoidance while being as close as possible to the user commands. We validated it through simulations and compared it with a vector field histogram (VFH). The preliminary results show that the RL algorithm does not disruptively alter the user intention, reduces the number of collisions, and provides better door passages than a VFH; furthermore, it can be integrated on an embedded device. However, it still suffers from higher jerkiness. [ABSTRACT FROM AUTHOR]

The cooperative, connected, and automated mobility (CCAM) infrastructure plays a key role in understanding and enhancing the environmental perception of autonomous vehicles (AVs) driving in complex urban settings. However, the deployment of CCAM infrastructure necessitates the efficient selection of the computational processing layer and deployment of machine learning (ML) and deep learning (DL) models to achieve greater performance of AVs in complex urban environments. In this paper, we propose a computational framework and analyze the effectiveness of a custom-trained DL model (YOLOv8) when deployed in diverse devices and settings at the vehicle-edge-cloud-layered architecture. Our main focus is to understand the interplay and relationship between the DL model's accuracy and execution time during deployment at the layered framework. Therefore, we investigate the trade-offs between accuracy and time by the deployment process of the YOLOv8 model over each layer of the computational framework. We consider the CCAM infrastructures, i.e., sensory devices, computation, and communication at each layer. The findings reveal that the performance metrics results (e.g., 0.842 mAP@0.5) of deployed DL models remain consistent regardless of the device type across any layer of the framework. However, we observe that inference times for object detection tasks tend to decrease when the DL model is subjected to different environmental conditions. For instance, the Jetson AGX (non-GPU) outperforms the Raspberry Pi (non-GPU) by reducing inference time by 72%, whereas the Jetson AGX Xavier (GPU) outperforms the Jetson AGX ARMv8 (non-GPU) by reducing inference time by 90%. A complete average time comparison analysis for the transfer time, preprocess time, and total time of devices Apple M2 Max, Intel Xeon, Tesla T4, NVIDIA A100, Tesla V100, etc., is provided in the paper. Our findings direct the researchers and practitioners to select the most appropriate device type and environment for the deployment of DL models required for production. [ABSTRACT FROM AUTHOR]

Localizing tactile stimulation is an important capability for everyday function and may be impaired in people with persistent pain. This study sought to provide a detailed description of lumbar spine tactile localization accuracy in healthy individuals. Sixty-nine healthy participants estimated where they were touched at nine different points, labelled in a 3 × 3 grid over the lumbar spine. Mislocalization between the perceived and actual stimulus was calculated in horizontal (x) and vertical (y) directions, and a derived hypotenuse (c) mislocalization was calculated to represent the direct distance between perceived and actual points. In the horizontal direction, midline sites had the smallest mislocalization. Participants exhibited greater mislocalization for left- and right-sided sites, perceiving sites more laterally than they actually were. For all vertical values, stimulated sites were perceived lower than reality. A greater inaccuracy was observed in the vertical direction. This study measured tactile localization for the low back utilizing a novel testing method. The large inaccuracies point to a possible distortion in the underlying perceptual maps informing the superficial schema; however, further testing comparing this novel method with an established tactile localization task, such as the point-to-point method, is suggested to confirm these findings. [ABSTRACT FROM AUTHOR]

The exoskeleton robot is a wearable electromechanical device inspired by animal exoskeletons. It combines technologies such as sensing, control, information, and mobile computing, enhancing human physical abilities and assisting in rehabilitation training. In recent years, with the development of visual sensors and deep learning, the environmental perception of exoskeletons has drawn widespread attention in the industry. Environmental perception can provide exoskeletons with a certain level of autonomous perception and decision-making ability, enhance their stability and safety in complex environments, and improve the human–machine–environment interaction loop. This paper provides a review of environmental perception and its related technologies of lower-limb exoskeleton robots. First, we briefly introduce the visual sensors and control system. Second, we analyze and summarize the key technologies of environmental perception, including related datasets, detection of critical terrains, and environment-oriented adaptive gait planning. Finally, we analyze the current factors limiting the development of exoskeleton environmental perception and propose future directions. [ABSTRACT FROM AUTHOR]

To improve the rapidity of path planning for drones in unknown environments, a new bio-inspired path planning method using E-DQN (event-based deep Q-network), referring to introducing event stream to reinforcement learning network, is proposed. Firstly, event data are collected through an airsim simulator for environmental perception, and an auto-encoder is presented to extract data features and generate event weights. Then, event weights are input into DQN (deep Q-network) to choose the action of the next step. Finally, simulation and verification experiments are conducted in a virtual obstacle environment built with an unreal engine and airsim. The experiment results show that the proposed algorithm is adaptable for drones to find the goal in unknown environments and can improve the rapidity of path planning compared with that of commonly used methods. [ABSTRACT FROM AUTHOR]

Due to the operation of live-line working robots at elevated heights for precision tasks, a suitable visual assistance system is essential to determine the position and distance of the robotic arm or gripper relative to the target object. In this study, we propose a method for distance measurement in live-line working robots by integrating the YOLOv5 algorithm with binocular stereo vision. The camera's intrinsic and extrinsic parameters, as well as distortion coefficients, are obtained using the Zhang Zhengyou calibration method. Subsequently, stereo rectification is performed on the images to establish a standardized binocular stereovision model. The Census and Sum of Absolute Differences (SAD) fused stereo matching algorithm is applied to compute the disparity map. We train a dataset of transmission line bolts within the YOLO framework to derive the optimal model. The identified bolts are framed, and the depth distance of the target is ultimately calculated. And through the experimental verification of the bolt positioning, the results show that the method can achieve a relative error of 1% in the proximity of positioning. This approach provides real-time and accurate environmental perception for symmetrical structural live-line working robots, enhancing the stability of these robots. [ABSTRACT FROM AUTHOR]

