Search Results (2,671)

Indoor air pollution, including fine particulate matter (PM_2.5), poses a severe threat to human health. Due to the diverse sources of indoor PM_2.5 and its high spatial heterogeneity in distribution, traditional single-point fixed monitoring fails to accurately reflect the actual human exposure level. In recent years, the development of high spatiotemporal resolution monitoring technologies has provided a new perspective for revealing the dynamic distribution patterns of indoor PM_2.5. This study discusses two cutting-edge monitoring strategies: (1) mobile monitoring technology based on Indoor Positioning Systems (IPS) and portable sensors, which maps 2D exposure trajectories and concentration fields by having personnel carry sensors while moving; and (2) 3D dynamic monitoring technology based on in situ Lateral Scattering LiDAR (I-LiDAR), which non-intrusively reconstructs the 3D dynamic distribution of PM_2.5 concentrations using laser arrays. This study elaborates on the principles, calibration methods, application cases, advantages, and disadvantages of the two technologies, compares their applicable scenarios, and outlines future research directions in multi-technology integration, intelligent calibration, and public health applications. It aims to provide a theoretical basis and technical reference for the accurate assessment of indoor air quality and the prevention and control of health risks. Full article

Wayfinding with minimal effort is essential for reducing cognitive load and emotional stress in unfamiliar environments. This exploratory quasi-experimental study investigated wayfinding challenges in a university building housing three spatially dispersed counseling centers and three academic departments that share the building entrances, lobby, and hallways. Using mobile eye tracking with concurrent think-aloud protocols and schematic mapping, we examined visual attention patterns during predefined navigation tasks performed by 24 first-time visitors. Findings revealed frequent fixations on non-informative structural features, while existing wayfinding cues were often overlooked. High rates of null gazes indicated unsuccessful visual searching. Thematic analysis of verbal data identified eight key issues, including spatial confusion, aesthetic monotony, and inadequate signage. Participants frequently described the environment as disorienting and emotionally taxing, comparing it to institutional settings such as hospitals. In response, we developed wayfinding design proposals informed by our research findings, stakeholder needs, and contextual priorities. We used an experiential digital twin that prioritized perceptual fidelity to analyze the current wayfinding challenges, develop experimental protocols, and discuss design options and costs. This study offers a transferable methodological framework for identifying wayfinding challenges through convergent analysis of gaze patterns and verbal protocols, demonstrating how empirical findings can inform targeted wayfinding design interventions. Full article

The perception of 3D space by mobile robots is rapidly moving from flat metric grid representations to hybrid metric-semantic graphs built from human-interpretable concepts. While most approaches first build metric maps and then add semantic layers, we explore an alternative, concept-first architecture in which spatial understanding emerges from asynchronous concept agents that directly instantiate and manage semantic entities. Our robot employs two spatial concepts—room and door—implemented as autonomous processes within a cognitive distributed architecture. These concept agents cooperatively build a shared scene graph representation of indoor layouts through active exploration and incremental validation. The key architectural principle is hierarchical constraint propagation: Room instantiation provides geometric and semantic priors to guide and support door detection within wall boundaries. The resulting structure is maintained by a complementary functional principle based on prediction-matching loops. This approach is designed to yield an actionable, human-interpretable spatial representation without relying on any pre-existing global metric map, supporting scalable operation and persistent, task-relevant understanding in structured indoor environments. Full article

The Seaport City of Valparaíso, Chile, declared a World Heritage Site (WHS) by the United Nations, is known for its built landscape shaped during the first phase of globalization in the late 19th century, including early transport systems. However, the city now faces growing 21st-century urban and transport challenges common in Latin America. Amid the rise of electric micromobility in Chile, this study explores the potential use of electric bicycles (E-Bikes) in Valparaíso’s historic quarter. A qualitative methodology was employed, including participatory mapping to identify strengths, weaknesses, opportunities, and threats (SWOT), and focus group discussions supported by AI-based text analysis. Findings reveal that barriers to E-Bike use extend beyond topography and infrastructure, highlighting concerns such as deteriorated public spaces, lack of green areas, and safety issues. Promoting E-Bike adoption will require improving infrastructure, launching educational campaigns, optimizing routes, and fostering community participation. This study aims to inform local decision-makers on how to enhance sustainable mobility by integrating E-Bikes into the historical electric transport of Valparaíso, thereby contributing to the modernization of mobility within a heritage context. Full article

