Search Results (3,386)

This paper introduces the overall design plan, development timeline, and preliminary progress of the Autonomous Pit Exploration System project. This project aims to develop an advanced multi-robot system for the efficient inspection of nuclear waste-storage tank pits. The project is structured into three phases: Phase 1 involves data collection and interface definition in collaboration with Hanford Site experts and university partners, focusing on tank riser geometry and hardware solutions. Phase 2 includes the selection of sensors and robot components, detailed mechanical design, and prototyping. Phase 3 integrates all components into a cohesive system managed by a master control package which also incorporates digital twin and surrogate models, and culminates in comprehensive testing and validation at a simulated tank pit at the Idaho National Laboratory. Additionally, the system’s communication design ensures coordinated operation through shared data, power, and control signals. For transportation and deployment, an electric vehicle (EV) is chosen to support the system for a full 10 h shift with better regulatory compliance for field deployment. A telescopic arm design is selected for its simple configuration and superior reach capability and controllability. Preliminary testing utilizes an educational robot to demonstrate the feasibility of splitting computational tasks between edge and cloud computers. Successful simultaneous localization and mapping (SLAM) tasks validate our distributed computing approach. More design considerations are also discussed, including radiation hardness assurance, SLAM performance, software transferability, and digital twinning strategies. Full article

Satellite-based target positioning is vital for applications like disaster relief and precision mapping. Practically, satellite errors, e.g., thermal deformation and attitude errors, lead to a mix of fixed and random errors in the measured line-of-sight angles, resulting in a decline in target-positioning accuracy. Motivated by this concern, this study introduces a systematic error self-correction target-positioning method under continuous observations using a single video satellite. After analyzing error sources and establishing an error-inclusive positioning model, we formulate a dimension-extended equation estimating both target position and fixed biases. Based on the equation, a projection transformation method is proposed to obtain the linearized estimation of unknown parameters first, and an iterative optimization method is then utilized to further refine the estimate. Compared with state-of-the-art algorithms, the proposed method can improve positioning accuracy by 98.70% in simulation scenarios with large fixed errors. Thus, the simulation and actual data calculation results demonstrate that, compared with state-of-the-art algorithms, the proposed algorithm effectively improves the target-positioning accuracy under non-ideal error conditions. Full article

Brain tumors elicit complex neuropsychiatric disturbances that frequently occur prior to radiological detection and hinder differentiation from major psychiatric disorders. These syndromes stem from tumor-dependent metabolic reprogramming, neuroimmune activation, neurotransmitter dysregulation, and large-scale circuit disruption. Dinucleotide hypermethylation (e.g., IDH-mutant gliomas), through the accumulation of 2-hydroxyglutarate (2-HG), execute broad DNA and histone hypermethylation, hypermethylating serotonergic and glutamatergic pathways, and contributing to a treatment-resistant cognitive-affective syndrome. High-grade gliomas promote glutamate excitotoxicity via system Xc⁻ transporter upregulation that contributes to cognitive and affective instability. Cytokine cascades induced by tumors (e.g., IL-6, TNF-α, IFN-γ) lead to the breakdown of the blood–brain barrier (BBB), which is thought to amplify neuroinflammatory processes similar to those seen in schizophrenia spectrum disorders and autoimmune encephalopathies. Frontal gliomas present with apathy and disinhibition, and temporal tumors lead to hallucinations, emotional lability, and episodic memory dysfunction. Tumor-associated neuropsychiatric dysfunction, despite increasing recognition, is underdiagnosed and commonly misdiagnosed. This paper seeks to consolidate the mechanistic understanding of these syndromes, drawing on perspectives from neuroimaging, molecular oncology, neuroimmunology, and computational psychiatry. Novel approaches, including lesion-network mapping, exosomal biomarkers or AI-based predictive modeling, have projected early detection and precision-targeted interventions. In the context of the limitations of conventional psychotropic treatments, mechanistically informed therapies, including neuromodulation, neuroimmune-based interventions, and metabolic reprogramming, are essential to improving psychiatric and oncological outcomes. Paraneoplastic neuropsychiatric syndromes are not due to a secondary effect, rather, they are manifestations integral to the biology of a tumor, so they require a new paradigm in both diagnosis and treatment. And defining their molecular and circuit-level underpinnings will propel the next frontier of precision psychiatry in neuro-oncology, cementing the understanding that psychiatric dysfunction is a core influencer of survival, resilience, and quality of life. Full article