The consumption of salad mixes has increased because of their convenience and nutritional value, resulting in significant sales increases during the past decade. Conversely, the uses of pest-control chemicals, long-distance transportation of salad mixes, and plastic packaging have raised environmental concerns among “green consumers.” Because proenvironmental products are becoming more widespread, this study delved into market segments of salad mix consumers based on their preferences for proenvironmental labels. Data for this study were collected via a 2020 web-based survey of 2100 salad mix consumers in the United States. We performed a comprehensive two-stage cluster analysis that integrated both hierarchical and partitioning methods. This analysis was based on consumers’ preferences and evaluations of production (low energy use, low fertilizer use, low greenhouse gas emissions, low water use, and pollinator-friendly) and marketing-related (biodegradable packaging, low carbon footprint, and low food miles) proenvironmental labels. Three segments were identified. We used ordered probit regression to assess the impact of consumer demographic characteristics, market preferences, and environmental perceptions on cluster membership. The deep-rooted segment, which represented 36% of the sample, highly valued all proenvironmental labels related to salad mixes and had a particular preference for labels that included low fertilizer use, pollinator-friendly production methods, and low greenhouse gas emissions. The indecisive segment comprised 40% of the sample and moderately valued all proenvironmental labels; this group mainly comprised individuals with lower income levels and those living in rural areas. The skeptic segment represented 23% of the sample and valued environmental labels less than the deep-rooted and indecisive segments did; additionally, they reported the lowest consumption of salad mixes. These findings can help retailers and policymakers communicate information about proenvironmental labels more effectively to each segment of salad mix consumers. [ABSTRACT FROM AUTHOR]

QuerySeed germination is a vital step in the life cycle of a plant, playing a significant role in seedling establishment and crop yield potential. It is also an important factor in the conservation of plant germplasm resources. This complex process is influenced by a myriad of factors, including environmental conditions, the genetic makeup of the seed, and endogenous hormones. The perception of these environmental signals triggers a cascade of intricate signal transduction events that determine whether a seed germinates or remains dormant. Despite considerable progress in uncovering the molecular mechanisms governing these processes, many questions remain unanswered. In this review, we summarize the current progress in the molecular mechanisms underlying the perception of environmental signals and consequent signal transduction during seed germination, and discuss questions that need to be addressed to better understand the process of seed germination and develop novel strategies for germplasm improvement. [ABSTRACT FROM AUTHOR]

Artisanal small‐scale fisheries are crucial for subsistence and food security, especially in developing countries. However, artisanal fishers face stressors, such as economic and social marginalization, conflicts, and overfishing. Socio‐economic attributes potentially influence their risk perception. We investigated risk perception among artisanal fishers in a Marine Protected Area through interviews and semi‐structured questionnaires. Risk perception was positively influenced by fishing environment and negatively influenced by a lack of religious beliefs. Offshore fishers faced more operating conditions and time at sea, with higher risk of losses, and perceived more health and conflict risks. Inshore fishers have higher perception of environmental degradation. Prioritizing different risk perceptions is essential for effective fisheries management. Separate management plans for offshore and inshore fisheries are recommended due to varying catch, environmental perception, socio‐environmental risks, and exploited resource characteristics among artisanal fisher groups. [ABSTRACT FROM AUTHOR]

Environmental problems on a global scale (such as climate change) are of increasing concern in the scholar. However, it is unclear to what extent society is aware of the severity of these problems. The aim of this study is to advance our knowledge about the environmental perceptions of citizens, with the understanding that citizen participation and action are key to mitigating environmental problems. Specifically, we aim to determine the degree of importance of demographic and sociocultural factors in this perception. To this end, data from 506 questionnaires carried out in Spain and Argentina were analysed. Quantitative data were treated using multivariate analysis based on multiple logistic regression. The results showed that demographic variables, such as location and age, have a greater influence than sociocultural variables, such as profession and sources of environmental information, in the perception of environmental problems. Our findings show that the analysis of environmental perception is a useful tool both for incorporating the point of view of local actors in public policies and for defining effective strategies for action in participatory management. They also show that the different age, occupation and profession of the population must be taken into account and that, to address specific environmental problems, it is necessary to carry out diagnoses focussed on demographic factors, mainly to find out which aspects should be strengthened through programmes and projects aimed at solving environmental problems. It is important to bear in mind that the behaviour of local actors is related to their environmental perception. [ABSTRACT FROM AUTHOR]

As the global population grows, the demand for textiles is increasing rapidly. However, this puts immense pressure on manufacturers to produce more fiber. While synthetic fibers can be produced cheaply, they have a negative impact on the environment. On the other hand, fibers from wool, sisal, fique, wood pulp (viscose), and man-made cellulose fibers (MMCFs) from cotton cannot alone meet the growing fiber demand without major stresses on land, water, and existing markets using these materials. With a greater emphasis on transparency and circular economy practices, there is a need to consider natural non-wood alternative sources for MMCFs to supplement other fiber types. However, introducing new feedstocks with different compositions may require different biomass conversion methods. Therefore, based on existing work, this review addresses the technical feasibility of various alternative feedstocks for conversion to textile-grade fibers. First, alternative feedstocks are introduced, and then conventional (dissolving pulp) and emerging (fibrillated cellulose and recycled material) conversion technologies are evaluated to help select the most suitable and promising processes for these emerging alternative sources of cellulose. It is important to note that for alternative feedstocks to be adopted on a meaningful scale, high biomass availability and proximity of conversion facilities are critical factors. In North America, soybean, wheat, rice, sorghum, and sugarcane residues are widely available and most suitable for conventional conversion through various dissolving pulp production methods (prehydrolysis kraft, acid sulfite, soda, SO2 -ethanol-water, and potassium hydroxide) or by emerging cellulose fibrillation methods. While dissolving pulp conversion is well-established, fibrillated cellulose methods could be beneficial from cost, efficiency, and environmental perspectives. Thus, the authors strongly encourage more work in this growing research area. However, conducting thorough cost and sustainability assessments is important to determine the best feedstock and technology combinations. [ABSTRACT FROM AUTHOR]

