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AbstractPurpose:Materials and methods:Results and Discussion:\nIMPLICATIONS FOR REHABILITATIONFocusing on individuals with visual impairment, this study investigated the effects of spatial learning using tactile maps on orientation accuracy by path integration in physical and mental imagery walking scenarios.Twelve blindfolded sighted people learned nonlooping routes with two corners using tactile maps composed of volumetric raised-line elements, then navigated the routes physically and mentally. At four orientation points along the route—the starting point, Corner A, Corner B, and the endpoint—participants indicated the direction of the other points by aligning a raised, tapered rectangle attached to a horizontal digital protractor.During the physical and mental walking tasks, the participants’ mean orientation error values, representing the error in angle from the correct orientation, approximated zero for all orientation directions. However, the mean absolute error, i.e. the absolute value of the orientation error, ranged from 12.5° to 32.5° across different orientation points and tasks. As the participants followed the route, the absolute error relative to the next direction of travel increased, and the absolute errors for orientation points they had passed were substantially large. These results indicate that although tactile maps do not always enable precise orientation, they provide navigators with a surveyed spatial understanding that assists orientation through path integration. Furthermore, the mean difference in orientation error between mental and walking tasks measured on the same route for all directions was not significantly different from zero. This suggests that mental imagery walking with the tactile map helps predict orientation performance by path integration in navigators without vision.Learning routes using a tactile map is a useful approach for facilitating orientation based on path integration, even though it does not completely eliminate orientation-related errors.The orientation task, which uses a tactile map to perform the mental walking task, has potential as a new evaluation method for orientation ability through path integration in people with visual impairment.Learning routes using a tactile map is a useful approach for facilitating orientation based on path integration, even though it does not completely eliminate orientation-related errors.The orientation task, which uses a tactile map to perform the mental walking task, has potential as a new evaluation method for orientation ability through path integration in people with visual impairment. [ABSTRACT FROM AUTHOR]

The real-time and accurate detection and ranging of ships play a pivotal role in ensuring navigation safety, this study aims to enhance the navigation safety and environmental perception capabilities of inland waterway vessels. In the ship detection stage, addressing challenges such as large parameters, high computational complexity, and poor real-time performance in existing ship detection models, this paper proposes the MS-YOLOv5s ship target detection algorithm. This algorithm, based on YOLOv5s, utilizes the lightweight MobileNetV3-Small network to replace the original YOLOv5s feature extraction backbone network, thereby improving the detection speed. The results indicate that the parameter size of the MS-YOLOv5s model is 3.55M, only 50.49% of YOLOv5s. Achieving a detection rate of 50.28 FPS, the precision is 96.80%, and the mAP is 98.40%, striking a balance between high accuracy and low computational demand. In the depth estimation stage, influenced by the environment, leading to unstable measurement data, this paper proposes a binocular Kalman filter fusion ranging algorithm. The standard deviation of the ranging results is minimized to 6.032μm, which is one order of magnitude smaller than traditional ranging algorithms, significantly enhancing the robustness of the measurement results. Within a distance of 20m from the ship target, the error can be controlled within 3%, showcasing the applicability of the method proposed in this paper in complex inland waterway environments contributes to the enhancement of ships' environmental perception capabilities and navigation safety, holding positive implications for the development of intelligent vessels. [ABSTRACT FROM AUTHOR]

Horizontal and vertical viewing perspectives exert varying influences on the environmental perceptions and emotional states of college students. Despite this, scant research addresses the impact on this demographic. We selected typical campus open spaces for comprehensive physical parameter assessments, encompassing meteorological and spatial characteristics. A cohort of 36 healthy college students participated in a questionnaire survey designed to ascertain shifts in visual comfort, thermal comfort, and emotional responses when viewing landscapes in look-forward and look-up orientations. Key findings following both viewing modalities included: 1) a notable rise in mean visual comfort vote (MVCV), by 1.22 for look-forward and 1.01 for look-up, with a pronouncedly higher sunlight sensation vote (SSV) for the latter orientation; 2) a significant increase in thermal comfort vote (TCV), although the difference in increments between the two viewing angles was minimal; 3) Positive affect (PA) exhibited considerable improvement in both viewing conditions, while negative affect (NA) was markedly reduced in the look-up condition relative to look-forward viewing; 4) The SSV was predominantly influenced by the trunk ratio and canopy-to-trunk ratio, with substantial weights of 31.47% and 32.15%, respectively. Landscape element diversity emerged as the most critical factor affecting visual comfort vote (VCV) and aesthetic assessment score (AAS), with overwhelming weights of 70.67% and 63.15%, respectively. Moreover, the leaf ratio was identified as the chief determinant of emotional responses. Our results provide insights into the design of campus spaces for enhanced student well-being. [ABSTRACT FROM AUTHOR]

An important step in implementing geospatial digital twins is to enhance the expressiveness of virtual geographical scenes for the physical world. However, the existing virtual geographical scenes cannot quickly express the dynamically changing geographic environment for remote users due to the inefficient handling of modeling processes, user perception, and remote sharing. The research analysed the concept and characteristics of geospatial digital twins, and constructed the virtual geographical scene ontology, based on which we developed geographical spatiotemporal semantic rules and designed a dynamic annotation algorithm to enhance the representation of virtual geographical scenes. Finally, we investigated a real-time transmission method of panoramic video based on 5 G and used immersive virtual reality (IVR) to realize the user experience of remote immersion in geographical scenes. We selected a specific geographic environment containing multiple typical geographic entities to develop three prototype systems for experimental analyses. The results showed that the proposed method enabled users to view the virtual geographical scene on a VR device. The average latency for this process was 14.72 seconds. Compared with the virtual geographical scenes constructed by traditional methods, the experiments showed the proposed method advantageous in comprehensiveness, timeliness, and photorealism and abilities to enhance the user's geographical scene perception. [ABSTRACT FROM AUTHOR]

Information Architecture (IA) organises digital content ranging from web interfaces to ATM screens and car dashboards. Beyond structure, it encompasses the labelling and organisation of information within each interface. Presented through mind maps, IA is essential in both academic and professional environments. In complex products such as social networks, multiple IA patterns are employed simultaneously. This research introduces IA to Applied Communication Sciences, highlighting its significance for enhancing user experience in social networks. The study identifies standard patterns and new types of mental maps for digital creation. [ABSTRACT FROM AUTHOR]