Active transport (AT) offers an effective and sustainable strategy to address physical inactivity, reduce traffic congestion, and mitigate environmental challenges. However, participation in AT among young adults (YA) aged 18–25 remains low, leading to public health issues. This review synthesises evidence on how traffic stress (TS), built environment (BE) features, and socioecological factors interact to shape AT behaviour among YA, a relationship that remains insufficiently understood. We systematically reviewed 173 peer-reviewed studies (2015–2025) from Web of Science (WoS), PubMed, and Scopus, following the PRISMA 2020 guidelines. Thematic analysis, bibliometric mapping, and meta-synthesis informed the impact of TS, the Level of Traffic Stress (LTS), the 5Ds of BE, and the Socioecological Model (SEM) on AT among YA. The findings show that high TS, characterised by unsafe road conditions, high-speed motor traffic, and inadequate walking or cycling facilities, consistently reduces AT participation. In contrast, supportive BE features, including street connectivity, land-use diversity, and destination accessibility, increase AT by reducing TS while enhancing safety and comfort. Socioecological factors, including self-efficacy, social norms, and peer support, further mediate these effects. This review introduces two novel metrics: Daily Traffic Stress (DTS), a time-sensitive measure of cumulative daily TS exposure, and the Stress-to-Step Ratio (SSR), a step-based index that standardises how stress exposures translate into AT behaviour. By integrating environmental and psychosocial domains, it offers a theoretical contribution as well as a practical foundation for targeted, multilevel policies to increase AT among YA and foster healthier, more equitable urban mobility. Full article

Productive and healthy soils are essential in agriculture and other economic uses of land which depend on plant growth, and are under increasing pressure globally. The physical properties of soil, its porosity and pore structure, also have a significant impact on a wide range of environmental factors, such as surface water runoff and greenhouse gas exchange. Methods exist for evaluating soil porosity that are applied in a laboratory environment or by inserting sensors into soil in the field. However, such methods do not readily sample adequately in space or time and are labour-intensive. The purpose of the current study is to investigate the potential for estimation of soil porosity and pore size using the strength of reflection of audio pulses from natural soil surfaces. Estimation of porous material properties using acoustic reflections is well established. But because of the complex, viscous interactions between sound waves and pore structures, these methods are generally restricted to transmissions at low audio frequencies or at ultrasonic frequencies. In contrast, this study presents a novel design for an integrated broad band sensing system, which is compact, inexpensive, and which is capable of rapid, non-contact, and in situ sampling of a soil structure from a small, moving, farm vehicle. The new system is shown to have the capability of obtaining soil parameter estimates at sampling distances of less than 1 m and with accuracies of around 1%. In describing this novel design, special care is taken to consider the challenges presented by real agriculture soils. These challenges include the pasture, through which the sound must penetrate without significant losses, and soil roughness, which can potentially scatter sound away from the specular reflection path. The key to this new integrated acoustic design is an extension of an existing theory for acoustic interactions with porous materials and rigorous testing of assumptions via simulations. A configuration is suggested and tested, comprising seven audio frequencies and three angles of incidence. It is concluded that a practical, new operational tool of similar design should be readily manufactured. This tool would be inexpensive, compact, low-power, and non-intrusive to either the soil or the surrounding environment. Audio processing can be conducted within the scope of, say, mobile phones. The practical application is to be able to easily map regions of an agricultural space in some detail and to use that to guide land treatment and mitigation. Full article

This paper presents the Women’s Wise Walkshops (WWW) project, a participatory feminist methodology for urban co-design implemented in Ferrara, Italy. The research explores how women’s situated knowledge and lived experiences can inform inclusive urban planning through collaborative urban traversals and participatory design processes. Drawing on feminist epistemologies and combining elements of flâneuserie and Situationist dérive, the WWW methodology employs a seven-phase approach including semi-structured interviews, focus groups, urban walkshops, and collective mapping exercises. The study involved approximately 110 women across two distinct neighborhoods—Arianuova-Giardino and Krasnodar—representing diverse socio-demographic backgrounds. Through a thematic analysis of interviews, visual documentation, and post-walkshop discussions, six key thematic clusters emerged: safety, public space, mobility systems, community spaces and associations, public services for citizens, and participatory processes. The findings reveal that women’s perspectives from marginalized positions provide critical insights into urban inequalities and offer transformative visions for more inclusive cities. The research shows that structured participatory processes not only generate valuable urban policy recommendations but also foster community cohesion, democratic engagement, and spatial justice. The WWW methodology represents a significant contribution to feminist urban studies and participatory planning, offering a replicable framework for integrating women’s voices into urban governance and design processes. Full article