Copyright of Aposta is the property of Aposta and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ocean recreational activities (ORAs), including swimming, surfing, diving, paddleboarding, etc., in global marine areas are becoming increasingly popular. However, there are potential risks in coastal ocean environments. This study aims to establish a framework for assessing the risk according to the joint hazard and vulnerability levels of the ORA environment. Important factors include meteo-ocean conditions, geographic features, biological and chemical characteristics, and records of historical accidents, as well as social, environmental and tourist perceptions. The fuzzy Delphi method (FDM) is used to screen the representative factors, followed by using the analytical hierarchy process (AHP) to determine the weight of each factor. The results show that 9 hazard factors and 12 vulnerability factors are dominant. The framework is applied to 307 beaches in Taiwan, and 8% of them are categorized as very high risk and 13% as very low risk for ORAs. A risk map that displays the hazard, vulnerability, and overall risk levels is presented to tourists to provide quick access to risk information. [ABSTRACT FROM AUTHOR]

Within the rapidly growing urban tourism industry, the development of urban landscapes plays a crucial role in shaping a city's image and competitiveness; however, standardized and mismatched landscapes often have a negative impact, highlighting the importance of assessing urban landscape perceptions. Although existing studies have discussed this through subjective questionnaires and physiological methods, the underlying neural mechanisms have not been thoroughly explored. The research focuses on Jingdezhen, a renowned historical and cultural city in China, as its case study. Utilized the event-related potential (ERP) method to explore individuals' perceptual consistency and neural activity toward different types of urban landscapes. We adopted a 2 (landscape type: historical, modern) X 2 (perceptual match: consistent, inconsistent) within-subject design while recording behavioral data and electrophysiological responses. The results showed that, under any condition, there were no significant differences in people's behavioral data. Neurophysiological results indicate that consistent perceptions of modern landscapes elicited greater P200 responses, suggesting increased attention driven by visual aesthetics and emotional activation. Under conditions of perceptual inconsistency, historical landscapes elicited higher N400 amplitudes than modern landscapes, revealing cognitive conflict and effort. This study demonstrates that P200 and N400 components are effective indicators for assessing urban perception, proving the viability of the event-related potential method in urban landscape research. Additionally, the research reveals the neural mechanisms of urban environmental perception from the early stages of attention and emotional distribution to the later stages of cognitive decision-making, which involve cognitive processes from "bottom-up" to "top-down". This study not only provides a reference for efficient design planning for those involved in urban science but also inspires the coordination between the developmental needs of historical and modern urban landscapes. Moreover, it offers a new perspective for an interdisciplinary approach to urban perception assessment. [ABSTRACT FROM AUTHOR]

Previous studies on environmental restorative effects have mainly focused on visual landscapes, and less on the influence of soundscapes on restorative, but soundscapes play a crucial role in restorative environments, especially rural soundscapes, but there is insufficient existing theoretical evidence on the subject. Therefore, this study aims to investigate the influence of Rural Soundscape Perception on Environmental Restoration Perception, and introduces two affective variables, tourism nostalgia and place attachment, to explore the mechanism of Rural Soundscape Perception on Environmental Restoration Perception, as well as the moderating role of the number of trips is also discussed. Based on the theory of restorative environment, this study took the Taohuayuan Scenic Spot in Changde, Hunan Province, China, as the case site, and selected the rural soundscape in the area as the research object; a total of 506 valid data were collected through questionnaire surveys, and structural equation modeling was used to validate the collected data. It was found that rural soundscape perception had a significant positive effect on tourism nostalgia, place attachment, and environmental restoration perception. The results also showed that tourism nostalgia and place attachment mediated the relationship between rural soundscape perception and environmental restoration perception. Additionally, the results revealed that the number of trips did not play a moderating role in the structural relationship between rural soundscape perception and environmental restoration perception. Last, the results of the study shed light on the complex influence path of "rural soundscape perception→tourism nostalgia→place attachment→environmental restoration perception", which provides a new perspective for understanding the mechanism of the rural environment to people's health, and also has a certain guiding significance for the landscape planning of rural tourism sites. [ABSTRACT FROM AUTHOR]

Environmental perception is a complex issue that has significant impacts on public health. Despite limited research on subjective perceptions of the environment in various global contexts, including Ukraine, this study aimed to identify perceived environmental factors among urban residents and to assess associations with various health indicators. This is a cross-sectional study using data from the national survey carried out in 2017 in Ukraine. Data on demographic, behavioural, socioeconomic, and lifestyle risk factors were processed. A factor analysis was used to identify three environmental factors: outdoor infrastructure, environment, and safety and roads. The associations between these factors and health indicators were assessed using complex samples logistic regression models. The study showed that perceived environmental factors were significantly associated with body mass index, physical activity, stroke, stress, and harmful habits. Outdoor infrastructure emerged as the most important factor associated with health-related outcomes. Environmental neighbourhood attributes can significantly impact an individual's health, highlighting the need for policies and interventions that promote healthy neighbourhood environments, including improvements in outdoor infrastructure, community resources, and safety measures. Our findings suggest that urban environmental improvements should be a priority in primary disease prevention, would positively impact public health, and would be beneficial to the community. [ABSTRACT FROM AUTHOR]

Drawing from the characteristics of centipedes, such as their low center of gravity, high stability in movement, adaptability to complex terrains, and ability to continue moving even after losing a limb, this paper designs a self-reconfigurable centipede-type rescue robot with relatively high stability while moving. The robot's body can lift and traverse higher obstacles, and its multi-segmented structure enables self-disconnection and reconstruction for docking. Moreover, the proposed robot is adept at navigating diverse terrains and surmounting obstacles, equipped with a camera sensor facilitating life recognition, terrain surveying, scene understanding, and obstacle avoidance. Its capabilities prove advantageous for achieving challenging ground rescue missions. Motion stability tests, conducted across various terrains, showcase the robot's ability to maintain a consistent movement path in rugged environments. Operating with a leg lift height of 0.02 m, the robot achieves a speed of 0.09 m per second. In simulated damaged conditions, the robot demonstrates the capacity to disconnect and reconnect its limbs swiftly, restoring movement capabilities within a single second. During environmental perception tasks, the robot processes and analyzes environmental data in real time at a rate of approximately 15 frames per second, with an 80% confidence level. With an F1 score exceeding 93% and an average precision rate surpassing 98%, the robot showcases its reliability and efficiency. [ABSTRACT FROM AUTHOR]