Methane (CH₄) emissions from the wetlands of the Tibetan Plateau (TP) remain poorly quantified, particularly regarding their historical dynamics, spatial heterogeneity, and response to climate change. This study provides the high-resolution, observation-driven reconstruction of TP wetland CH₄ emissions over the past four decades, integrating a machine learning model with 108 flux measurements from 67 sites. This unique combination of field-based data and fine-scale mapping enables unprecedented accuracy in quantifying both emission intensity and long-term trends. We show that current TP wetlands emit 5.87 ± 1.43 g CH₄ m⁻² yr⁻¹, totaling 97.3 Gg CH₄ yr⁻¹, equivalent to 7.8% of East Asia’s annual wetland emissions. Despite a climate-driven increase in per-unit-area CH₄ fluxes, a 19.8% (8432.9 km²) loss of wetland area since the 1980s has reduced total emissions by 15%, counteracting the enhancement from warming and moisture increases. Our comparative analysis demonstrates that existing land surface models (LSMs) substantially underestimate TP wetland CH₄ emissions, largely due to the inadequate representation of TP wetlands and their dynamics. Projections under future climate scenarios indicate a potential 8.5–21.2% increase in emissions by 2100, underscoring the importance of integrating high-quality, region-specific observational datasets into Earth system models. By bridging the gap between field observations and large-scale modeling, this work advances understanding of alpine wetland–climate feedback, and provides a robust foundation for improving regional carbon budget assessments in one of the most climate-sensitive regions on Earth. Full article

Spectroscopic observations of Earth-like exoplanets and ultra-faint galaxies–top scientific priorities for the coming decades–involve measuring broadband signals at rates of only a few photons per square meter per hour. This imposes exceptional requirements on the detector performance, necessitating dark currents below 1 e⁻/pixel/kilo second, read noise under 1 e⁻/pixel/frame, and the ability to handle large-format arrays–capabilities that are not yet met by most existing infrared detectors. In addition, spaceborne LiDAR systems require photodetectors with exceptional sensitivity, compact size, low power consumption, and multi-channel capability to facilitate long-range range finding, topographic mapping, and active spectroscopy without increasing the instrument burden. MCT Avalanche photodiodes arrays offer high internal gain, pixelation, and photon-counting performance across SW to MW wavelengths needed for multi-beam and multi-wavelength measurements, marking them as a critical enabling technology for next-generation planetary and Earth science LiDAR missions. This work reports the latest progress in developing Hg_1−xCd_xTe linear-mode e-APDs at premier industrial research institutions, including relevant experimental data, simulations and major project planning. Related studies are summarized to demonstrate the practical and iterative approach for device fabrication, which have a transformative impact on the evolution of this discipline. Full article

The Euphrates–Tigris Basin is experiencing significant environmental transformations due to climate change, Land Use and Land Cover Change (LULCC), and anthropogenic pressures. This study employs Earth Map, an open-access remote sensing platform, to comprehensively assess climate trends, vegetation dynamics, water resource variability, and land degradation across the basin. Key findings reveal a geographic shift toward aridity, with declining precipitation in high-altitude headwater regions and rising temperatures exacerbating water scarcity. While cropland expansion and localized improvements in land productivity were observed, large areas—particularly in hyperarid and steppe zones—show early signs of degradation, increasing the risk of dust source expansion. LULCC analysis highlights substantial wetland loss, irreversible urban growth, and agricultural encroachment into fragile ecosystems, with Iraq experiencing the most pronounced transformations. Climate projections under the SSP245 and SSP585 scenarios indicate intensified warming and aridity, threatening hydrological stability. This study underscores the urgent need for integrated water management, Land Degradation Neutrality (LDN), and climate-resilient policies to safeguard the basin’s ecological and socioeconomic resilience. Earth Map is a vital tool for monitoring environmental changes, offering rapid insights for policymakers and stakeholders in this data-scarce region. Future research should include higher-resolution datasets and localized socioeconomic data to improve adaptive strategies. Full article