Reliable and representative ground data is fundamental for accurate crop classification using satellite imagery. This study demonstrates a structured approach to ground truth planning in the Bareilly district, Uttar Pradesh, where wheat is the dominant crop. Pre-season spectral clustering of Sentinel-2 Level-2A NDVI time-series data (November–March) was applied to identify ten spectrally distinct zones across the district, capturing phenological and land cover variability. These clusters were used at the village level to guide spatially stratified and optimized field sampling, ensuring coverage of heterogeneous and agriculturally significant areas. A total of 197 ground truth points were collected using the iCrops mobile application, enabling standardized and photo-validated data collection with offline functionality. The collected ground observations formed the basis for random forest supervised classification, enabling clear differentiation between major land use and land cover (LULC) classes with an overall accuracy of 91.6% and a Kappa coefficient of 0.886. The findings highlight that systematic ground data collection significantly enhances the reliability of remote sensing-based crop mapping. The outputs serve as a valuable resource for agricultural planners, policymakers, and local stakeholders by supporting crop monitoring, land use planning, and informed decision-making in the context of sustainable agricultural development. Full article

Electric vehicles are key to sustainable mobility, but their limited range remains a major obstacle to widespread adoption. Extending driving distance requires optimizing energy use across subsystems. This study combines bibliometric mapping (2017–2024, Scopus) with a focused qualitative review to structure recent research. Results highlight a strong emphasis on energy efficiency, with China leading due to its market size, industrial base, and supportive policies. Major research directions tied to range extension include energy storage, motion control, thermal regulation, cooperative driving, and grid interaction. Among these, hybrid energy storage systems and motor control stand out for their measurable impact and industrial relevance, while thermal management, regenerative braking, and systemic approaches (V2V and V2G) remain underexplored. Beyond mapping contributions, the study identifies ongoing gaps and calls for integrated strategies that combine electrical, thermal, and mechanical aspects. As EV adoption accelerates and battery demand increases, the findings emphasize the need for battery-aware, multi-objective energy management strategies. This synthesis provides a vital framework to guide future research and support the development of robust, integrated, and industry-ready solutions for optimizing EV energy use and extending driving range. Full article

In the contemporary urban context, safety in public space presents profound inequalities linked to gender, especially in the night period. This research explores how the subjective perception of security in the central district of Madrid affects women’s mobility patterns and use of public space. Through a mixed methodology, which combines spatial analysis with sensitive cartographies and collective mapping, it seeks to make visible the conditions of (in)security experienced in the city. The approach adopts a feminist and multi-scalar perspective, ranging from the object to the district scale. The analysis is structured around four layers: mobility, urban environment, green areas and night-time uses. Tools such as Geographic Information Systems were used for the treatment of objective data and qualitative techniques such as interviews and tours accompanied by a set of subjective perceptions. The results show the existence of multiple barriers that condition women’s access to and enjoyment of public space, revealing a discrepancy between what is planned and what is lived. The final considerations anticipate the possibility of replicating the methodology applied in urban planning, proposing future strategies to build safer, more inclusive and sensitive environments to the diversity of their inhabitants. Full article

Vertiports, as the foundational ground infrastructure for Urban Air Mobility (UAM), have garnered increasing scholarly attention in recent years. To examine how the existing literature has reviewed and summarized vertiport-related knowledge, this study conducts a bibliometric analysis of publications (2000–2024) from four major databases, including Web of Science and Scopus, using VOSviewer and CiteSpace. By analyzing co-citation and keyword co-occurrence patterns, the results suggest that vertiport research frontiers are shifting toward facility location, network planning, airspace and scheduling management, scalable infrastructure, and integration with ground transport systems. Scholars and institutions in the United States, China, Europe, and South Korea have taken leading roles in advancing this field, though collaboration among research organizations still requires strengthening. Overall, the findings reveal future research pathways and provide support for the planning and integration of vertiport infrastructure in UAM operations. Full article

Greenhouse automation has become increasingly important in facility agriculture, yet multi-span glass greenhouses pose both scientific and practical challenges for autonomous mobile robots. Scientifically, solid-state LiDAR is vulnerable to glass-induced reflections, sparse geometric features, and narrow vertical fields of view, all of which undermine Simultaneous Localization and Mapping (SLAM)-based localization and mapping. Practically, large-scale crop production demands accurate inter-row navigation and efficient rail switching to reduce labor intensity and ensure stable operations. To address these challenges, this study presents an integrated localization-navigation framework for mobile robots in multi-span glass greenhouses. In the intralogistics area, the LiDAR Inertial Odometry-Simultaneous Localization and Mapping (LIO-SAM) pipeline was enhanced with reflection filtering, adaptive feature-extraction thresholds, and improved loop-closure detection, generating high-fidelity three-dimensional maps that were converted into two-dimensional occupancy grids for A-Star global path planning and Dynamic Window Approach (DWA) local control. In the cultivation area, where rails intersect with internal corridors, YOLOv8n-based rail-center detection combined with a pure-pursuit controller established a vision-servo framework for lateral rail switching and inter-row navigation. Field experiments demonstrated that the optimized mapping reduced the mean relative error by 15%. At a navigation speed of 0.2 m/s, the robot achieved a mean lateral deviation of 4.12 cm and a heading offset of 1.79°, while the vision-servo rail-switching system improved efficiency by 25.2%. These findings confirm the proposed framework’s accuracy, robustness, and practical applicability, providing strong support for intelligent facility-agriculture operations. Full article