In the light of the dramatic escalation of the Russian war on Ukraine since February 2022, questions concerning the handling of Russia in the post-Cold War era, and the enlargement of NATO in particular, are timelier than ever. This article scrutinises the views of George F. Kennan, one of the most influential critics of the decision to expand NATO. It is widely accepted that Kennan's opposition to NATO was grounded in realism about international affairs. A careful study of the development of his thinking, based on his private papers and archival sources from many countries, suggests that his perceptions of developments inside Russia and his imagination of the countries in Central and Eastern Europe were more important than any foreign policy doctrine. Kennan's criticism was grounded in his 'geopolitics of sympathy', understood as a fusion of mental maps, sympathies and personal connections towards the region affected by NATO enlargement. However, Kennan's geopolitics must be considered in conjunction with his ideas about international order, in particular with his defence of empire. [ABSTRACT FROM AUTHOR]

In this study, we examined ecopreneurial behavior in the COVID-19 context by analyzing the influence of moral obligation and environmental engagement on the perception of empathy, ecopreneurial self-efficacy, and environmental value creation. Primary data was collected from 130 Atlantic Forest Connection Project participants. Data analysis was performed using PLS-SEM. Six of eight hypotheses were supported. Our study confirmed that empathy and self-efficacy perception are influenced by higher moral obligation levels. Results also indicate that more environmentally engaged ecopreneurs have higher levels of self-efficacy and environmental value creation. On the other hand, the premise that a feeling of morality could positively impact environmental value creation arising from the business was contradicted. Similarly, the hypothesis that environmental engagement positively influences ecopreneurial empathy has not been confirmed. Thus, we advanced on ecopreneurship by opening a new research direction for testing the explanatory power of variables that have not been tested so far. The research also offers practical insights for policymakers and educators involved with ecopreneurship. In the same way, we shed light on farmers and agriculture. Additionally, we present contributions directly related to the UN Sustainable Development Goals (SDG) Agenda. [ABSTRACT FROM AUTHOR]

In this study, we investigate the environmental perception changes in college students during the COVID-19 pandemic. During the almost worldwide lockdown between March and December 2020, many students experienced dramatic changes in their socioeconomic life that have affected their well-being, ability to learn, and environmental perceptions. Students were sent home from college campuses, many businesses were shut down, and changes were seen in the environment throughout the world. In many places, lockdown policies lead to improved air, water, and land quality, mainly due to fewer vehicle and anthropogenic emissions. An online survey conducted on students enrolled in the University of South Florida and Florida State University demonstrates that most students have become more environmentally conscious after the COVID-19 lockdown, indicating significant perception changes. The students' demographic backgrounds played a major role in affecting their perception changes. Furthermore, mass media was found to have affected the student's perceptions, which demonstrates the important role media plays in directing public opinion, especially among college students and millennia. This study also highlights the environmental issues that underrepresented students face and calls for policies to address them on both educational and socioeconomic levels. [ABSTRACT FROM AUTHOR]

High-definition (HD) maps serve as crucial infrastructure for autonomous driving technology, facilitating vehicles in positioning, environmental perception, and motion planning without being affected by weather changes or sensor-visibility limitations. Maintaining precision and freshness in HD maps is paramount, as delayed or inaccurate information can significantly impact the safety of autonomous vehicles. Utilizing crowdsourced data for HD map updating is widely recognized as a superior method for preserving map accuracy and freshness. Although it has garnered considerable attention from researchers, there remains a lack of comprehensive exploration into the entire process of updating HD maps through crowdsourcing. For this reason, it is imperative to review and discuss crowdsourcing techniques. This paper aims to provide an overview of the overall process of crowdsourced updates, followed by a detailed examination and comparison of existing methodologies concerning the key techniques of data collection, information extraction, and change detection. Finally, this paper addresses the challenges encountered in crowdsourced updates for HD maps. [ABSTRACT FROM AUTHOR]

Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather circumstances present serious difficulties for object-detecting systems, which are essential to contemporary safety procedures, infrastructure for monitoring, and intelligent transportation. AVs primarily depend on image processing algorithms that utilize a wide range of onboard visual sensors for guidance and decisionmaking. Ensuring the consistent identification of critical elements such as vehicles, pedestrians, and road lanes, even in adverse weather, is a paramount objective. This paper not only provides a comprehensive review of the literature on object detection (OD) under adverse weather conditions but also delves into the ever-evolving realm of the architecture of AVs, challenges for automated vehicles in adverse weather, the basic structure of OD, and explores the landscape of traditional and deep learning (DL) approaches for OD within the realm of AVs. These approaches are essential for advancing the capabilities of AVs in recognizing and responding to objects in their surroundings. This paper further investigates previous research that has employed both traditional and DL methodologies for the detection of vehicles, pedestrians, and road lanes, effectively linking these approaches with the evolving field of AVs. Moreover, this paper offers an in-depth analysis of the datasets commonly employed in AV research, with a specific focus on the detection of key elements in various environmental conditions, and then summarizes the evaluation matrix. We expect that this review paper will help scholars to gain a better understanding of this area of research. [ABSTRACT FROM AUTHOR]

This study examines how acculturation – defined as the changes in cultural patterns after cultural contact between different groups – relates to environmental perceptions of Latin American and Caribbean immigrants living in Michigan, US. The study explores, via in-depth interviews, the roles of information sources, and the use of public spaces in the process. Results highlight that within acculturation, respondents either engaged in integration or separation – assimilation and marginalisation were not reported. Acculturation among study participants was largely associated with negative emotions, which was related in some cases with negative experiences in nature. Results also show that some immigrants are more aware of environmental issues in their country of origin, which could also influence how they think about issues in the United States. Finally, news consumption played a marginal role in informing those conceptualizations. Future research should explore the differences among this diverse group of people based on factors such as place of origin, identity, and place of residence. [ABSTRACT FROM AUTHOR]

