Search Results (61)

Light Detection and Ranging (LiDAR) point clouds are an essential perception modality for artificial intelligence systems like autonomous driving and robotics, where the ubiquity of small objects in real-world scenarios substantially challenges the visual tracking of small targets amidst the vastness of point cloud data. Current methods predominantly focus on developing universal frameworks for general object categories, often sidelining the persistent difficulties associated with small objects. These challenges stem from a scarcity of foreground points and a low tolerance for disturbances. To this end, we propose a deep neural network framework that trains a Siamese network for feature extraction and innovatively incorporates two pivotal modules: the target-awareness prototype mining (TAPM) module and the regional grid subdivision (RGS) module. The TAPM module utilizes the reconstruction mechanism of the masked auto-encoder to distill prototypes within the feature space, thereby enhancing the salience of foreground points and aiding in the precise localization of small objects. To heighten the tolerance of disturbances in feature maps, the RGS module is devised to retrieve detailed features of the search area, capitalizing on Vision Transformer and pixel shuffle technologies. Furthermore, beyond standard experimental configurations, we have meticulously crafted scaling experiments to assess the robustness of various trackers when dealing with small objects. Comprehensive evaluations show our method achieves a mean Success of 64.9% and 60.4% under original and scaled settings, outperforming benchmarks by +3.6% and +5.4%, respectively. Full article

This paper presents a novel Earth-System Stratified Grid (ISEA4H-ESSG) model, designed to address the challenges in multi-layer geoscience data management and analysis. In the realm of geosciences, which encompasses the solid earth, atmosphere, hydrosphere, and biosphere, as well as planetary and space sciences, the effective integration of diverse data sources is crucial. Traditional grids have limitations in three-dimensional spatial modeling, cross-layer data fusion, and dynamic multi-scale analysis. The ISEA4H-ESSG model overcomes these drawbacks by integrating the Icosahedral Snyder Equal-Area Aperture 4 Hexagon Discrete Global Grid System (ISEA4H DGGS) with a degenerative subdivision mechanism. It adheres to six core principles, including stratified spherical coverage, geographic consistency, multi-scale dynamic adaptability, global seamless partitioning, encoding uniqueness and efficiency, and multi-source data compatibility. Through the independent subdivision of spherical and radial layers, this model balances resolution differences and resolves polar-grid distortion and cross-layer data heterogeneity issues. The introduction of a four-dimensional spatiotemporal encoding framework enhances the storage and parallel computing capabilities of massive datasets. Case studies on ionosphere three-dimensional modeling and global atmospheric temperature field formatting demonstrate the high precision and adaptability of the ISEA4H-ESSG model. This research provides a unified spatial data infrastructure for geosciences, facilitating in-depth studies on natural hazards, climate change, and planetary evolution, and offering new perspectives for international partnerships and future Earth-related research. Full article

The effective management of spatiotemporal Earth observation data is a significant challenge due to their growing size and scale, geometric distortion, temporal gaps, and restricted access. In this article, we introduce a novel methodology utilizing a Discrete Global Grid System (DGGS) to address a set of challenges related to spatiotemporal data storage with a live updating mechanism, the multiresolution processing of an arbitrary region of interest (ROI) in real time, and the approximation of missing data in a smooth, continuous manner. We use reverse Chaikin subdivision and B-spline curve fitting to handle temporal data gaps, allowing for real-time updates. Additionally, our work presents a triangular wavelet scheme to incorporate a flexible, tensor-based multiresolution storage scheme for spatiotemporal raster data. The case study we present uses data from the RADARSAT Constellation Mission (RCM) of the Canadian Space Agency (CSA). Our system enables the dynamic retrieval and visualization of time-varying data for a user-defined ROI. The obtained results demonstrate that our method ensures high data fidelity while making spatiotemporal data more accessible across various practical applications in Earth observation. Full article

Despite extensive research on what draws people to urban streets, most existing insights originate from Western contexts, offering limited perspectives from wider urban contexts. This study addresses this gap by examining everyday street activities in Chinese urban villages, focusing specifically on how two spatial scales, the entire street edge and territorial segments, influence necessary, optional, and social engagements. Drawing on video recordings and walk-by observations in two urban villages in Wuhan, China, the research systematically measured the type and duration of activities across 110 territorially defined segments. The findings reveal that territorial segments, i.e., smaller-scale personalised subdivisions at a micro-scale often shaped by bottom–up adaptations, exert a significantly stronger influence upon how people use and linger in street space rather than entire street edges at a macro-scale, which shows only limited impact. This underscores the importance of fine-grained socio-spatial design and local ownership in fostering vibrant people-centred streets. By demonstrating the decisive role of micro-scale features, which span storefront layouts, semi-public alcoves, and adaptive uses, these results carry important implications for urban practitioners seeking to balance top–down redevelopment with bottom–up initiatives. Ultimately, the study enriches the global discourse on street-edge understanding and design, emphasising that territorial segments can be powerful catalysts for promoting activity and community life in dense urban contexts. Full article