Multispectral pedestrian detection with visible light and infrared images is robust to changes in lighting conditions and therefore is of great importance to numerous applications that require all‐day environmental perception. This paper proposes a novel method named FCE‐RCNN, which integrates saliency detection as a sub‐task and utilizes global information for enhanced feature representation. The approach enhances thermal inputs by incorporating gradients at the raw‐data level before feature extraction. Utilizing a dual‐stream backbone, a global semantic information extraction module is introduced that combines pooling with horizontal–vertical attention mechanisms, capturing high‐quality global semantic information for lower‐level feature enrichment and guidance. Additionally, the pedestrian locality enhancement module is designed to enhance spatial locality information of pedestrians through saliency detection. Furthermore, to alleviate the challenges posed by positional shifts between cross‐spectral features, deformable convolution is innovatively employed. Experimental results on the KAIST dataset demonstrate that FCE‐RCNN significantly improves nighttime detection, achieving a log‐average miss rate of 6.92%, outperforming the new method ICAFusion by 0.93%. These results underscore the effectiveness of FCE‐RCNN, and the method also maintains competitive inference speed, making it suitable for real‐time applications. [ABSTRACT FROM AUTHOR]

Aquaculture stakeholders have mental models, which are the internal cognitive representations of how they understand and prioritize the different features of their aquaculture systems. Individuals and stakeholder groups are likely to have different mental models, with implications for making cooperative governance work and guiding the rapidly emerging sector's sustainable development. We apply a participatory approach called fuzzy cognitive mapping to capture and compare the mental models of community-based coastal pond aquaculture stakeholders in Indonesia, including farmers, government managers, and researchers who need to work together to govern a rapidly expanding aquaculture sector which faces critical sustainability challenges. To conceptually structure our comparison, we use Elinor Ostrom's social–ecological systems framework. Our results highlight important differences between stakeholder group mental models which represent potential conflicts of interest and barriers for collaborative governance. Fish farmer models emphasize resource system challenges relating to production instability and risk, while government managers emphasize increasing production intensity to meet sectoral growth targets. Researchers, in contrast, tend to focus on pond waste treatment and water quality management. Governance attributes were consistently perceived as less frequent and less influential compared to other social–ecological dimensions, reflecting perceptions of weak governance in the sector. We identify a critical need for programs aimed at strengthening community-level institutional arrangements for governing shared aquaculture resources, increasing technical knowledge capacity, and managing financial risk. By merging all stakeholder models into a single "community" model, we identify key consensus action situations across the three groups as potential focal points for aquaculture development which may serve as a starting point for actors to work together to identify context-appropriate institutional solutions to these sustainability challenges. [ABSTRACT FROM AUTHOR]

Climate change communication is an important behavioral manifestation of the public's understanding, expression, and participation in addressing climate change. Social media play an important role in the climate change knowledge communication. Does social media promote climate change communication behavior in the Chinese context? Is its effect stronger than other types of media? Combined with the research context, we divide media into central media, local media, and social media and construct the influence mechanism model of media use on climate change communication behavior. In this study, a questionnaire survey was conducted among the public in China, and 1062 valid questionnaires were empirically tested by methods of hierarchical regression and bootstrapping. According to the findings of the study, different media use has a positive effect on climate change communication behavior. While social media is more likely to be used by the public to obtain climate change-related information than central and local media (with a mean value of 3.84 for social media compared to 3.51 for central media and 3.19 for local media), it is actually the central media that have the greatest effect on climate change communication behavior. This is evident in the total effect value, where the central media have a value of 0.21, which is higher than social media's value of 0.20 and local media's value of 0.12. Risk perception and environmental values play an important mediating role in the influence of media use on climate change communication behavior, among which environmental values have the largest mediating effect. (Specifically, the mediating effects of environmental values were 26.83%, 31.28%, and 38.57% for central media, local media, and social media, respectively.) In addition, risk perception can also positively affect environmental values, thus forming a chain mediating effect between media use and climate change communication behavior (the confidence intervals for the chain mediating effect also exclude the numbers 0). [ABSTRACT FROM AUTHOR]

Combining architectural and cultural anthropological approaches, this study explores the domestic lived spaces of Syrian women in Istanbul to understand how they create belonging in a new social and architectural setting and perform gender roles. We analyze data gathered from several types of dwellings according to the concept of spatial practice of Henri Lefebvre to explore how women's daily life praxis fosters feelings of contentment and safety, and how they reflect on their previous homes in Syria through a lens of nostalgia. At the same time, we explore how houses in Syria are remembered via reflections on spatial changes. Methodologically, we rely on semi-structured interviews and mental map drawings of houses in Istanbul and reminisced houses from Syria. Ultimately, this research provides a fine-grained portrait of the (lived) space of Syrian women, showing how they reconstruct domestic lives through past/Syrian and current/Turkish spatial practices. [ABSTRACT FROM AUTHOR]

This research investigates the use of compound image schemas in the discourse surrounding Japan's nuclear wastewater discharge. By analyzing editorials from Asahi Shimbun, this study reveals how discourse producers, from government bodies to media outlets, employ image schemas to navigate, shape, and legitimize their stances on this contentious issue. The investigation identifies the prevalent use of PATH and CONTAINER schemas, facilitating the construction of complex narrative spaces that define "in-group" and "out-group" dynamics, thereby guiding public perception and discourse participation towards an implicit endorsement of specific policy directions. The findings highlight the dynamic interplay between micro-level discourse strategies and macro-level cognitive frameworks, projecting tailored narratives into the public sphere. This projection, rooted in sophisticated cognitive mechanisms, illustrates the strategic use of discourse to influence public opinion and legitimize environmental policy decisions, reflecting broader social implications and the power of discourse in shaping environmental risk perceptions. [ABSTRACT FROM AUTHOR]

Purpose: The main objective of this paper is to examine the motivators and barriers parents face when acquiring secondhand products for their children while specifically investigating the potential influence of prior experience on the relationship between these motivators, barriers and parents' purchase intention toward secondhand clothing for their children. Design/methodology/approach: To address these objectives, a survey was conducted, yielding 265 valid responses. The sample comprised parents, with 96 having previous experience buying secondhand products and 169 without such experience. Multiple and binomial linear regression analyses were employed to examine the collected data. Findings: Two motivators (economic motivation and environmental sustainability) and three barriers (social embarrassment, hygiene and risk) were tested, and our findings indicate that environmental sustainability and the perception of risk significantly influenced the intention to buy secondhand products for childrenswear. The results showed that for consumers with previous experience, the perception of risk is nonsignificant, suggesting that experience influences consumers' barriers to buying secondhand products. Originality/value: This article is focused on the consumer behavior of parents who buy clothes for their children, and it is one of the few articles that proposes and tests a theoretical framework aiming to find empirical evidence about the motivators and barriers to consuming secondhand products in this market. Research limitations/implications: This study was specific to the childrenswear market, with characteristics that incentivize secondhand consumption, which might limit the findings. Practical implications: The results suggest that marketers should focus their efforts on highlighting sustainability claims when advertising their secondhand products to consumers and investing in incentivizing consumers to buy secondhand products for the first time, as it might reduce barriers to their consumption in the future. [ABSTRACT FROM AUTHOR]