As an important enabling technology in the era of Industry 4.0, the intelligent connected vehicle (ICV) facilitates robust data interaction with the outside through sensors and communication technologies, ultimately making scientific decisions based on environmental perception information. However, due to constraints such as limited communication bandwidth and computing resources, the influx of data simultaneously impedes the sustainable optimisation of the vehicle decision making process at the same time. As a novel technology that effectively connects physical and virtual space, the special ability of the digital twin (DT) is to identify characteristics within a certain lifecycle, thereby garnering widespread attention across various industries. The purpose of this paper is to review the contribution of digital twins in the application field of intelligent vehicles and explore its potential for development. First, the key technologies of ICV provide a basis for the embedding of digital twins. Then, by analysing the development process and technical composition of digital twins, readers can better understand the concept of digital twins. Finally, the application of DTs in ICV is reviewed from the perspective of vehicles, traffic facilities, and occupants. Future challenges and opportunities in this direction are described at the same time. [ABSTRACT FROM AUTHOR]

Current education policy in Aotearoa New Zealand (Aotearoa is the country's indigenous Māori name) requires schools and teachers to engage with parents and the school community to enhance student educational experience and achievement. The broad wording in these policy statements allows schools and teachers to tailor their parental engagement practice to specific community contexts. There is, however, little attention given to the built space of the school itself as an aspect of the material context within which parental engagement occurs. This article draws on a case study analysis of a single, bounded primary school in Aotearoa New Zealand, to examine how the school's built environment influences parental engagement. It involves the analysis of plans and other school artifacts, semi-structured interview transcripts of staff and parents, and the mental maps of parents. The findings reveal that multiple meanings are read from built space, with staff at risk of underestimating those readings and their agency to author new stories that better support parental engagement. [ABSTRACT FROM AUTHOR]

Instagram is widely recognized as one of the most popular digital platforms. Therefore, it is crucial to gain a comprehensive understanding of the goals and motivations of its users in order to effectively engage with this social media. This article presented the findings of a qualitative research study aimed at examining 26 Instagram users, who dedicated a minimum of 2 hours per day to the platform. The researchers employed the laddering interview method and means-end chain analysis to uncover the participants’ mental maps, resulting in the identification of 7 features, 14 functions, and 7 values represented by distinct ladders. According to the audience's perspectives, the most prominent feature of Instagram was its availability, while the most prevalent function derived from the research was the aspect of enjoyment. Furthermore, social compatibility emerged as the predominant value among the participants. These research outcomes highlighted Instagram as a distinctive social platform offering a diverse range of features that facilitated social connections and content sharing. By fostering a sense of amusement, Instagram enhanced communication quality and provided users with multiple values, including the ability to exert influence, mental tranquility, freedom, comfort, and success. The findings of this research held significant implications for companies and businesses seeking to formulate effective strategies and approaches to attract and retain Instagram users. Additionally, these study results could inform enhancement of design and development practices, not only for Instagram, but also for similar social platforms. [ABSTRACT FROM AUTHOR]

In the logistics warehouse environment, the autonomous navigation and environment perception of the logistics sorting robot are two key challenges. To deal with the complex obstacles and cargo layout in a warehouse, this study focuses on improving the robot perception and navigation system to achieve efficient path planning and safe motion control. For this purpose, a scheme based on an improved Gmapping algorithm is proposed to construct a high-precision map inside a warehouse through the efficient scanning and processing of environmental data by robots. While the improved algorithm effectively integrates sensor data with robot position information to realize the real-time modeling and analysis of warehouse environments. Consequently, the precise mapping results provide a reliable navigation basis for the robot, enabling it to make intelligent path planning and obstacle avoidance decisions in unknown or dynamic environments. The experimental results show that the robot using the improved Gmapping algorithm has high accuracy and robustness in identifying obstacles and an effectively reduced navigation error, thus improving the intelligence level and efficiency of logistics operations. The improved algorithm significantly enhances obstacle detection rates, increasing them by 4.05%. Simultaneously, it successfully reduces map size accuracy errors by 1.4% and angle accuracy errors by 0.5%. Additionally, the accuracy of the robot's travel distance improves by 2.4%, and the mapping time is reduced by nine seconds. Significant progress has been made in achieving high-precision environmental perception and intelligent navigation, providing reliable technical support and solutions for autonomous operations in logistics warehouses. [ABSTRACT FROM AUTHOR]

Strategies that motivate agrifood producers to adopt more sustainable practices are a critical component for a sustainable future. This case study examines farmer attitudes to a recently released New Zealand agricultural policy aimed at improving freshwater quality by restricting agricultural activities. Our study interprets interviews of nine individuals managing a range of dairy and sheep farming operations to explore how these farmers manage societal expectations of being a 'good farmer' in the context of the new regulations. Four themes were developed to capture perceptions of participants: the vested interest of farmers in positive community engagement and being seen as a 'good' farmer; the need to manage farms prudently for future generations; the unique geographical and environmental conditions on each farm and their concerns about the lack of acknowledgment of differences across land types in New Zealand; and the unintended consequences of the new regulations for rural communities. Overall, the themes suggest that the way the policy was enacted conflicts with perceptions of a 'good farmer' and good farming practice, resulting in identity conflicts for the farming community and reduced motivation to adopt alternative practices. [ABSTRACT FROM AUTHOR]

This empirical study builds upon prior research concerning cultural influences on spatial mental representations in Oceania. A comprehensive examination of 93 mental maps sourced from 59 lagoon fishers of Moorea (French Polynesia) reveals interesting facts about the way they organize and share their spatial knowledge. Firstly, consistent with previous studies across Oceania, Polynesian fishers exhibit a preference for the allocentric perspective when representing their environment. Secondly, they generally rely on marine landmarks for navigation, with a particular emphasis on four entities: the reef barrier, maritime beacons, coral outcrops, and a key chromatic marker — Moana (blue in Tahitian) — indicating the depth of the lagoon. Finally, the factor analysis we conducted highlights two significant facts: (1) a geographical self‐censorship, demonstrated by the low presence or even the absence of landmarks useful for locating their fishing spots; (2) a continuum between the surface and the depths of the lagoon, showing that surface fishers (line, net, troll, etc.) have a proven knowledge of seabed topography, whereas underwater speargun fishers also rely on landmarks located above the water. [ABSTRACT FROM AUTHOR]