The construction environment is a highly dynamic and complex system, presenting challenges for accurately identifying and managing dynamic resources in digital twin-based scenes. This study aims to address the problem of object coordinate distortion caused by camera image deformation, which often reduces the fidelity of dynamic object mapping in digital construction monitoring. A novel dynamic object mapping generation method is proposed to enhance precision and synchronization of dynamic objects within a digital twin environment. The approach integrates internal and external camera parameters, including spatial position, field of view (FOV), and camera pose, into BIM using Dynamo, thereby creating a virtual camera aligned with the physical one. The YOLOv11 algorithm is employed to recognize dynamic objects in real-time camera footage, and corresponding object families are generated in the BIM model. Using perspective projection combined with a linear regression model, the system computes and updates accurate coordinate positions of the dynamic objects, which are then fed back into the camera view to achieve real-time mapping. Experimental validation demonstrates that the proposed method significantly reduces mapping errors induced by lens distortion and provides accurate spatial data, supporting improved dynamic resource perception and intelligent management in digital twin construction environments. Full article

In the Mexican Caribbean, the demand for tourism services led to the expansion of the hotel industry from the coast inland. This caused rural and urban communities in the region to become involved in tourism activities, initiating the formulation of an international model of sustainable development with a focus on cultural tourism. Considering the tourism potential that the study area can offer to nearby rural communities, as well as the limited number of studies aimed at estimating tourism carrying capacity (see examples of TCC for environmental management units in communal land areas like Baja California, Mexico and the Huagapo cave in Peru), the present research aims at estimating the tourism carrying capacity in the southern region of the Mexican Caribbean. A mixed methodological approach was adopted for the present study entailing a detailed description of flora and fauna in the study area using natural resource mapping tools, social diagnosis of the communities in the study area using the Participatory Action Research (PAR) technique in the communities of Caobas and San José de la Montaña and the estimation of tourism carrying capacity (TCC), Physical Carrying Capacity (PCC), Real Carrying Capacity (RCC), and Effective Carrying Capacity (ECC) using information gathered through fieldwork and bibliographic review. It was found that the area can support a tourism carrying capacity of 538.33 visits per day. In this initial assessment, it was estimated that the implementation of an ecotourism project in a rural community would not alter its environmental conditions. The estimated indicators provide appropriate tools for designing and planning long-term sustainable tourism proposals. Moreover, they integrate environmental, economic, and social aspects in a balanced manner, generating tangible and lasting benefits. Full article

As an important representation form of three-dimensional scenes, the point cloud contains rich geometry and attribute information. The video-based point cloud compression standard (V-PCC) divides and projects three-dimensional data directionally onto a two-dimensional plane. The generated geometric and attribute graphs contain occupied pixels obtained by projection and unoccupied pixels used for smooth filling. Among them, the non-occupied pixels have no practical effect on the reconstructed point cloud. However, in the process of encoding bitrate allocation, V-PCC still uses the original bitrate control method, resulting in insufficient bitrate utilization efficiency. To this end, this paper proposes a method for optimizing the unoccupied pixels of point cloud videos based on V-PCC and jointly controlling the coding rate of geometries and attribute graphs. For geometric graphs, this paper improves the allocation of bitrate weights based on whether the encoded blocks contain non-occupied pixels and the proportion of occupied pixels, and stops allocating bitrates to encoded blocks that are all non-occupied pixels. For the attribute graph, the input pixel improvement algorithm is designed by using the occupation map, and the invalid unoccupied pixel information is cavitation. Experiments show that under the All Intra configuration, compared with the original scheme, this method reduces the Geom.BD-GeomRate by an average of 15.67% and 16.68%, respectively, in the point-to-point D1 and point-to-face D2 metrics. The end-to-end BD-AttrRate is reduced by an average of 4.38%, 0.68%, and 1.74%, respectively. Overall, the average savings are 29.88%, 31.50%, 5.50%, 2.66%, and 3.34%, respectively, achieving bitrate optimization and effectively controlling encoding loss. Full article