Workshops provide a flexible approach to studying complex issues. This paper discusses the methodological considerations involved in doing research through workshops and aims to further the understanding of how workshops can facilitate interactive and creative knowledge practices in case study research. The paper builds on two research projects - where workshops were used to study the practices of professionals' use of digital tools at a consultancy firm and the everyday life of residents in a newly built city district. In the project about professional practices, three workshops were carried out with professionals from different disciplines and focused on their use of a specific digital tool, BIM. Post-its were used to introduce, explore and discuss different topics around everyday practices and digital tools, both individually and in various group arrangements. The three workshops built on each other and resulted in a concrete action plan for developing the company's work regarding BIM. In the research about the everyday life, the participants in five workshops were asked to draw a map of their home and other meaningful places in the city district. These individual "mental maps" were then used as a starting point for discussing the residents' everyday practices in a group setting. While the workshops in the two research projects targeted different groups and had different forms, they both made use of visual materials as a central aspect of workshops. Therefore, we analysed the workshops with a relational material framework to further understand how design choices affect research practice and research outcomes. From this lens, we analyse our choices in designing the workshops, the analytical processes and the consequences these choices have on what knowledge we create in interaction with the participants. [ABSTRACT FROM AUTHOR]

Environmental pollution has become a serious public concern in China. What impact does the Internet have on people’s perception of pollution? This study evaluates the causal effect of the Internet usage on the perception of air pollution by scrutinizing data from the China General Social Survey 2013. The results indicate that people who rely more heavily on the Internet for information perceive more severe pollution. This amplification effect cannot be attributed to the contextual factors including economic development or pollution severity through increasing the usage of the Internet. This study concludes with an exploratory mechanism analysis which demonstrates that people acquire greater environmental knowledge by using the Internet which serves to raise the awareness and amplify the perceived seriousness of air pollution. [ABSTRACT FROM AUTHOR]

Flexible multimodal sensors have gained increasing popularity for applications in healthcare and extreme environment operations owing to their all‐around environmental perception and data acquisition capabilities. However, fabricating a magnetism‐mechanics‐humidity multimodal sensor that possesses high sensitivity without signal overlapping while in a facile methodology remains a great challenge. Herein, a highly sensitive, conductive, and flexible hydrogel sponge sensor with discriminable magnetism, mechanics, and humidity sensing capability is proposed, which shows stable pore size (19.30 µm) and satisfactory mechanical properties based on the synergistic hydrogen bonding among sodium alginate, poly(vinyl alcohol) and glycerol. The proposed sensors can not only display favorable humidity sensing ability with rapid response/recovery time (2.5/4 s) but also possess enhanced sensitivities (a gauge factor of 0.46 T−1 for magnetic field, −1.16 kPa−1 for pressure), superior stability and durability (over 8000 cycles). Benefiting from the separated capacitive and resistive response signals, the sensors can precisely distinguish the magnetic, mechanical, and humidity stimuli without cross‐talk. Further, the sensor arrays assisted by the deep learning algorithm are developed to realize gesture language recognition with a high accuracy of 99.17%. It can be believed that this high‐performance sensor will have good prospects in future soft electronics and human‐machine interaction systems. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to examine the complex relationships between environmental monitoring efforts (EME), community engagement (CE), and environmental risk perception (ERP) in the context of improving environmental disaster preparedness (EDP) in the specific case of fluoride pollution. Using the Theory of Planned Behavior (TPB) as a conceptual framework, the study examines the effect of several variables on disaster preparation behavior in communities exposed to environmental risks. Methods: The data was collected from people living in Shanxi Province, China, an area that suffers from severe fluoride contamination. Results: The results of structural equation modeling (SEM) indicate that community engagement significantly improves the perception of environmental risk, which, in turn, positively impacts the preparation for disasters. Moreover, environmental monitoring efforts were shown to be a mediator of the association between community participation and disaster preparation, underscoring the crucial role that systematic monitoring plays in disaster management. The results indicate that including social and environmental elements is crucial for improving the ability of communities to withstand and recover from environmental catastrophes. Conclusion: The findings emphasize that there is an urgent need to consider social, and environmental aspects if the resilience of communities to, and their recovery from, environmental disasters is to be improved. [ABSTRACT FROM AUTHOR]

Background: Despite suicide's public health significance and global mental health awareness, current suicide prevention efforts show limited impact, posing a challenge for low- and middle-income countries. This study aimed to develop a dynamic simulation model that could be used to examine the potential effectiveness of alternative interventions for reducing youth mental health problems and suicidal behavior in Bogotá, Colombia. Methods: A system dynamics model was designed using a participatory approach involving three workshops conducted in 2021 and 2022. These workshops engaged 78 stakeholders from various health and social sectors to map key mental health outcomes and influential factors affecting them. A model was subsequently developed, tested, and presented to the participants for interactive feedback, guided by a moderator. Simulation analyses were conducted to compare projected mental health outcomes for a range of intervention scenarios with projections for a reference scenario corresponding to business-as-usual. Results: A total of 6670 suicide attempts and 347 suicides are projected among 7 − 17-year-olds from January 1, 2023, to early 2031 under the business-as-usual scenario. Mental health issues among 12 − 17-year-olds are projected to increase from 18.9% (2023) to 27.8% (2031), and substance use issues from 2.29 to 2.49% over the same period. School-based suicide prevention and gatekeeper training are the most effective strategies, reducing total numbers of suicide attempts and suicides by more than 20% (i.e., compared to business-as-usual). However, discontinuous funding significantly hinders these effective suicide prevention efforts. Conclusions: Systems modelling is an important tool for understanding where the best strategic financial and political investments lie for improving youth mental health in resource-constrained settings. [ABSTRACT FROM AUTHOR]

Understanding public perception of technology is crucial to aligning research, development, and governance of technology. This article introduces micro scenarios as an integrative method to evaluate mental models and social acceptance across numerous technologies and concepts using a few single-item scales within a single comprehensive survey. This approach contrasts with traditional methods that focus on detailed assessments of as few as one scenario. The data can be interpreted in two ways: Perspective (1): Average evaluations of each participant can be seen as individual differences, providing reflexive measurements across technologies or topics. This helps in understanding how perceptions of technology relate to other personality factors. Perspective (2): Average evaluations of each technology or topic can be interpreted as technology attributions. This makes it possible to position technologies on visuo-spatial maps to simplify identification of critical issues, conduct comparative rankings based on selected criteria, and to analyze the interplay between different attributions. This dual approach enables the modeling of acceptance-relevant factors that shape public opinion. It offers a framework for researchers, technology developers, and policymakers to identify pivotal factors for acceptance at both the individual and technology levels. I illustrate this methodology with examples from my research, provide practical guidelines, and include R code to enable others to conduct similar studies. This paper aims to bridge the gap between technological advancement and societal perception, offering a tool for more informed decision-making in technology development and policy-making. [ABSTRACT FROM AUTHOR]