Land use types other than specialized athletic fields provide a variety of jogging environments, addressing the shortage of urban fitness facilities and promoting urban health as well as sustainability. Currently, there is limited research comparing the differences in jogging support among various land use types, which can assist decision-makers in setting priorities and targeted strategies for urban renewal, especially in urban built-up areas with limited land resources. Initially, spatial information, statistical data, and recommendation reason text were extracted from recommended routes in mobile fitness apps and categorized into six land use types. Subsequently, spatial potential was measured through descriptive statistics, buffer area analysis, spatial autocorrelation analysis, and line density analysis. Environmental preferences were gauged by text analysis using jieba word segmentation and grouped word frequency calculation. Finally, the measurement results of different land uses were compared, including scale differences, spatial differentiation, environmental perception, and environmental elements. The research found that streets, residential areas, campuses, parks, and greenways possess significant potential to support jogging, particularly streets. These types of land use exhibit varying spatial potentials and attractions in environmental preferences. Targeted recommendations have been proposed to support the renewal of urban built-up areas and research in related fields. [ABSTRACT FROM AUTHOR]

This study measured the environmental perception (EP) of higher music education in the United States through the Higher Music Education Organizational Adaptation Survey. Music leaders were surveyed (n = 100) using items adapted from EP research in other disciplines. Diverse music units were found to exhibit moderate levels of EP and organizational complexity was found to be a significant factor in EP level. This study is highly significant for academic leaders in music due to the rapid evolution of the music industry and the need for higher music education to understand the complexity of its environment. Results also hold significance for all creative fields in higher education as a measurement of the relationship between academia and industry. [ABSTRACT FROM AUTHOR]

Foraging involves searching for resources distributed in space and time with varying nutritional values. New research suggests that free-ranging wild fruit bats track tree phenology, implicating the use of spatio-temporal mental maps. [ABSTRACT FROM AUTHOR]

The combination of perceptual dialectological and variationist linguistic work determines the structure of the collected volume, which is based on a conference held at the University of Vechta. Through seven articles, the volume delves into various linguistic regions of German, employing diverse methodologies in regional language research. The variation and perception of regional language features in Luxembourg are examined using a perceptual-dialectological approach, while a study focusing on perception and mental maps explores the transition area between East Franconian and North Bavarian, alongside a frame-semantic study on the recent thematisation of the opening of the inner-German border. Furthermore, a diachronic study evaluates spoken language in Hanover, rounding off the perceptual-dialectological approaches. The volume concludes with three variationist linguistic studies, which scrutinize regional and intergenerational differences in lenis plosives before sonorant, the pseudo-coordination of two verb forms in the Low German dialects of Schleswig-Holstein in comparison to other languages and the use of phraseologisms by different generations in the Rhineland and the Ruhr area. By showcasing the variety of methods and employing diverse corpora, the volume illustrates the potential and the thematic breadth of contemporary regional language research, fostering numerous interdisciplinary connections. [ABSTRACT FROM AUTHOR]

The article discusses the book "The Routledge Handbook of Urban Design Research Methods" edited by Hesam Kamalipour, Patricia Aelbrecht, and Nastarian Peimani. The book aims to establish urban design as its own discipline and explores various research methods and their real-world impact. It covers topics such as agency, affordance, place, informality, and performance. The book includes method-heavy chapters that provide innovative approaches to research, as well as case study chapters that discuss the outcomes of specific studies. Overall, the book contributes to the recognition of urban design as a legitimate discipline. [Extracted from the article]

In Advanced Driving Assistance Systems (ADAS), Automated Driving Systems (ADS), and Driver Assistance Systems (DAS), RGB camera sensors are extensively utilized for object detection, semantic segmentation, and object tracking. Despite their popularity due to low costs, RGB cameras exhibit weak robustness in complex environments, particularly underperforming in low-light conditions, which raises a significant concern. To address these challenges, multi-sensor fusion systems or specialized low-light cameras have been proposed, but their high costs render them unsuitable for widespread deployment. On the other hand, improvements in post-processing algorithms offer a more economical and effective solution. However, current research in low-light image enhancement still shows substantial gaps in detail enhancement on nighttime driving datasets and is characterized by high deployment costs, failing to achieve real-time inference and edge deployment. Therefore, this paper leverages the Swin Vision Transformer combined with a gamma transformation integrated U-Net for the decoupled enhancement of initial low-light inputs, proposing a deep learning enhancement network named Vehicle-based Efficient Low-light Image Enhancement (VELIE). VELIE achieves state-of-the-art performance on various driving datasets with a processing time of only 0.19 s, significantly enhancing high-dimensional environmental perception tasks in low-light conditions. [ABSTRACT FROM AUTHOR]

The mechanical LiDAR sensor is crucial in autonomous vehicles. After projecting a 3D point cloud onto a 2D plane and employing a deep learning model for computation, accurate environmental perception information can be supplied to autonomous vehicles. Nevertheless, the vertical angular resolution of inexpensive multi-beam LiDAR is limited, constraining the perceptual and mobility range of mobile entities. To address this problem, we propose a point cloud super-resolution model in this paper. This model enhances the density of sparse point clouds acquired by LiDAR, consequently offering more precise environmental information for autonomous vehicles. Firstly, we collect two datasets for point cloud super-resolution, encompassing CARLA32-128in simulated environments and Ruby32-128 in real-world scenarios. Secondly, we propose a novel temporal and spatial feature-enhanced point cloud super-resolution model. This model leverages temporal feature attention aggregation modules and spatial feature enhancement modules to fully exploit point cloud features from adjacent timestamps, enhancing super-resolution accuracy. Ultimately, we validate the effectiveness of the proposed method through comparison experiments, ablation studies, and qualitative visualization experiments conducted on the CARLA32-128 and Ruby32-128 datasets. Notably, our method achieves a PSNR of 27.52 on CARLA32-128 and a PSNR of 24.82 on Ruby32-128, both of which are better than previous methods. [ABSTRACT FROM AUTHOR]

