Search Results (2,213)

Recent transformations of utopia as a form can be followed from modernist totalizing grand narratives that depicted new socio-spatial orderings to its fragmentation, pluralization, and critical turn in the second half of the 20th century. But if we think about utopia as a critical form in our contemporary context, we often encounter it being perceived either as a pejorative term for a concept too outlandish and impossible to even be considered, or as a term used in conjunction with large-scale ideological projects which hold little regard for their socio-spatial context. Refusing to concede that utopia as a critical form has lost its relevance within the architectural discipline, the paper asks how contemporary utopian production could be identified, mapped, and interpreted after the fragmentation of modernist grand narratives. To that aim, the paper develops a three-axis analytical framework which observes contemporary forms of utopian architectural production. Viewing utopia not as a prescriptive image of an ideal future, but as a critical apparatus aimed at projection and inquiry, the framework maps utopian production according to its position between the possible and the impossible, the critical and the affirmative, and the uncovering and the projective. Building on the positions and relationships revealed through the structured three-axis framework, the paper constructs a typology of four ideal-typical protagonists: the Critical Thinker, the Speculative Designer, the Architect, and the Developer, demonstrating that contemporary utopian thought has not disappeared, but has dispersed across different forms of theory, speculative design, practice, and spatial production. Identifying through the four protagonists the potential of utopia not as a representational or prescriptive form, but rather as an operative strategy and a method of inquiry, the paper offers both a conceptual tool for analyzing architecture’s contemporary engagement with utopia as a critical method, and demonstrates how utopian thinking operates as critique, intervention, ideological projection, and a speculative scenario building within our fragmented and individualized contemporary condition. Full article

This paper presents a non-learning-based, seed-dependent, semi-automatic pedestrian candidate generation pipeline for LiDAR point clouds. The proposed method is designed to support 3D annotation workflows by reducing irrelevant candidate clusters while improving the reliability of pedestrian candidate selection under distance-dependent point sparsity. The pipeline integrates distance-aware DBSCAN clustering, Single Template Matching (STM), and Centralized Point Augmentation (CPA). First, LiDAR points within the camera field of view are preprocessed, and pedestrian candidate clusters are generated using DBSCAN parameters configured according to distance intervals. Ground-snapping-based bounding-box refinement and height-based filtering are then applied to improve geometric consistency and reduce non-pedestrian candidates. In the second stage, STM compares PCA-aligned projected silhouettes of candidate clusters with a seed pedestrian template to suppress false positives. To address silhouette instability caused by sparse mid-range pedestrian points, CPA adds centroid-contracted points in the projection-relevant plane before template matching. Experiments on pedestrian-containing frames from the KITTI dataset show that STM improves precision from 27.6% to 60.5% and increases the F1-score from 36.8% to 51.4% compared with the initial DBSCAN-based candidate generation stage. The final CPA configuration improves recall from 44.7% to 46.7% and the overall F1-score from 51.4% to 52.1%, while revealing a precision–recall trade-off. Supplementary IoU analysis shows that the final DBSCAN–STM–CPA configuration maintains meaningful spatial overlap with pedestrian ground-truth boxes, achieving 88.9% at 3D IoU ≥ 0.10 and 81.6% at BEV IoU ≥ 0.25. Runtime analysis further shows that height-based filtering reduces the average per-frame processing time from 151.5 ms to 125.1 ms, while the final CPA configuration introduces only a small overhead, resulting in 126.2 ms per frame. These results demonstrate that the proposed DBSCAN–STM–CPA pipeline can provide reliable pedestrian candidates for semi-automatic 3D labeling without requiring class-specific detector training. Full article