Within the area of environmental perception, automatic navigation, object detection, and computer vision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which have spawned the creation of a large number of related applications. At the same time, with the rapid increase in autonomous systems in the market, energy consumption has become an increasingly critical issue in computer vision and SLAM (Simultaneous Localization and Mapping) algorithms. This paper presents the results of a detailed review of over 100 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement in computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the state-of-the-art advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis of conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM algorithms in the context of autonomous vehicles was explored. Additionally, in the context of intelligent robots for low-carbon, unmanned factories, we discussed model optimization techniques such as pruning and quantization, highlighting their importance in enhancing energy efficiency. We conducted a comprehensive comparison of the performance and energy consumption of various computer vision algorithms, with a detailed exploration of how to balance these factors and a discussion of potential future development trends. [ABSTRACT FROM AUTHOR]

The rapid development of ocean exploration and underwater robot technology has put forward new requirements for underwater sensing methods, which can be used for hydrodynamic characteristics perception, underwater target tracking, and even underwater cluster communication. Here, inspired by the specialized undulated surface structure of the seal whisker and its ability to suppress vortex‐induced vibration, a multidirectional hydrodynamic sensor based on biomimetic whisker array structure and magnetic 3D self‐decoupling theory is introduced. The magnetic‐based sensing method enables wireless connectivity between the magnetic functional structures and electronics, simplifying device design and endowing complete watertightness. The 3D self‐decoupling capability enables the sensor, like a seal or other organisms, to perceive arbitrary whisker motions caused by the action of water flow without complex calibration and additional sensing units. The whisker sensor is capable of detecting a variety of hydrodynamic information, including the velocity (RMSE < 0.061 m s−1) and direction of the steady flow field, the frequency (error < 0.05 Hz) of the dynamic vortex wake, and the orientation (error < 7°) of the vortex wake source, demonstrating its extensive potential for underwater environmental perception and communication, especially in deep sea conditions. [ABSTRACT FROM AUTHOR]

Environmental risk is a special risk that threatens human life and property and may affect people's environmental behavior, while pro-environmental behavior is the key to coping with environmental problems. This study explores the mechanisms by which environmental risk perception influences pro-environmental behavior based on the protective motivation theory (PMT) and the stimulus-organism-response model (SOR). In this mechanism, we believe that two factors, environmental concern, and natural exposure, play essential roles, and we developed a theoretical model of environmental risk perception influencing pro-environmental behavior. We recruited a sample of 626 general residents (316 males and 310 females) of age (mean (SD) = 37.37 ± 12.84). Participants filled out a paper-and-pencil questionnaire, which included questionnaires on general socio-demographic characteristics and an environmental, psychological scale assessing general environmental risk perceptions, environmental concern, nature exposure, and pro-environmental behavior. The study results showed that environmental risk perception promotes pro-environmental behaviors, environmental concern mediates the relationship between environmental risk perception and pro-environmental behaviors, and nature exposure positively moderates the relationship between environmental risk perception and environmental concern and thus influences pro-environmental behaviors. Our study explains how environmental risk perception affects pro-environmental behavior, constructs a theoretical model of environmental risk perception, and expands the scope of application of the PMT theory and the SOR model. Based on the results of this study, it is suggested that in environmental management, individuals should be aware of environmental risks and encouraged to get close to nature to promote pro-environmental behaviors and realize the harmonious coexistence between human beings and nature. [ABSTRACT FROM AUTHOR]

South Asian cities share a contested late colonial history. Calcutta (Kolkata) occupies a distinctive place in their emergence as modernising urban centres. Instead of viewing changes in Calcutta's markets merely as the products of colonial and racial hierarchies—or as the product of modernisation—mental maps of people moving through these spaces inform a deeper understanding. Attempts by the Calcutta municipality to 'modernise' markets using European assumptions about how urban markets work, failed to recognise the autonomous nature of indigenous 'mental maps'. The paper analyses these tensions through a street-level observational typology. An experiential legibility of urban spaces thus exposes an otherwise obscured haptic and psychological tension, as cities modernised, between a Westernised urban sensibility and an expanding autonomous space. [ABSTRACT FROM AUTHOR]

Objective: Alice in Wonderland syndrome (AIWS) profoundly affects human perception of size and scale, particularly regarding one's own body and the environment. Its neuroanatomical basis has remained elusive, partly because brain lesions causing AIWS can occur in different brain regions. Here, we aimed to determine if brain lesions causing AIWS map to a distributed brain network. Methods: A retrospective case–control study analyzing 37 cases of lesion‐induced AIWS identified through systematic literature review was conducted. Using resting‐state functional connectome data from 1,000 healthy individuals, the whole‐brain connections of each lesion were estimated and contrasted with those from a control dataset comprising 1,073 lesions associated with 25 other neuropsychiatric syndromes. Additionally, connectivity findings from lesion‐induced AIWS cases were compared with functional neuroimaging results from 5 non‐lesional AIWS cases. Results: AIWS‐associated lesions were located in various brain regions with minimal overlap (≤33%). However, the majority of lesions (≥85%) demonstrated shared connectivity to the right extrastriate body area, known to be selectively activated by viewing body part images, and the inferior parietal cortex, involved in size and scale judgements. This pattern was uniquely characteristic of AIWS when compared with other neuropsychiatric disorders (family‐wise error‐corrected p < 0.05) and consistent with functional neuroimaging observations in AIWS due to nonlesional causes (median correlation r = 0.56, interquartile range 0.24). Interpretation: AIWS‐related perceptual distortions map to one common brain network, encompassing regions critical for body representation and size‐scale processing. These findings lend insight into the neuroanatomical localization of higher‐order perceptual functions, and may inform future therapeutic strategies for perceptual disorders. ANN NEUROL 2024;96:662–674 [ABSTRACT FROM AUTHOR]

Humans excel at forming mental maps of their surroundings, equipping them to understand object relationships and navigate based on language queries. Our previous work SI Maps (Nanwani L, Agarwal A, Jain K, et al. Instance-level semantic maps for vision language navigation. In: 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE; 2023 Aug.) showed that having instance-level information and the semantic understanding of an environment helps significantly improve performance for language-guided tasks. We extend this instance-level approach to 3D while increasing the pipeline's robustness and improving quantitative and qualitative results. Our method leverages foundational models for object recognition, image segmentation, and feature extraction. We propose a representation that results in a 3D point cloud map with instance-level embeddings, which bring in the semantic understanding that natural language commands can query. Quantitatively, the work improves upon the success rate of language-guided tasks. At the same time, we qualitatively observe the ability to identify instances more clearly and leverage the foundational models and language and image-aligned embeddings to identify objects that, otherwise, a closed-set approach wouldn't be able to identify. Project Page - [ABSTRACT FROM AUTHOR]