The Tibetan Plateau’s lakes, serving as critical water towers for over two billion people, exhibit divergent responses to climate change that remain poorly quantified. This study develops a deep learning framework integrating Synthetic Aperture Radar (SAR) altimetry from Sentinel-3A with bias-corrected CMIP6 (Coupled Model Intercomparison Project Phase 6) climate projections under Shared Socioeconomic Pathways (SSP) scenarios (SSP2-4.5 and SSP5-8.5, adjusted via quantile mapping) to predict lake-level changes across eight Tibetan Plateau (TP) lakes. Using a Feed-Forward Neural Network (FFNN) optimized via Bayesian optimization using the Optuna framework, we achieve robust water level projections (mean validation R² = 0.861) and attribute drivers through Shapley Additive exPlanations (SHAP) analysis. Results reveal a stark north–south divergence: glacier-fed northern lakes like Migriggyangzham will rise by 13.18 ± 0.56 m under SSP5-8.5 due to meltwater inputs (temperature SHAP value = 0.41), consistent with the early (melt-dominated) phase of the IPCC’s ‘peak water’ framework. In comparison, evaporation-dominated southern lakes such as Langacuo face irreversible desiccation (−4.96 ± 0.68 m by 2100) as evaporative demand surpasses precipitation gains. Transitional western lakes exhibit “peak water” inflection points (e.g., Lumajang Dong’s 2060 maximum) signaling cryospheric buffer loss. These projections, validated through rigorous quantile mapping and rolling-window cross-validation, provide the first process-aware assessment of TP Lake vulnerabilities, informing adaptation strategies under the Sustainable Development Goals (SDGs) for water security (SDG 6) and climate action (SDG 13). The methodological framework establishes a transferable paradigm for monitoring high-altitude freshwater systems globally. Full article

Geospatial sensing and data-driven technologies (GSDDTs) are playing an increasingly important role in transforming (agro)forestry practices across the Western Balkans 6 region (WB6). This review critically examines the current state of GSDDT application in six WB countries (also known as the WB6 group)—Albania, Bosnia and Herzegovina, Kosovo*, Montenegro, North Macedonia, and Serbia—with a focus on their contributions to sustainable (agro)forest management. The analysis explores the use of unmanned aerial vehicles (UAVs), light detection and ranging (LiDAR), geographic information systems (GIS), and satellite imagery in (agro)forest monitoring, biodiversity assessment, landscape restoration, and the promotion of circular economy models. Drawing on 25 identified case studies across WB6—for example, ALFIS, Forest Beyond Borders, ForestConnect, Kuklica Geosite Survey, CREDIT Vibes, and Project O₂ (including drone-assisted reforestation in Kosovo*)—this review highlights both technological advancements and systemic limitations. Key barriers to effective GSDDT deployment across WB6 in the (agro)forestry sector and its cross-border cooperation initiatives include fragmented legal frameworks, limited technical expertise, weak institutional coordination, and reliance on short-term donor funding. In addition to mapping current practices, this paper offers a comparative overview of UAV regulations across the WB6 region and identifies six major challenges influencing the adoption and scaling of GSDDTs. To address these, it proposes targeted policy interventions, such as establishing national LiDAR inventories, harmonizing UAV legislation, developing national GSDDT strategies, and creating dedicated GSDDT units within forestry agencies. This review also underscores how GSDDTs contribute to compliance with seven European Union (EU) acquis chapters, how they support eight Sustainable Development Goals (SDGs) and their sixteen targets, and how they advance several EU Green Agenda objectives. Strengthening institutional capacities, promoting legal alignment, and enabling cross-border data interoperability are essential for integrating GSDDTs into national (agro)forest policies and research agendas. This review underscores GSDDTs’ untapped potential in forest genetic monitoring and landscape restoration, advocating for their institutional integration as catalysts for evidence-based policy and ecological resilience in WB6 (agro)forestry systems. Full article

Despite a global trend toward socially engaged higher education, architectural pedagogy continues to grapple for a coherent approach that systematically and genuinely integrates socio-cultural dimensions into design studio teaching practices. Defined as the interwoven social, cultural, and political factors that shape the built environment, the socius is treated peripherally within architectural pedagogy, limiting students’ capacity to develop civic agency, spatial justice awareness, and critical reflexivity in navigating complex societal conditions. This article argues for a socius-centric reorientation of architectural pedagogy, postulating that socially engaged studio models, which include Community Design, Design–Build, and Live Project, must be conceptually integrated to fully harness their pedagogical merits. The article adopts two lines of inquiry: first, mapping the theoretical underpinnings of the socius across award-winning pedagogical innovations and Google Scholar citation patterns; and second, defining the core attributes of socially engaged pedagogical models through a bibliometric analysis of 87 seminal publications. Synthesising the outcomes of these inquiries, the study offers an advanced articulation of studio learning as a process of social construction, where architectural knowledge is co-produced through role exchange, iterative feedback, interdisciplinary dialogue, and emergent agency. Conclusions are drawn to offer pragmatic and theoretically grounded pathways to reshape studio learning as a site of civic transformation. Full article