Fringe projection profilometry (FPP) is widely used for 3D reconstruction, but conventional single-view FPP systems suffer from inherent occlusions and shadow regions, leading to incomplete surface recovery. In this study, we propose CVA-Net, an end-to-end deep learning framework with cross-view attention (CVA) that directly reconstructs dense depth maps from multi-view fringe patterns. CVA-Net simultaneously processes four fringe images acquired from orthogonal projection directions and leverages a CVA module to explicitly model inter-view dependencies, enabling adaptive fusion of complementary information. A 3D U-Net backbone with attention gates, atrous spatial pyramid pooling (ASPP), and an auxiliary parameter estimation branch further enhances reconstruction accuracy and structural consistency via multitask learning. To support Sim2Real network training, we build a Blender-based digital twin of a multi-view FPP system and generate a large-scale synthetic dataset with perfect ground truth. Extensive experiments on both synthetic and real-world objects demonstrate that CVA-Net significantly outperforms state-of-the-art single-view methods. With a symmetric four-view configuration and fringe period of 8, CVA-Net achieves an MAE of 0.0359 mm, an MSE of 0.0379 mm² and an RMSE of 0.1947 mm, reducing the MAE, MSE, and RMSE by 32.8%, 54.1%, and 32.2%, respectively, compared to the best single-view competitor. Ablation studies validate the contribution of each architectural component, while real-system experiments demonstrate the feasibility of transferring a network trained purely on synthetic data to practical FPP measurements without domain adaptation. Although further improvements are required to enhance reconstruction accuracy under real imaging conditions, the proposed framework provides an effective initial step toward bridging the gap between digital-twin-based training and real-world multi-view FPP applications. CVA-Net provides a robust, occlusion-aware solution for multi-view FPP reconstruction. Full article

Background and Clinical Significance: Many diagnostic radiopharmaceuticals are excreted in the urine. This can pose a diagnostic challenge when urine-containing structures are in atypical locations, particularly in review of planar imaging without anatomical details from cross-sectional imaging. This case highlights a challenging ^99mTc-methylene diphosphonate (^99mTc-MDP) bone scan in a patient with an inguinal hernia containing a portion of the urinary bladder. Subsequently, we review diagnostic challenges on conventional and molecular imaging following surgical repair of the inguinal hernia. Case Presentation: A 79-year-old man with prostate cancer underwent initial staging prior to prostatectomy with ^99mTc-MDP bone scintigraphy. Anterior and posterior images showed focal uptake overlying the pubic symphysis. Lateral views showed that the activity was extraosseous. Follow-up CT urography showed a bladder hernia as the cause of the abnormality on bone scan. Prostatectomy and inguinal hernia repair were performed as a combination case. Four years postoperatively, follow-up ⁶⁸Ga-PSMA-11 positron emission tomography/computed tomography (PET/CT) showed no recurrence. The CT component of the exam showed an intermediate-density focus at the right inguinal hernia repair site, corresponding to a plugoma related to a polypropylene mesh plug, and a hyperattenuating Gore-Tex mesh repair of the left inguinal hernia. Conclusions: This case highlights the importance of lateral projections in resolving scintigraphic pitfalls and recognizing mesh-related imaging appearances to prevent misinterpretation. Full article

Point clouds acquired by low-cost laser scanning systems have a problem of high noise, which makes the point cloud appear as thick and geometric features blurred, while existing denoising algorithms either fail to maintain a balance between denoising and shape preservation or incur excessive computational cost. To address this issue, this paper proposes a shape-preserving denoising algorithm based on normal-guided cylindrical neighborhood and bilateral weighting. Specifically, the proposed method first optimizes the PCA-initialized normals of the point cloud by integrating curvature-based feature detection and bilateral weighting. Subsequently, a cylindrical neighborhood is constructed for each point along the optimized normal direction. Finally, a bilateral weighted projection mechanism that jointly incorporates spatial and normal features is employed, whereby the aggregated projection of neighboring points drives the displacement of the central point along the normal direction, thereby achieving point cloud denoising. Experiments are conducted on synthetic datasets and real scanned datasets. The results show that, for synthetic data denoising, the proposed method achieves the best or second-best performance in 25 out of 30 experiment cases across different models and different noise levels. For real scanned data, the section views and reconstructed mesh models demonstrate that the proposed method outperforms popular algorithms in removing complex noise while preserving geometric features. In addition, the proposed method demonstrates excellent computational efficiency, capable of denoising at a speed of processing one million points every 2.4 s, and achieves acceleration of processing speed by six times compared to the fastest competitive algorithms. Full article