When children enter adolescence, their personality traits easily give rise to psychological reactance (PR). PR involves a desire for autonomy and independence, as well as an aversion to parental and social rules and restrictions. Factors that influence PR include physiological, familial, and social aspects. However, most studies on adolescent noncompliance have primarily focused on rebellious behavior. Little research has examined motivational state reactance, although its interaction with environmental perception can significantly affect adolescents' lives. This study aimed to explore the relationship between the perceived friendliness of the community environment and cognitive processing at different quality of life (QoL) levels in relation to PR among adolescents. Propensity score matching was performed on a sample of 3546 adolescents, collected in Sanya, China. The results show that child-friendly communities (CFCs) had a significantly negative impact on adolescents' PR levels. Additionally, QoL had a moderating effect, meaning that the higher the QoL, the stronger the role of a CFC in alleviating PR. In contrast to claims that PR is determined by temperament or character profiles, this study reveals the importance of environmental shaping through triadic reciprocal determinism and a focus on the key role of the community environment. [ABSTRACT FROM AUTHOR]

This study aims to comprehensively examine the effects of different types of Internet use and happiness while considering the mediating role of environmental quality perception and the moderating role of a sense of environmental security. Drawing on the uses and gratifications theory, negativity bias, and social cognitive theory, the study investigates the mediating role of environmental quality perception and the moderating role of environmental security in the above relationship. Using data from 3162 respondents in the 2021 Chinese Social Survey (CSS) and Structural Equation Modeling (SEM), the study finds that Internet use for information and educational purposes significantly enhances happiness, with environmental quality perception acting as a mediator. Moreover, a moderating effect of environmental security was observed in the relationship between Internet use for educational purposes and national environmental quality perception. Specifically, the interaction between study-related Internet use and the sense of environmental security significantly and positively predicted national environmental quality perception. These findings highlight the complex interaction between Internet use, environmental factors, and happiness, offering insights into policy interventions aimed at improving Internet access and environmental awareness to enhance public mental health outcomes in China. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) represent a transformative innovation in transportation, promising enhanced safety, efficiency, and sustainability. Despite these promises, achieving robustness, reliability, and adherence to ethical standards in AV systems remains challenging due to the complexity of integrating diverse technologies. This survey reviews literature from 2017 to 2023, analyzing over 90 papers to explore the integration of knowledge graphs (KGs) into AV technologies. Our findings indicate that KGs significantly enhance AV systems by providing structured semantic understanding, improving real-time decision-making, and ensuring compliance with regulatory standards. The paper identifies that while KGs contribute to better environmental perception and contextual reasoning, challenges remain in their seamless integration with existing systems and in maintaining processing speed. We also address the ethical dimensions of AV decision-making, advocating for frameworks that prioritize safety and transparency. This review underscores the potential of KGs to address critical challenges in AV technologies, offering a hopeful and optimistic outlook for the development of robust, reliable, and socially responsible autonomous transportation solutions. [ABSTRACT FROM AUTHOR]

Air pollution has become an important environmental and public health problem in China; improving air quality has become one of the most important tasks of the Chinese central government in recent years. In all aspects of environmental governance, the public's pro-environmental behavior is crucial to comprehensively improve air quality. This study used data provided by the 2021 China General Social Survey (CGSS) to explore the relationship between air pollution perception, environmental knowledge, and pro-environmental behavior. The results of the study indicated that: (1) Residents' subjective perception of air pollution has a significant positive impact on residents' pro-environmental behaviors. The more serious the air pollution in the surrounding area is, the more likely residents are to adopt more pro-environmental behaviors; (2) Environmental knowledge also has a positive impact on residents' pro-environmental behaviors and individuals with higher environmental knowledge are more likely to adopt more pro-environment behaviors; and (3) Environmental knowledge plays a positive regulating role in the relationship between air pollution perception and pro-environmental behavior. Individuals with higher environmental knowledge are more willing to engage in more pro-environmental behaviors when they perceive air pollution. To promote the residents' pro-environmental behaviors, we should further strengthen the education of environmental knowledge and strengthen the disclosure of environmental information. This study provides valuable insights for China to formulate environmental governance policies, enrich the research on pro-environmental behavior to a certain extent, and provide lessons for other developing countries and high-polluting countries to find environmental governance methods. [ABSTRACT FROM AUTHOR]

With the rapid growth in demand for security surveillance, assisted driving, and remote sensing, object detection networks with robust environmental perception and high detection accuracy have become a research focus. However, single-modality image detection technologies face limitations in environmental adaptability, often affected by factors such as lighting conditions, fog, rain, and obstacles like vegetation, leading to information loss and reduced detection accuracy. We propose an object detection network that integrates features from visible light and infrared images—IV-YOLO—to address these challenges. This network is based on YOLOv8 (You Only Look Once v8) and employs a dual-branch fusion structure that leverages the complementary features of infrared and visible light images for target detection. We designed a Bidirectional Pyramid Feature Fusion structure (Bi-Fusion) to effectively integrate multimodal features, reducing errors from feature redundancy and extracting fine-grained features for small object detection. Additionally, we developed a Shuffle-SPP structure that combines channel and spatial attention to enhance the focus on deep features and extract richer information through upsampling. Regarding model optimization, we designed a loss function tailored for multi-scale object detection, accelerating the convergence speed of the network during training. Compared with the current state-of-the-art Dual-YOLO model, IV-YOLO achieves mAP improvements of 2.8%, 1.1%, and 2.2% on the Drone Vehicle, FLIR, and KAIST datasets, respectively. On the Drone Vehicle and FLIR datasets, IV-YOLO has a parameter count of 4.31 M and achieves a frame rate of 203.2 fps, significantly outperforming YOLOv8n (5.92 M parameters, 188.6 fps on the Drone Vehicle dataset) and YOLO-FIR (7.1 M parameters, 83.3 fps on the FLIR dataset), which had previously achieved the best performance on these datasets. This demonstrates that IV-YOLO achieves higher real-time detection performance while maintaining lower parameter complexity, making it highly promising for applications in autonomous driving, public safety, and beyond. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) Scene Understanding achieves environmental perception by extracting and analyzing point cloud data with wide applications including virtual reality, robotics, etc. Previous methods align the 2D image feature from a pre-trained CLIP model and the 3D point cloud feature for the open vocabulary scene understanding ability. We believe that existing methods have the following two deficiencies: (1) the 3D feature extraction process ignores the challenges of real scenarios, i.e., point cloud data are very sparse and even incomplete; (2) the training stage lacks direct text supervision, leading to inconsistency with the inference stage. To address the first issue, we employ a Masked Consistency training policy. Specifically, during the alignment of 3D and 2D features, we mask some 3D features to force the model to understand the entire scene using only partial 3D features. For the second issue, we generate pseudo-text labels and align them with the 3D features during the training process. In particular, we first generate a description for each 2D image belonging to the same 3D scene and then use a summarization model to fuse these descriptions into a single description of the scene. Subsequently, we align 2D-3D features and 3D-text features simultaneously during training. Massive experiments demonstrate the effectiveness of our method, outperforming state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In the context of rapid urbanization, accurately identifying the visual factors that influence environmental safety perception is crucial for improving urban transportation environments and enhancing pedestrian safety. With the increase in urban population density and traffic flow, optimizing urban environmental design to elevate residents' sense of safety has become a key issue in urban planning and management. However, the existing studies face numerous challenges in conducting large-scale quantitative analysis of environmental safety perception in complex scenarios, such as difficulties in data acquisition and limitations in analytical methods. This study addresses these challenges by applying image semantic segmentation and object detection techniques to extract key visual elements from street view images, combined with manual scoring and deep learning methods, to construct a road safety perception dataset. Using a LightGBM model and the SHAP interpretation framework, in this study, we identify the critical visual factors influencing environmental safety perception. An empirical study was conducted in Macau, a modern city where Eastern and Western cultures intersect, and tourism thrives. The findings reveal that: ① The overall environmental safety perception in the eight parishes and surrounding roads of Macau is relatively high, with significant regional differences in safety perception scores around Macau's parish roads; ② The proportions of buildings, sidewalks, roads, and trees in images are the four primary factors influencing environmental safety perception; ③ The proportions and quantities of visual elements interact with each other, and their reasonable distribution helps form clear spatial visibility and creates conducive activity spaces, thereby enhancing the perception of environmental safety. Through empirical analysis, this study uncovers the mechanisms by which visual elements in urban street scenes affect environmental safety perception, providing scientific evidence for urban planning and transportation environment improvement. The research holds theoretical significance and offers practical references for urban design and management, demonstrating broad application value. [ABSTRACT FROM AUTHOR]