This article presents STEAM Architecture, a STEAM project for all educational levels, from pre-school to high school, a project that links the learning of subjects with architectural concepts, thus trying to generate meaningful learning in students. The project is the result of an ERASMUS+ project (DART4City (2020-1-ES01-KA227-SCH-095545) Empowering Arts and creativity for the cities of tomorrow) in which a methodology was developed to extract STEAM projects from European curricula. This methodology has two variants: “forward” and “backward”. The “forward” variant analyzes the curriculum and found the areas of opportunity with more connections among the contents while the “backward” methodology proposes a specific theme to look for the connections. The “backward” variant allows finding a topic that may be of social interest. This is the variant we use in this article. We explore the “backward” methodology in order to find an area of opportunity in society, in particular related to architecture. A questionnaire is distributed to different sectors of people in society to find out whether the learning of different architectural concepts at pre-university levels is interesting. The results of these tests show the potential of a STEAM project related to architecture. The design of the STEAM architecture project shows how the subdivision is carried out from an educational point of view, and also from an architectural point of view. Both worlds agree on dividing space into micro-, meso- and macro-space depending on the scale of what is being treated. For this reason, the STEAM architecture project is subdivided into Room, House, Neighbourhood and City for each educational level: pre-school, primary school and high school (which is 4 years of secondary school (ESO) and the last 2 years of high school). At the end of the article, we show the different workshops that were held in order to analyze the goodness of the proposal. Full article

Customizing and optimizing lattice materials poses a challenge to designers. This study proposed a data-driven generative method to customize and optimize lattice material. The method utilizes subdivision modeling to parametrically describe lattice morphologies and skeletons. Next, the homogenization method is employed to analyze elastic moduli for collecting a dataset. Then, a two-tiered machine learning (ML) framework is proposed to predict the elastic modulus for a forward design. The first-tier model employs polynomial regression to estimate relative density, which serves as an additional input feature for the second-tier model. The prediction accuracy of the second-tier model is improved through the additional inputs. The forward and reverse design strategies offer a flexible and accurate means of tailoring lattice properties to meet specific performance requirements. Two case studies demonstrate the practical value of the framework: customizing a lattice material to achieve a desired elastic modulus and optimizing the mechanical performance of lattice materials under relative density constraints. The results show that the prediction accuracy of the elastic modulus using the two-tiered ML model achieved an error of less than 10% compared to finite element analysis, demonstrating the reliability of the proposed approach. Furthermore, the optimization design achieved up to a 25% improvement in mechanical performance compared to conventional lattice configurations under the same relative density constraints. These findings underscore the advantages of combining generative design, machine learning, and genetic algorithms to navigate complex design spaces and achieve enhanced material performance. Full article

Valence bond theory (VB) was used to determine the extent and driving forces for covalent vs. dative bonding in 10-valence-electron diatomic molecules N₂, CO, NO⁺, CN⁻, P₂, SiS, PS⁺, and SiP⁻. VBSCF calculations were performed at the CCSD(T)/cc-pVDZ optimized geometries. The full triply bonded system included 20 VB structures. A separation of the σ and π space allowed for a subdivision of the full 20 structure set into sets of 8 and 3 for the π and σ systems, respectively. The smaller structure sets allowed for a more focused look at each type of bond. In situ bond energies for σ bonds, individual π bonds, the π system, and triple bonds follow expected trends. Our data shows that N₂ and P₂ have three covalent bonds whereas CO and SiS contain two covalent and one dative bond, and charged species NO⁺, CN⁻, PS⁺, and SiP⁻ are a mixture of N₂ and CO type electronic arrangements, resulting in a nearly equal charge distribution. Dative bonds prefer to be in the π position due to enhanced σ covalency and π resonance. Both σ and π resonance energies depend on a balance of ionic strength, orbital compactness, σ constraints, and bond directionality. Resonance energy is a major contributor to bond strength, making up more than 50% of the π bonds in SiS and PS⁺ (charge-shift bonds), and is greater than charge transfer in dative bonds. Full article