LiDAR-based place recognition is critical for long-term autonomous navigation in Global Navigation Satellite System (GNSS)-denied environments, yet existing methods struggle to balance accuracy and efficiency under substantial yaw rotations. This paper proposes a robust framework based on a multi-channel polar bird’s-eye-view (BEV) representation. Under yaw-dominated revisits, the polar BEV image transforms yaw rotation into cyclic column shifts, providing a useful structural prior for rotation-equivariant feature extraction. Raw point clouds are projected onto polar BEV grids encoding density, height, and intensity. A rotation-equivariant feature extractor comprising a Radial Compression Module and a rotation-equivariant Transformer module captures long-range azimuthal dependencies via Conditional Positional Encoding and Circular Relative-Position Bias. The equivariant features are aggregated by NetVLAD into a compact global descriptor, trained end-to-end with a hard-example mining triplet loss. Extensive experiments on the public KITTI and NCLT datasets, as well as our self-constructed LiDAR Place Recognition Revisit (LPRR) dataset, demonstrate competitive performance on KITTI and superior performance on NCLT and LPRR among the compared methods. The proposed framework achieves a favorable trade-off between performance and computational cost, and shows promising cross-dataset generalization on the evaluated NCLT and LPRR datasets without fine-tuning. Full article

The formation of direction-selective visual circuits is thought to involve the progressive refinement of synaptic connections during development. In biological visual systems, patterned spontaneous activity, such as retinal waves, has been proposed to provide structured spatiotemporal activity that contributes to the refinement of visual pathways before mature sensory experience is fully established. Motivated by this view of activity-dependent circuit organization, this study develops a Self-Organizing Map-Based Artificial Visual System, termed SOM-AVS, to examine how organized connectivity may emerge in a motion direction-detecting circuit. In the proposed model, local motion-detecting units extract elementary direction-related responses from visual input and project them to a global motion direction layer represented by a self-organizing map. Connections are progressively reshaped by winner selection and local cooperative updating, allowing initially unstructured connections to gradually acquire organized direction preference. After repeated exposure to generated retinal-wave-like activity data, the SOM layer develops topographically arranged regions corresponding to distinct motion directions. This organization suggests that direction-related response domains can emerge from activity-dependent self-organization without externally imposed labels. The proposed model should be regarded as a biologically motivated computational abstraction rather than a direct physiological reproduction of retinal-wave-driven circuit development. Within this scope, the model provides a computational framework for examining how retinal-wave-like activity and self-organizing plasticity may contribute to the formation of motion direction-related connectivity, offering a possible developmental interpretation for bio-inspired visual motion processing. Full article

Introduction: The invasive European catfish (Silurus glanis) is actively spreading across Iberian freshwaters, with no effective management measures in place to control its growing abundance or prevent its establishment in new localities. It poses a severe threat to endemic and already endangered species, and is simultaneously a preferred target by few anglers who continuously promote its spread. The LIFE-PREDATOR project aims to stop the spread of European catfish in lentic systems in Portugal and Italy, particularly in protected areas. Objectives: This talk will present the mid-term results of the LIFE-PREDATOR in Portugal, and discuss the difficulties and future challenges to reduce the size of local populations of European catfish. Methodology: The LIFE-PREDATOR team developed several tasks in Portugal: (1) established the reference situation of fish communities in six reservoirs in the Tagus Basin, using scientific fishing, fish telemetry and eDNA-based tools; (2) determined the optimal protocols for sampling catfish; (3) implemented an early detection programme based on warning teams, data-mining and eDNA tools; (4) developed population control actions in four reservoirs; and (5) organised dissemination events for the general public, anglers, and students from kindergarten to university levels. Results: Overall, there is a grim view about recipient communities in the studied lentic systems, which tend to be dominated by invasive fish species, including common carp (Cyprinus carpio), gibel carp (Carassius gibelio), European catfish, pikeperch (Sander lucioperca), European perch (Perca fluviatilis) and largemouth bass (Micropterus nigricans). At least three new localities harbouring catfish were identified from online data-mining and warning teams. A total of 8 tons of catfish were removed by mid-June of 2025, mostly from the Natural Park of International Tagus. Outreach activities were conducted in nearly 60 schools, reaching more than 5000 students. Moreover, 67 general public events have reached more than 4500 people since the project started (September 2023). Conclusions: Despite its positive outcomes, the LIFE-PREDATOR team has encountered challenges in engaging key stakeholders such as anglers, involving local municipalities, and implementing catfish removal actions in remote areas. Difficulties and challenges in catfish management must therefore be debated in order to assure the after-LIFE implementation across Portuguese protected areas. Full article