Background: The physical inactivity pandemic not only has a negative impact on the physical and mental health of children and youth, but it is also a key contributor to the non-communicable disease (NCD) burden, particularly among low- and middle-income countries. The widespread effects of climate change, ranging from extreme weather events to worsening air quality, are exacerbating the physical inactivity pandemic, highlighting the need to undertake holistic interventions to address environmental barriers while promoting physical activity. Despite the potential benefits of active school transportation (AST) on physical activity and the environment, no study has examined the intersection between perceptions of AST, environmental perceptions, and moderate-to-vigorous physical activity (MVPA) among children and youth in India from a child and youth perspective. Methods: As a part of this cross-sectional, observational study, digital survey links were administered to 1,042 children and youth aged 5 to 17 (50.3% boys; 49.7% girls). Participants were recruited from 41 schools across 28 rural and urban regions in India. Children and youth provided information on various sociodemographic factors, perceptions of crime and air pollution, and peer support factors. MVPA data were collected using an adapted version of the IPAQ short-form questionnaire. The overall sample was stratified by age, gender, and location, resulting in a total of seven multiple linear regression models to assess the association between AST and MVPA. Results: Children and youth who engaged in AST were associated with higher MVPA than those who did not engage in AST (β = 18.020, 95% CI [5.890 to 30.149], p = 0.004), after adjusting for age, gender, and location. In contrast, perceptions of high crime in the neighbourhood (β = − 21.921, CI [−36.195 to −7.647], p = 0.003) and perceptions of air pollution (β = − 12.472, CI [−23.797 to −1.147], p = 0.031) were associated with lower MVPA. Moreover, having active friends was associated with higher MVPA (β = 32.391, CI [9.264 to 55.518], p = 0.006) than not having active friends. AST was significantly associated with higher MVPA in the boys, rural, and aged 13 to 17 models; however, this association was not statistically significant in the girls, urban, and aged 5 to 12 models. Conclusions: These findings highlight that promoting AST can potentially promote MVPA and contribute to mitigating the NCD burden among children and youth in India. Future policies and interventions should prioritize initiatives that promote AST, considering diverse sociodemographic factors, and addressing environmental challenges such as perceptions of crime and air pollution. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC 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.)

This study highlights the crucial role of tourists' perceptions of environmental issues in shaping environmentally sustainable behaviors, addressing a significant gap in understanding the psychological mechanisms behind these actions. It investigates how subjective norms mediate and behavioral beliefs moderate the relationship between these perceptions and behaviors. Employing a quantitative research method, data were collected from 440 international tourists visiting Turkey. Path analysis was conducted using AMOS to explore the mediator role, while the process macro was used to analyze the moderator role. Key findings reveal that environmental perception significantly influences environmentally sustainable vacation behavior and subjective norms, but does not affect behavioral beliefs. Furthermore, the mediating role of subjective norms was confirmed, whereas behavioral beliefs did not moderate the relationship. This study contributes to the literature by elucidating the complex interplay of psychological factors in promoting sustainable tourism behavior, offering valuable insights for policymakers and stakeholders in the tourism industry. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute 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.)

The sensory–neuromorphic interface is key to the application of neuromorphic electronics. Artificial spiking neurons and artificial sensory nerves have been created, and a few studies showed a complete neuromorphic system through cointegration with synaptic electronics. However, artificial synaptic devices and systems often do not work in real environments, which limits their ability to provide realistic neural simulations and interface with biological nerves. We report a sensory–neuromorphic interface that uses a fiber synapse to emulate a biological afferent nerve. For the first time, a sensing–neuromorphic interface is connected to a living organism for peripheral nerve stimulation, allowing the organism to establish a connection with its surrounding environment. The interface converts perceived environmental information into analog electrical signals and then into frequency‐dependent pulse signals, which simplify the information interface between the sensor and the pulse‐data processing center. The frequency of the interface shows a sublinear dependence on strain amplitude at different stimulus intensities, and can deliver increased frequency spikes at potentially damaging stimulus intensities, similar to the response of biological afferent nerves. To verify the application of this interface, a system that monitors strain and provides an overstrain alarm was constructed based on this afferent neural circuit. The system has a response time of <2 ms, which is compatible with the response time in biological systems. The interface can be potentially extended to process signals from almost any type of sensors for other afferent senses, and these results demonstrate the potential for neuromorphic interfaces to be applied to bionic sensory interfaces. [ABSTRACT FROM AUTHOR]