Seed orchards are important seed resources for producing improved tree crops for future plantations, forest restoration, and forestry practices (i.e., gene conservation) and for transmitting current gene diversity to future generations. Seed orchards are a major sub-division in forest science. The establishment and management of a seed orchard involves many steps, from the selection of superior trees to the harvesting of a seed crop. Studying the trends and future directions of seed orchards using different analysis methods is critically important, especially to establish resistant forests via the production of climate-smart, biotic/abiotic-stress-resistant seedling materials. Published papers related to seed orchards should be analyzed to determine the current trends in this field and to contribute to its future directions. Bibliometric analysis has been used for different purposes in various scientific fields. However, it has not been performed for publications in seed orchards. This study was carried out to analyze the current trends of research on seed orchards and to determine the future directions of these orchards based on published papers. For these purposes, 1018 published papers were obtained from the Science Citation Index, Science Citation Index Expanded, and citation index databases of “Web of Science” using the keyword “seed orchard”. The papers were published between 1980 and 2022 and were subjected to bibliometric analysis based on the most prolific contributors, references, countries, and keywords. CiteSpace software 6.1 R6 was applied to visualize information about seed orchard research. The average number of citations per publication was 13.05, and the 4 H-Index of the publication set was 48. The most prolific contributors with the strongest citation bursts, the highest centrality, and the greatest numbers of published papers were from Canada, Sweden, South Korea, Finland, and Czech Republic, while Canada (186 published articles), the USA (140), and Sweden (115), together with China, Brazil, and Germany, were active countries, especially based on citations from recent years. The “keywords” of the papers were the core of the research. “Mating pattern”, “Swedish forestry”, “fertility variation”, “Hymenoscyphus fraxineus”, “threatened Pacific sandalwood”, “outbreeding depression”, “climate change”, “management”, and “growth”, together with others such as “genetic improvement” and “effective size”, were active study areas and keywords, based on results of the analysis. They also guided the literature search and inventory and classification of early studies and served as predictors for future studies. The results of this study are discussed based on the trends and future directions of the research and development of seed orchards. Full article

The conformation and dynamics of metropolitanisation act as propulsive elements of territorial transformations. The deficiency of infrastructural equipment, the heterogeneity of urban fabric and the lack of services and public spaces contribute to severing the identity ties between settled communities and territories. In light of this, within the more general reflection concerning urban regeneration, we recall the role that cultural heritage plays in the physical and functional organisation of the city, as a reflection of the interaction between community and context. The contribution is contextualized in the research work on the activities related to Thematic Line 4 of the Extended Partnership 5—CHANGES (NRRP). The thematic line activities follow three phases: 1. contextualization; 2. operational phase; 3. experimentation. The research work presented here is part of the ‘operational phase’, to identify strategies and projects for heritage-led regeneration. The article analyses the pilot case of CSOA Forte Prenestino as a starting point for thinking about expanding the research activity to other similar cases. It is a self-managed community centre in Rome (Italy) located in the nineteenth-century Forte, which has become a symbol of collective identity. The case study was identified following three levels of investigation: 1. identification of the municipality, first-level administrative subdivision of the city of Rome (Italy); 2. identification of the main historical, archaeological and architectural emergencies of the municipality; 3. identification of an asset to be analysed as “Heritage by designation” (involvement of experts) and “Heritage by appropriation” (involvement of communities). The research results show the “Recognition Path” of Forte Prenestina: according to what has been ‘designated’ by urban planning instruments, project instruments, legislative instruments and authors of scientific publications and conferences; and on the basis of the bottom-up ‘appropriation’ process of the asset that has allowed its management, assessment of its cultural and social potential and its development. The research results allow us to reflect on heritage-led urban regeneration as a strategy capable of capturing and promoting the links between social integration and cultural–historical identity. Full article

Research on sustainable, affordable housing is evolving. Yet, its conceptual efficacy in light of the changing needs of today’s cities and targeted low-to-middle-income households remains unknown. In today’s rapidly urbanizing world, understanding the conceptual relevance and importance of land use planning tools such as vertical subdivision to the delivery of sustainable housing is tenable. In response to this knowledge gap, this entry inquires, how can the delivery of affordable housing be configured in a manner that leverages the potential of a redefined vertical subdivision development to optimize densities and ensure that housing affordability is sustainable? Here, this entry re-defines vertical subdivision development as a housing planning and design tool that allows for the segregation of air spaces into individual volumetric land parcels that mimic the environmental features of the land-on-ground, such that housing construction within such volumetric spaces is a function of the contextually relevant needs of occupants. This entry demonstrates a paradigm shift from existing housing infrastructure planning models and narratives to one that responds to and addresses all three dimensions of sustainability: economic (sustainable affordability), environmental (sustainable densities), and social (occupant wellness) in the housing infrastructure planning and delivery process. Full article