Background: The upper gastrointestinal (GI) tract is a complex environment characterized by sharp physicochemical gradients. While the oral microbiome is a major source of microbial seeding for downstream organs, it remains unclear how these communities correlate and diverge across different anatomical sites. This study provides a high-resolution re-analysis of a comprehensive multi-site dataset to delineate the microbial architecture and ecological signatures along the oral–upper GI axis. Method: Human oral, esophageal, gastric mucosal, and gastric juice microbiome sequencing data were retrieved from the publicly available National Center for Biotechnology Information (NCBI) BioProject PRJNA1049979 database. Using these publicly available 16S rRNA sequencing data, we performed an integrated ecological analysis. Microbial diversity, taxonomic composition, and niche-specific community structures were evaluated using Quantitative Insights Into Microbial Ecology 2 (QIIME2) and R-based tools, including linear discriminant analysis effect size (LEfSe) and phylogenetic mapping. Results: The esophageal microbiome showed significantly greater richness and evenness than the oral cavity and stomach. Beta diversity analysis demonstrated clear compositional separation between oral and downstream upper GI communities, whereas gastric samples, particularly gastric juice, showed greater heterogeneity. Although major phyla were shared across sites, their relative abundances differed markedly. Oral samples were enriched with periodontal-associated taxa, including Porphyromonas, Prevotella, Alloprevotella, and Fusobacterium. In contrast, gastric mucosal samples were enriched with Akkermansia muciniphila and Helicobacter pylori, whereas gastric juice was characterized by Sarcina ventriculi, Fusobacterium periodonticum, and Clostridium perfringens. These findings indicate both taxonomic continuity and pronounced site-specific ecological divergence along the oral–upper GI axis. Conclusion: The oral cavity, esophagus, stomach, and gastric juice share a common microbial framework but exhibit distinct community restructuring driven by local environmental selection. This study provides a detailed ecological view of the oral–upper GI microbiome and highlights the importance of site-specific microbial organization in upper GI health and disease. Full article

The construction sector operates under conditions of high capital intensity, project complexity, cost uncertainty, fragmented supply chains, and increasing pressure to improve efficiency, sustainability, and long-term competitiveness. Although prior research has emphasized the importance of organizational resources and knowledge-based capabilities for competitive advantage, fewer empirical studies have examined how internal capacities, intellectual capital, and knowledge sharing jointly explain sustainable competitive advantage in construction companies. Drawing on the resource-based view, the knowledge-based view, and the dynamic capabilities perspective, this study examines the effects of marketing capacity, financial capacity, innovative capacity, management capacity, human capacity, human capital, structural capital, relational capital, and knowledge sharing on sustainable competitive advantage in the construction sector. Survey data were collected from 306 employees working in construction companies in the Republic of Serbia and analyzed using confirmatory factor analysis and covariance-based structural equation modeling. The measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity. The structural results indicate that financial capacity is the only significant internal capacity predicting sustainable competitive advantage, while relational capital is the only significant dimension of intellectual capital. Marketing capacity, innovative capacity, management capacity, human capacity, human capital, structural capital, and knowledge sharing did not show significant direct effects. The study contributes to research on sustainable competitive advantage by showing that, in construction companies, competitiveness is most strongly associated with financial robustness and stakeholder-based relational strength. For managers, the findings highlight the importance of strengthening liquidity, investment capacity, risk absorption, and long-term relationships with clients, suppliers, subcontractors, and institutional stakeholders. Full article