The smellscape of garden plants plays a crucial role in promoting human well-being. Despite this, empirical data on the specific effects of distinct stimulation methods on public health remain insufficient. The objective of this research is to investigate the influence of three distinct sensory modalities, olfactory, visual, and their combined effect, on both physiological and psychological reactions to a pine forest's smellscape. A sample of 95 college students was selected, with data collected through both physiological and psychological measurements. The analysis focused on variables such as blood pressure (BP), pulse rate (P), pulse pressure (PP), skin conductance (SC), brainwave patterns (α, β), the odor emotion semantic differential (SD), and the State Anxiety Inventory (S-AI) scale. The results reveal that in the pine forest aroma environment, the central nervous system shows a compromise effect during olfactory–visual interaction, with greater autonomic nervous system (ANS) activation compared with either stimulus alone, suggesting cumulative effects. Psychologically, the influence of olfactory interaction on anxiety fell between that of visual and olfactory stimulation. Participants consistently reported that the combination of both the sight and scent of a pine forest was the most invigorating. Furthermore, research revealed that combining olfactory and visual stimuli led to a more profound amplification of positive environmental perceptions compared with when each sense was engaged individually. These findings lay the groundwork for understanding how garden plant aromas contribute to human well-being. [ABSTRACT FROM AUTHOR]

In the face of a highly competitive tourism market, when tourists hold positive brand equity towards a destination, it enhances the destination's ability to differentiate itself from competitors. This study focuses on the brand equity of organic agricultural tourism, using multidimensional scaling (MDS) to explore the factor structure of brand equity and the perceptual positioning of various tourism destination brands. The research targets tourists engaging in organic agricultural tourism in the Hualien and Taitung regions, with 220 valid questionnaires collected. The research findings indicate the following: 1. Among the 22 brand equity items, "loyalty to organic agricultural tourism", "awareness of organic agricultural products", "quality of organic agricultural products", "environmental sustainability", "image of healthy tourism", and "recognition of organic agricultural development" scored the highest. 2. The analysis revealed that the brand equity factors are ranked in the following order: BIHS, BACI, BPQ, BLO, and BAW. 3. Through MDS analysis, the five organic agricultural tourism destinations were categorized into high, medium, and low brand equity groups, illustrating the differentiated competitive relationships among these destinations. The top three factors influencing the brand perceptual maps were BAW, BIHS, and BPQ. The results of this study can serve as a reference for future research on brand equity in organic agricultural tourism and provide a scientific basis for the practical application of shaping brand equity and formulating competitive strategies. [ABSTRACT FROM AUTHOR]

Our study sought to understand how individuals think about their home location and county, including their geospatial awareness, and how they conceive of risk from a tornado geospatially. We conducted interviews and a sketch mapping activity with sixty-five individuals from Mississippi and Alabama. Our results indicated that participants could name and correctly locate only a few neighboring counties and that there was a difference of 4.7 miles on average between their perceived home location and their actual address. Participants labeled on average 9.1 features. Some maps displayed common heuristics or biases, but none was present in a majority of the sketch maps. Regarding geospatial risk personalization, participants’ drawings indicated that many inferred a tornado approaching from the southwest, and that they would take protective action when a tornado was within an average of fifteen miles of their home. Human landmarks and point locations were the features most frequently mentioned that would let participants know a tornado was coming close and participants estimated these features were on average 9.7 miles. Both distances showed a great deal of variation. The results have implications not only for tornado warning communication but also for understanding how individuals might think about a “close call.” [ABSTRACT FROM AUTHOR]

This study presents the results of the practical application of the first prototype of WEMoS, the Wearable Environmental Monitoring System, in a real case study in Singapore, along with two other wearables, a smart wristband to monitor physiological data and a smartwatch with an application (Cozie) used to acquire users' feedback. The main objective of this study is to present a new procedure to assess users' perceptions of the environmental quality by taking into account a multi-domain approach, considering all four environmental domains (thermal, visual, acoustic, and air quality) through a complete wearable system when users are immersed in their familiar environment. This enables an alternative to laboratory tests where the participants are in unfamiliar spaces. We analysed seven-day data in Singapore using a descriptive and predictive approach. We have found that it is possible to use a complete wearable system and apply it in real-world contexts. The WEMoS data, combined with physiology and user feedback, identify the key comfort features. The transition from short-term laboratory analysis to long-term real-world context using wearables enables the prediction of overall comfort perception in a new way that considers all potentially influential factors of the environment in which the user is immersed. This system could help us understand the effects of exposure to different environmental stimuli thus allowing us to consider the complex interaction of multi-domains on the user's perception and find out how various spaces, both indoor and outdoor, can affect our perception of IEQ. [ABSTRACT FROM AUTHOR]

Multisensor (MS) data fusion is important for improving the stability of vehicle environmental perception systems. MS joint calibration is a prerequisite for the fusion of multimodality sensors. Traditional calibration methods based on calibration boards require the manual extraction of many features and manual registration, resulting in a cumbersome calibration process and significant errors. A joint calibration algorithm for a Light Laser Detection and Ranging (LiDAR) and camera is proposed based on deep learning without the need for other special calibration objects. A network model constructed based on deep learning can automatically capture object features in the environment and complete the calibration by matching and calculating object features. A mathematical model was constructed for joint LiDAR-camera calibration, and the process of sensor joint calibration was analyzed in detail. By constructing a deep-learning-based network model to determine the parameters of the rotation matrix and translation matrix, the relative spatial positions of the two sensors were determined to complete the joint calibration. The network model consists of three parts: a feature extraction module, a feature-matching module, and a feature aggregation module. The feature extraction module extracts the image features of color and depth images, the feature-matching module calculates the correlation between the two, and the feature aggregation module determines the calibration matrix parameters. The proposed algorithm was validated and tested on the KITTI-odometry dataset and compared with other advanced algorithms. The experimental results show that the average translation error of the calibration algorithm is 0.26 cm, and the average rotation error is 0.02°. The calibration error is lower than those of other advanced algorithms. [ABSTRACT FROM AUTHOR]

The advance in the development of augmented reality technologies has attracted interest in their applicability in rescue scenarios. The characteristics of the different missions covered by First Responders, as well as the different objectives they can cover in a rescue operation, condition the importance of the additional information they can receive in these rescue processes through technology. This white paper aims to analyze the difficulties encountered when converging on the design of an interface that is adaptable to the professional and contextual circumstances in a rescue task. [ABSTRACT FROM AUTHOR]

The way that the relationship between university students and the city is conceptualised in geographic literature is strongly determined by the studentification debate, which risks subjectifying students in redundant and unilateral ways. This paper suggests that geographic education and its spatialities can inform this debate with an alternative subjectification, by emphasising the students’ capacity to construct their own criticism on the urban phenomena that they experience. It draws empirically from a place-based workshop conducted within a master’s programme at the University of Florence, which aimed at exploring the students’ sense of place through creative methods. Life charts, mental maps, urban diaries and short films, analysed together with textual material, showcase the complexity of sense of place: for students, visuality and affect contribute significantly to their feeling of alienation and its expression, but at the same time become instrumental to negotiating it by seeking a sense of belonging in the touristified and spectacularized city. When approached as “revealed” and “claimed” space, the spatialities creatively produced through the films prove that geographic education is capable of fostering the shaping of students as critical subjectivities, thus restoring the problematic subjectification performed by the studentification debate. [ABSTRACT FROM AUTHOR]