Artificial intelligence (AI) often suffers from high energy consumption and complex deployment in resource-constrained environments, leading to a structural mismatch between capability and deployability. This review takes two representative scenarios—energy-first and performance-first—as the main thread, systematically comparing cloud, edge, and fog/cloudlet/mobile edge computing (MEC)/micro data center (MDC) architectures. Based on a standardized literature search and screening process, three categories of miniaturization strategies are distilled: redundancy compression (e.g., pruning, quantization, and distillation), knowledge transfer (e.g., distillation and parameter-efficient fine-tuning), and hardware–software co-design (e.g., neural architecture search (NAS), compiler-level, and operator-level optimization). The purposes of this review are threefold: (1) to unify the “architecture–strategy–implementation pathway” from a system-level perspective; (2) to establish technology–budget mapping with verifiable quantitative indicators; and (3) to summarize representative pathways for energy- and performance-prioritized scenarios, while highlighting current deficiencies in data disclosure and device-side validation. The findings indicate that, compared with single techniques, cross-layer combined optimization better balances accuracy, latency, and power consumption. Therefore, AI miniaturization should be regarded as a proactive method of structural reconfiguration for large-scale deployment. Future efforts should advance cross-scenario empirical validation and standardized benchmarking, while reinforcing hardware–software co-design. Compared with existing reviews that mostly focus on a single dimension, this review proposes a cross-level framework and design checklist, systematizing scattered optimization methods into reusable engineering pathways. Full article

Impact investing and sustainable finance are crucial in addressing social and environmental issues while developing a more resilient, equitable, and sustainable world. The purpose of this article is to analyze, synthesize, and evaluate the existing literature on the impact investing and sustainable finance research domain. Using PRISMA protocol, data was extracted from the Web of Science and Scopus databases, resulting in the compilation of 498 documents. Researchers use Biblioshiny and VOSviewer to analyze the bibliographic meta data. The findings show that the number of publications in this field has increased significantly over the last five years. In terms of journal productivity, Sustainability is the most prominent source, followed by Resources Policy and Journal of Cleaner Production. The results indicate that China published 189 articles, securing the first position, followed by India with 82 articles and the UK with 72 articles. Thematic map analysis underscores the significance of impact investing in renewable energy for sustainable economic growth. In addition, four research themes have emerged from the co-occurrence of keywords analysis. These themes are “sustainable finance for sustainable economic development”; “the rise of ESG investing in the changing world”; “corporate governance and CSR in enhancing firm performance”; and “mobilizing sustainable finance to tackle climate changes”. Furthermore, the research gives a complete summary of current research trends, future research directions and policy recommendations to assist academic researchers, investors, policymakers, business organizations and financial institutions in better understanding the impact investment and sustainable finance. Full article

Coastal boulder deposits (CBDs) are important geomorphic indicators of extreme wave activity, yet integrated morphometric and hydrodynamic analyses remain limited along the Moroccan Atlantic coast. This study characterizes the morphology, spatial distribution, and transport thresholds of supratidal boulders at Oued Cherrat and Mansouria, and quantifies the wave energy required for their mobilization. Between 2021 and 2025, 85 boulders were surveyed, supported by lithological analyses, GPS mapping, and pre-/post-storm photographic documentation. At Oued Cherrat, boulders ranged from 0.01 to 3.56 m³ (≤7.84 t), with solitary blocks located 30–94 m inland and larger imbricated clasts up to 150.5 m. At Mansouria, dimensions reached 22 × 20 × 3.5 m (>2032 t), positioned 5–140 m from the shoreline. Storms in January and March 2025 displaced boulders up to 4.5 m at Oued Cherrat (e.g., 6.39 t) and up to 3 m at Mansouria (e.g., 21.42 t), with new blocks deposited and megaboulders showing slight in situ rotations. Hydrodynamic modelling estimated sliding thresholds of 1.1–4.0 m/s at Oued Cherrat and 2.7–11.0 m/s at Mansouria, while rolling thresholds reached 18.23 m/s. These values confirm the dependence of transport on boulder mass, imbrications, and topography. The findings demonstrate that extreme storms can rapidly reorganize multi-tonne CBDs, while the largest megaboulders require rare, exceptionally high-energy events. Full article