The construction industry, a cornerstone of global economic growth, continues to struggle with entrenched inefficiencies, including low productivity, cost overruns, and fragmented project delivery. Addressing these persistent challenges requires more than incremental improvements, it demands a strategic unification of Lean Construction, Building Information Modeling (BIM), and Emerging Technologies. This systematic review synthesizes evidence from 64 academic studies to identify the most influential tools, techniques, and methodologies across these domains, revealing both their individual strengths and untapped synergies. The analysis highlights widely adopted Lean practices such as the Last Planner System (LPS) and Just-In-Time (JIT); BIM capabilities across 3D, 4D, 5D, 6D, and 7D dimensions; and a spectrum of digital innovations including Digital Twins, AR/VR/MR, AI, IoT, robotics, and blockchain. Crucially, the review demonstrates that despite rapid advancements, integration remains sporadic and unstructured, representing a critical research and industry gap. By moving beyond descriptive mapping, this study establishes an essential foundation for the development of robust, adaptable integration frameworks capable of bridging theory and practice. Such frameworks are urgently needed to optimize efficiency, enhance sustainability, and enable innovation in large-scale and complex construction projects, positioning this work as both a scholarly contribution and a practical roadmap for future research and implementation. Full article

Cross-modal semantic segmentation of individual tree LiDAR point clouds is critical for accurately characterizing tree attributes, quantifying ecological interactions, and estimating carbon storage. However, in forest environments, this task faces key challenges such as high annotation costs and poor cross-domain generalization. To address these issues, this study proposes a cross-modal semantic transfer framework tailored for individual tree point cloud segmentation in forested scenes. Leveraging co-registered UAV-acquired RGB imagery and LiDAR data, we construct a technical pipeline of “2D semantic inference—3D spatial mapping—cross-modal fusion” to enable annotation-free semantic parsing of 3D individual trees. Specifically, we first introduce a novel Multi-Source Feature Fusion Network (MSFFNet) to achieve accurate instance-level segmentation of individual trees in the 2D image domain. Subsequently, we develop a hierarchical two-stage registration strategy to effectively align dense matched point clouds (MPC) generated from UAV imagery with LiDAR point clouds. On this basis, we propose a probabilistic cross-modal semantic transfer model that builds a semantic probability field through multi-view projection and the expectation–maximization algorithm. By integrating geometric features and semantic confidence, the model establishes semantic correspondences between 2D pixels and 3D points, thereby achieving spatially consistent semantic label mapping. This facilitates the transfer of semantic annotations from the 2D image domain to the 3D point cloud domain. The proposed method is evaluated on two forest datasets. The results demonstrate that the proposed individual tree instance segmentation approach achieves the highest performance, with an IoU of 87.60%, compared to state-of-the-art methods such as Mask R-CNN, SOLOV2, and Mask2Former. Furthermore, the cross-modal semantic label transfer framework significantly outperforms existing mainstream methods in individual tree point cloud semantic segmentation across complex forest scenarios. Full article

Debris flows are rapid mass movements of water-laden debris that flow down mountainsides into valley channels and eventually settle on valley floors. The risk of debris flows can be significantly increased after wildfires. Following the destructive 2021 debris flows in Glenwood Canyon, the Colorado Geological Survey (CGS) initiated a LiDAR-Based Alluvial Fan Mapping Project to improve geologic hazard delineation of alluvial and high-angle fans in response to developing wildfire-ready watersheds. These landforms, shaped by episodic sediment-laden flows, pose significant risks and are often misrepresented on conventional geologic maps. CGS delineated fan-shaped landforms with improved precision using 1-m resolution LiDAR-based DEMs, DEM-derived terrain metrics, hydrologic analysis, and geospatial analysis tools within the ArcGIS Pro platform. Our results reveal previously unmapped or misclassified alluvial or high-angle fans in areas undergoing increasing development pressure, where low-gradient terrain indicates a high hazard potential. Through this study, over 3200 alluvial and high-angle fan polygons were delineated across six Colorado counties, encompassing approximately 81 km² of alluvial fans and 54 km² of high-angle fans. High-resolution LiDAR data, geospatial analytical techniques, and systematic QA/QC protocols were used to support refined hazard awareness. The resulting dataset enhances proactive land-use planning and wildfire resilience by identifying areas prone to debris flow and flood hazards. These maps are intended for regional screening and planning purposes and are not intended for site-specific design. These maps also serve as a critical resource for prioritizing geologic evaluations and guiding mitigation planning across Colorado’s wildfire-affected landscapes. Full article