This study delves into the intricate mechanisms by which states employ a complex network of competing and intersecting borders—both real and imagined—to delineate and perpetuate the image of Syrian refugees as security risks. Drawing upon insights from border studies, securitization theory, and framing analysis, we explore the nuanced processes of mental mapping and bordering within the context of the Syrian crisis. By scrutinizing the construction of these borders and mental maps, we highlight the deliberate state‐driven narrative that portrays Syrians as threats, emphasizing that such perceptions are not inherent but rather intentionally crafted. Our investigation sheds light on the state's agency in framing Syrians as threats, a narrative rarely challenged despite the multifaceted nature of the refugee crisis. Through an expanded discussion on historical, geopolitical, and socio‐cultural dimensions, we aim to provide a comprehensive understanding of the intricate dynamics underlying the portrayal of Syrian refugees as perennial security concerns. [ABSTRACT FROM AUTHOR]

Scene segmentation is widely used in autonomous driving for environmental perception. Semantic scene segmentation has gained considerable attention owing to its rich semantic information. It assigns labels to the pixels in an image, thereby enabling automatic image labeling. Current approaches are based mainly on convolutional neural networks (CNN), however, they rely on numerous labels. Therefore, the use of a small amount of labeled data to achieve semantic segmentation has become increasingly important. In this study, we developed a domain adaptation framework based on optimal transport (OT) and an attention mechanism to address this issue. Specifically, we first generated the output space via a CNN owing to its superior of feature representation. Second, we utilized OT to achieve a more robust alignment of the source and target domains in the output space, where the OT plan defined a well attention mechanism to improve the adaptation of the model. In particular, the OT reduced the number of network parameters and made the network more interpretable. Third, to better describe the multiscale properties of the features, we constructed a multiscale segmentation network to perform domain adaptation. Finally, to verify the performance of the proposed method, we conducted an experiment to compare the proposed method with three benchmark and four SOTA methods using three scene datasets. The mean intersection-over-union (mIOU) was significantly improved, and visualization results under multiple domain adaptation scenarios also show that the proposed method performed better than semantic segmentation methods. [ABSTRACT FROM AUTHOR]

Background: Identifying the correlates of exercise behavior is essential to combating physical inactivity as a public health concern. The purpose of this study is to identify the correlates of physical activity among urban Chinese adults under the social-ecological theoretical model in order to facilitate targeted interventions to promote physical activity. Methods: Using the socio-ecological model, we conducted a questionnaire survey among 1459 urban residents in Zhejiang and Shaanxi provinces of China, collecting data on individual demographic factors, sociological factors, environmental perception, and exercise behavior. Binary logistic regression models were employed to analyze the relationships between exercise behavior and socio-ecological factors. Results: Male gender (p < 0.01), advanced age (p < 0.001), higher education level (p < 0.05), living independently from parents (p < 0.05), absence of childcare responsibilities (p < 0.01), residence in a county/prefecture-level city (p < 0.001), favorable neighborhood esthetics (p < 0.001), available greenways/parks (p < 0.001), and family support for exercise participation (p < 0.05) were significantly correlated with an increased likelihood of participating in physical activity. Male gender (p < 0.001), advanced age (p < 0.001), absence of childcare responsibilities (p < 0.05), good neighborhood vegetation (p < 0.01), availability of free neighborhood exercise facilities (p < 0.05), and support from friends for exercise participation (p < 0.01) were significantly correlated with an increased likelihood of engaging in physical activity for more than 150 min per week. BMI, community air quality, traffic safety, public safety, and level of social development were not major correlates. Conclusions: To promote exercise behavior, more attention should be paid to individuals who are female, young, have lower levels of education, bear childcare responsibilities, or reside in provincial capitals in China. Improving the habitat environment and providing convenient and affordable facilities should also be considered. Furthermore, support from family and friends can positively reinforce exercise behavior. [ABSTRACT FROM AUTHOR]

In autonomous driving, environmental perception technology often encounters challenges such as false positives, missed detections, and low accuracy, particularly in detecting small objects and complex scenarios. Existing algorithms frequently suffer from issues like feature redundancy, insufficient contextual interaction, and inadequate information fusion, making it difficult to perform multi-task detection and segmentation efficiently. To address these challenges, this paper proposes an end-to-end multi-task environmental perception model named YOLO-Mg, designed to simultaneously perform traffic object detection, lane line detection, and drivable area segmentation. First, a multi-stage gated aggregation network (MogaNet) is employed during the feature extraction process to enhance contextual interaction by improving diversity in the channel dimension, thereby compensating for the limitations of feed-forward neural networks in contextual understanding. Second, to further improve the model's accuracy in detecting objects of various scales, a restructured weighted bidirectional feature pyramid network (BiFPN) is introduced, optimizing cross-level information fusion and enabling the model to handle object detection at different scales more accurately. Finally, the model is equipped with one detection head and two segmentation heads to achieve efficient multi-task environmental perception, ensuring the simultaneous execution of multiple tasks. The experimental results on the BDD100K dataset demonstrate that the model achieves a mean average precision (mAP50) of 81.4% in object detection, an Intersection over Union (IoU) of 28.9% in lane detection, and a mean Intersection over Union (mIoU) of 92.6% in drivable area segmentation. The tests conducted in real-world scenarios show that the model performs effectively, significantly enhancing environmental perception in autonomous driving and laying a solid foundation for safer and more reliable autonomous driving systems. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the impact of customers' environmental concerns and perceptions of ecofriendly hotels on their willingness to pay more for these hotels. According to the study's findings, environmental concerns and the perception of environmentally friendly hotels have a positive effect on the willingness to pay more for these hotels. Businesses that aim to implement a hotel service approach with high environmental awareness should conduct studies to be able to understand the sensitivity to environmental concerns as indicated from this study. The significance of the ecofriendly hotel concept has been highlighted from the results of this study, and it has become clear that there should be more research to support tourist lodging at these businesses. [ABSTRACT FROM AUTHOR]

This paper explores cognitive place associations; conceptualised as a place‐based mental model that derives subconscious links between geographic locations. Utilising a large corpus of online discussion data from the social media website Reddit, we experiment on the extraction of such geographic knowledge from unstructured text. First we construct a system to identify place names found in Reddit comments, disambiguating each to a set of coordinates where possible. Following this, we build a collective picture of cognitive place associations in the United Kingdom, linking locations that co‐occur in user comments and evaluating the effect of distance on the strength of these associations. Exploring these geographies nationally, associations were shown to be typically weaker over greater distances. This distance decay is also highly regional, rural areas typically have greater levels of distance decay, particularly in Wales and Scotland. When comparing major cities across the UK, we observe distinct distance decay patterns, influenced primarily by proximity to other cities. [ABSTRACT FROM AUTHOR]

Copyright of Psychological Science is the property of Psychological Science Editorial Office 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.)