Effective climate change adaptation requires a thorough understanding of whether and how affected populations perceive climatic and environmental changes. Existing research has been inconclusive regarding the consistency of these perceptions compared to objective meteorological indicators. Moreover, no systematic comparison has been done for the perception of discrete environmental events such as floods or erosion. This study relies on novel panel survey data of approximately 1700 households residing along the Jamuna River in Bangladesh as well as on unique individual-level, satellite-based erosion data. It compares respondents' perceptions of environmental events, namely riverbank erosion, and three climate change indicators, specifically long-term temperature change and changes in precipitation during wet and dry seasons, to objective measurements using satellite imagery and climatic time-series data (CRU TS). I find that long-term temperature change is perceived more accurately than long-term changes in precipitation. Given that educational attainment and climate change literacy among the study population are low, this indicates that global temperature increases are felt even by remote populations who have never heard the term climate change. Erosion is strongly overestimated, especially by those respondents who had been personally affected by it. Since human behavior is guided by perceptions rather than objective data, this has important policy implications, underlining the importance of considering people's perceptions if the goal is to assist them in adapting to environmental changes. [ABSTRACT FROM AUTHOR]

In a sustainable economy, each firm perceives different levels of carbon risk, but there are few studies on the relationship between a firm's carbon risk perception and Environmental, Social, and Governance (ESG) performance. This paper examines the impact of firm-level carbon risk perception on ESG performance using Chinese A-share listed firms during 2011–2020. We find a positive relationship between firm-level perception of carbon risk and firm ESG performance. Further analysis shows that analyst coverage and media attention exert a substantial moderating effect on the link between carbon risk perception and ESG performance. We identify three channels through which carbon risk perception affects ESG performance. First, rising carbon risk perception enables firms to take measures to reduce pollutants and greenhouse gas emissions and increases environmental (E) scores. Second, this perception encourages firms to take responsibility for social employment and increases social (S) scores. Finally, it improves firm sustainable governance and further increases governance (G) scores. Our research has an important guiding role in promoting firm ESG practice. [ABSTRACT FROM AUTHOR]

Amid the expanding green consumer market, companies are increasingly emphasizing their eco-friendly contributions in advertising. This study delves into the value delivery process within green consumption, guided by the servicedominant logic (S-D logic). A survey-based experiment with 589 responses reveals that the information frame in advertising significantly impacts consumer value perceptions and purchase intentions. Notably, the temporal frame of advertising plays a pivotal role, influencing consumers’ value perceptions. Future framing enhances emotional value perceptions, while past framing promotes environmental value perceptions. This research enhances our understanding of value delivery mechanisms in green consumption and holds valuable implications for marketers of sustainable products. [ABSTRACT FROM AUTHOR]

This article offers an introspective, intentionally imperfect, and contradictory exploration of various dimensions of multiple selves, belonging, and the migrant experience. It focuses on the author's personal experiences, examining the contradictions and challenges inherent to constructing a diasporic identity. The author unravels the complexities of self-identification and external perceptions, emphasizing how labels, names, and physical appearance influence interactions and self-understanding in specific cultural and geographical contexts. The adequacy of conventional words and terminology is questioned, as is their capacity to fully capture the richness and diversity of migratory and diasporic experiences. An argument is made for a more flexible conceptualization capable of accommodating the wide range of different backgrounds, challenges, and realities of racialized people and migrants of East Asian origin. Overall, the article proposes a re-evaluation and re-orientation of the dominant narratives and discourses around identity and migration. Through a personal reflection, it seeks to celebrate the multiplicity and complexity of diasporic identities and experiences using contradiction as a starting point. [ABSTRACT FROM AUTHOR]

Understanding why falls during pregnancy occur at over 25% rate over gestation has clinical impacts on the health of pregnant individuals. Attention, proprioception, and perception of the environment are required to prevent trips and falls. This research aimed to understand how the changes to these neurocognitive processes control obstacle avoidance through gestation. Seventeen pregnant participants were tested five times in 6-week intervals. Participants walked an obstacle course (OC), and we analyzed the crossings over obstacles that were set to 10% of participants' body height. Participants also performed an attentional network test (ANT: performance of specific components of attention), an obstacle perception task (OP: ability to visually define an obstacle and translate that to a body posture), and a joint position sense task (JPS: ability to recognize and recreate a joint position from somatosensation). In the OC task, average leading and trailing foot crossing heights significantly reduced by 13% and 23% respectively, with no change in variation, between weeks 13 and 31 of pregnancy, indicating an increased risk of obstacle contact during this time. The variability in minimum leading foot distances from the obstacle was correlated with all three neurocognition tasks (ANT, OP, and JPS). Increased fall rates in the second and third trimesters of pregnancy may be driven by changes in attention, with additional contributions of joint position sense and environmental perception at various stages of gestation. The results imply that a holistic examination on an individual basis may be required to determine individual trip risk and appropriate safety modifications. [ABSTRACT FROM AUTHOR]