Possibility and probability are the two aspects of uncertainty, where uncertainty represents the ignorance of a given individual. The notion of alternative (or event) belongs to the domain of possibility. An event is intrinsically subdivisible and a quadratic metric, whose value is intrinsic or invariant, is used to study it. By subdividing the notion of alternative, a joint (bivariate) distribution of mass appears. The mathematical expectation of X is proved to be invariant using joint distributions of mass. The same is true for $X_{12}$ and $X_{12\dots m}$. This paper describes the notion of $\alpha$-product, which refers to joint distributions of mass, as a way to connect the concept of probability with multilinear matters that can be treated through statistical inference. This multilinear approach is a meaningful innovation with regard to the current literature. Linear spaces over $\mathbb{R}$ with a different dimension can be used as elements of probability spaces. In this study, a more general expression for a measure of variability referred to a single random quantity is obtained. This multilinear measure is obtained using different joint distributions of mass, which are all considered together. Full article

Three-dimensional (3D) indoor models are a crucial component to simulate pedestrian evacuations realistically in indoor environments. However, existing 3D indoor models cannot fully represent realistic indoor environments to enable the simulation of 3D pedestrian motions in evacuations because spaces above/below some physical components (e.g., desks, chairs) have been largely overlooked. Thus, this paper introduces a conceptual space model to advance a space identification and classification scheme that can fully capture 3D pedestrian motions. This paper first proposes the definition and parameterisation of different 3D pedestrian motions. Then, the definition and specifications of three categories of space components are elaborated on based on the motions. Finally, a voxel-based approach is introduced to identify and classify the space components, which are demonstrated by an illustrative example. This work contributes to advancing 3D indoor modelling to enable a more realistic simulation of 3D pedestrian motions. Full article

Skylights are an efficient means of daylighting in exhibition spaces, but their design presents significant challenges. Considering that daylight utilization profoundly impacts both the visual and thermal environments while affecting energy consumption, the early application of multi-objective optimization strategies becomes imperative. However, many optimization studies provide numerical references only, without delving into the characteristics of opening distribution. This study introduces an optimized exploration approach for openings based on grid subdivision and material parameter selection, targeting Useful Daylight Illuminance (UDI), Energy Use Intensity (EUI), and Predicted Percentage of Dissatisfied (PPD). Simulations and optimizations were performed using Honeybee and Octopus, focusing on the optimal configurations of four typical skylights in Fukuoka, Japan’s climate. The results demonstrate that this novel optimization approach improves metrics for each case and challenges traditional perceptions of daylight systems. Flexible and diverse opening configurations, formed through irregular layouts and material combinations, help achieve more ideal holistic environmental effects under different climatic conditions. Thus, we should provide these research findings as design guidelines for similar scenarios. Full article

Major Depressive Disorder (MDD) and Bipolar Disorder (BD) are the most frequent mental disorders whose indeterminate etiopathogenesis spurs to explore new aetiologic scenarios. In light of the neuropsychiatric symptoms characterizing Cerebellar Cognitive Affective Syndrome (CCAS), the objective of this narrative review is to analyze the involvement of the cerebellum (Cbm) in the onset of these conditions. It aims at detecting the repercussions of the Cbm activities on mood disorders based on its functional subdivision in vestibulocerebellum (vCbm), pontocerebellum (pCbm) and spinocerebellum (sCbm). Despite the Cbm having been, for decades, associated with somato-motor functions, the described intercellular pathways, without forgiving the molecular impairment and the alteration in the volumetric relationships, make the Cbm a new important therapeutic target for MDD and BD. Given that numerous studies have showed its activation during mnestic activities and socio-emotional events, this review highlights in the Cbm, in which the altered external space perception (vCbm) is strictly linked to the cognitive-limbic Cbm (pCbm and sCbm), a crucial role in the MDD and BD pathogenesis. Finally, by the analysis of the cerebellar activity, this study aims at underlying not only the Cbm involvement in affective disorders, but also its role in social relationship building. Full article

With the rapid development of the big data era, Unmanned Aerial Vehicles (UAVs) are being increasingly adopted for various complex environments. This has imposed new requirements for UAV path planning. How to efficiently organize, manage, and express all kinds of data in complex scenes and intelligently carry out fast and efficient path planning for UAVs are new challenges brought about by UAV application requirements. However, traditional path-planning methods lack the ability to effectively integrate and organize multivariate data in dynamic and complicated airspace environments. To address these challenges, this paper leverages the theory of the three-dimensional subdivision of earth space and proposes a novel environment-modeling approach based on airspace grids. In this approach, we carried out the grid-based modeling and storage of the UAV flight airspace environment and built a stable and intelligent deep-reinforcement-learning grid model to solve the problem of the passage cost of UAV path planning in the real world. Finally, we designed multiple sets of experiments to verify the efficiency of the global subdivision coding system as an environmental organization framework for path planning compared to a longitude–latitude system and to demonstrate the superiority of the improved deep-reinforcement-learning model in specific scenarios. Full article