Environmental perception is crucial for unmanned mobile platforms such as autonomous vehicles and robots. Precise and fast semantic segmentation of the surrounding scene is a key task to enhance this capability. Existing real-time semantic segmentation networks are typically based on convolutional neural networks (CNNs), which have achieved good results, but they still lack control over global context features. In recent years, the Transformer architecture has achieved significant success in capturing global context, which is beneficial for improving segmentation accuracy. However, Transformers tend to ignore local connections, and their computational complexity makes real-time segmentation challenging. We propose a lightweight real-time semantic segmentation network called DTMC-Net, which combines the advantages of CNNs and Transformers. We design a special residual convolution module called the Lightweight Multi-layer Separable Convolution Attention module (LMSCA) to reduce the parameter count and perform multi-scale feature fusion to capture local features effectively. We introduce the Simple Dual-Resolution Transformer (SDR Transformer) that utilizes lightweight attention mechanisms and residual feed forward networks to capture and maintain features, with multiple bilateral fusions between two branches to exchange information. The proposed Anti-artifact Aggregation Pyramid Pooling Module (AAPPM) optimizes the upsampling process, refines features, and performs multi-scale feature fusion again. DTMC-Net only contains 4.2M parameters and achieves good performance on multiple public datasets with different scenarios. [Display omitted] • Emphasizes real-time semantic segmentation for unmanned mobile platforms. • A lightweight model that combines the strengths of CNNs and Transformers is proposed. • Proposes modules to enhance multi-scale feature capture and refinement capabilities. • Competitive results are achieved on several challenging datasets. [ABSTRACT FROM AUTHOR]

Automotive ultrasonic sensors come into play for close-range surround sensing in parking and maneuvering situations. In addition to ultrasonic ranging, classifying obstacles based on ultrasonic echoes to improve environmental perception for advanced driver-assistance systems is an ongoing research topic. Related studies consider only magnitude-based features for classification. However, the phase of an echo signal contains relevant information for target discrimination. This study discusses and evaluates the relevance of the target phase in echo signals for object classification in automotive ultrasonic sensing based on lab and field measurements. Several phase-aware features in the time domain and time-frequency features based on the continuous wavelet transform are proposed and processed using a convolutional neural network. Indeed, phase features are found to contain relevant information, producing only 4% less classification accuracy than magnitude features when the phase is appropriately processed. The investigation reveals high redundancy when magnitude and phase features are jointly fed into the neural network, especially when dealing with time-frequency features. However, incorporating the target phase information facilitates the identification quality in high clutter environments, increasing the model's robustness against signals with low signal-to-noise ratios. Ultimately, the presented work takes one further step toward enhanced object discrimination in advanced driver-assistance systems. [ABSTRACT FROM AUTHOR]

Spontaneous eye blinks are modulated around perceptual events. Our previous study, using a visual ambiguous stimulus, indicated that blink probability decreases before a reported perceptual switch. In the current study, we tested our hypothesis that an absence of blinks marks a time in which perceptual switches are facilitated in‐ and outside the visual domain. In three experiments, presenting either a visual motion quartet in light or darkness or a bistable auditory streaming stimulus, we found a co‐occurrence of blink rate reduction with increased perceptual switch probability. In the visual domain, perceptual switches induced by a short interruption of visual input (blank) allowed an estimate of the timing of the perceptual event with respect to the motor response. This provided the first evidence that the blink reduction was not a consequence of the perceptual switch. Importantly, by showing that the time between switches and the previous blink was significantly longer than the inter‐blink interval, our studies allowed to conclude that perceptual switches did not happen at random but followed a prolonged period of nonblinking. Correspondingly, blink rate and switch rate showed an inverse relationship. Our study supports the idea that the absence or presence of blinks maps perceptual processes independent of the sensory modality. [ABSTRACT FROM AUTHOR]

This study explored the relationship between parents' environmental knowledge and 3- to 6-year-old children's nature connectedness, and the mediating role of parents' environmental concern and parent–child engagement in that relationship. Participants were 427 parents from rural western China (female = 51.99%; mean age = 30.87 years, SD = 6.81 years). The parents completed the following surveys: Connectedness to Nature Index for Parents of Pre-school Children (CNI-PPC), Chinese version of the Environmental Knowledge Scale (CEKS), Chinese version of the New Environmental Paradigm (CNEP), and Parent–Child Engagement Scale (PCES). Using structural equation modelling and the bootstrap method, the results revealed that higher parents' environmental knowledge was associated with increased parent-rated children's nature connectedness. Parents' environmental concern and parent–child engagement independently mediated the relationship between parents' environmental knowledge and parents' perceived nature connectedness of their children. Additionally, parents' environmental concern and parent–child engagement had a serial mediation effect on that relationship, contributing to increased parent-rated children's nature connectedness. These findings highlight the importance of considering parents' environmental knowledge, environmental concern, and parent–child engagement to strengthen children's nature connectedness. The implication of this study is that environmental education programs should target parents in addition to children, as parents' environmental knowledge, concern, and engagement are vital to the development of their children's nature connectedness. [ABSTRACT FROM AUTHOR]

Paratransit provides the majority of road transport in South Africa and many other countries in the sub-Saharan African region. However, perceptions of electric vehicles (EV) and the factors that influence intention to adopt EVs for the critical agents in this sector, vehicle owners and drivers, have yet to be explored. The aim of this study is to provide insights that can inform policies and strategies to promote EVs in the region. To achieve this, we collected 4452 survey responses from paratransit owners and drivers in South Africa. 38% of respondents expressed a willingness to purchase an EV when they become available. Using structural equation modeling, we test eleven hypotheses regarding the factors that influence EV adoption intention, based on an innovative integrated framework of consumer behavior. The results show that risk perceptions, environmental considerations and perception of cost have the strongest influence on EV adoption intention although many factors come into play, with ten out of the eleven hypotheses being supported. The most salient takeaway from the model output is that interventions that emphasize safety and business advantages of EVs will be the most effective at improving EV adoption intention, but that a diversity of thoughtful approaches will be necessary to ensure significant lasting change in this area. Finally, we use the coefficients from the structural model to simulate the effects of example interventions on EV adoption intention, and offer sensible guidelines for formulating targeted promotional strategies that will address the principal concerns of paratransit owners and drivers regarding EVs. • Survey analysis of 4452 paratransit drivers and owners in South Africa. • Investigates factors influencing EV adoption intention. • 38% of respondents intend to purchase an EV when available. • Vehicle reliability and personal norms are the strongest predictors of EV adoption intention. • Provides guidelines for a diversity of targeted interventions. [ABSTRACT FROM AUTHOR]