Autonomous navigation in unstructured off-road environments presents fundamental challenges due to terrain heterogeneity, the absence of structured road markings, and the necessity for real-time traversability reasoning from raw sensory observations. We present TerrainFormer, a hierarchical framework that integrates a world model for terrain dynamics prediction with a temporal decision transformer for action selection. Our methodology employs a two-phase training paradigm: (1) self-supervised world model pretraining on LiDAR point clouds to learn terrain representations encompassing traversability, elevation, and semantic segmentation; (2) behavioral cloning of the decision transformer conditioned on frozen world model features with temporally derived goal directions. The world model processes raw 3D LiDAR point clouds through a PointPillars encoder for real-time bird’s-eye-view (BEV) projection, followed by a Vision Transformer backbone that produces latent terrain representations. A principal contribution is our cross-dataset generalization paradigm: the world model is trained on separate datasets while the decision transformer is trained on separate sequences, ensuring zero data overlap between training phases. We introduce automatic goal direction computation from vehicle pose trajectories, enabling the model to learn directionally conditioned navigation policies. To address the class imbalance inherent in off-road driving data, we employ focal loss with inverse-frequency class weighting and action-chunk supervision. Experimental evaluation on the RELLIS-3D dataset achieves 87.31% test accuracy with 0.7948 macro F1 across all 12 action classes. The world model’s predicted future frames produce only a 0.79% accuracy drop versus ground-truth observations, with 98.82% action agreement, demonstrating effective cross-dataset generalization for real-time off-road navigation. Full article

This paper develops a boundary-sensitive asymptotic theory for nonparametric conditional U-statistics smoothed by support-adapted asymmetric kernels when the response variable is subject to Missing-at-Random observation. The problem lies at the intersection of three well-established but traditionally separate lines of research: conditional U-statistics, asymmetric smoothing on constrained supports, and incomplete-data inference under MAR sampling. The contribution of the paper is not a novelty claim concerning any of these components in isolation. Rather, it consists in deriving a kernel-specific and MAR-aware limit theory for their simultaneous occurrence, where the estimators are nonlinear complete-case ratios of localized U-statistics and the localization devices are point-dependent approximate identities adapted to the geometry of the covariate support. The analysis covers three principal classes of support-respecting smoothers: Dirichlet kernels on the simplex, Bernstein polynomial smoothers, and multivariate beta kernels on hypercubes, with an additional extension to mixed continuous–categorical regressors. These smoothing schemes are not translation-invariant, and their local moments, effective support, normalizing constants and $L^2$-masses vary with the evaluation point, especially near the boundary. Consequently, their incorporation into conditional U-statistics requires more than a direct transfer of ordinary asymmetric-kernel regression theory. The numerator and denominator of the estimators are localized U-statistics whose stochastic expansions are governed by Hoeffding projections, including canonical components that must be controlled uniformly over the conditioning domain. Under regularity, smoothness and positivity assumptions adapted to the MAR setting, we establish uniform consistency, weak and strong uniform convergence rates, stochastic expansions and asymptotic normality. The results are obtained both on fixed compact subsets and on interior regions approaching the boundary, thereby identifying how support geometry enters the bias and stochastic normalizations. A central feature of the theory is the separation between the deterministic effect of complete-case sampling and its stochastic effect. For the complete-case estimator, the natural deterministic equivalent is obtained by replacing the design density f with the effective complete-case density $pf$, where p is the propensity score. Thus, the MAR mechanism may enter higher-order deterministic bias constants through the local design tilt, whereas the leading stochastic dispersion reflects the loss of effective information through propensity score factors. The precise variance constants and normalizing rates remain kernel-specific, depending on the local $L^2$-structure of the Dirichlet, Bernstein or beta smoothing device. The paper should therefore be viewed as a MAR extension and refinement of the complete-data asymmetric-kernel conditional U-statistic theory. It provides a common probabilistic architecture for several boundary-adapted smoothing schemes while retaining the kernel-dependent bias operators, variance constants, boundary regimes and Hoeffding-projection structures required for sharp asymptotic interpretation. Numerical experiments illustrate the finite-sample behavior predicted by the theory and highlight the interaction between support-adapted smoothing, boundary effects and incomplete response observation. Full article

The entorhinal cortex (EC) is a central structure of the medial temporal lobe, functioning as the main cortical gateway to the hippocampus (HPC) and playing a crucial role in memory, spatial navigation, and temporal representation. This review outlines the distinct yet complementary contributions of its two main subdivisions, the medial (MEC) and lateral (LEC) entorhinal cortices. Despite being historically viewed as functionally segregated, they operate instead in close coordination to support the encoding and retrieval of multidimensional experiences. While the MEC is prominently involved in mapping spatial relationships and movement through specialized cell populations, and the LEC in processing object-related and contextual information, growing evidence shows substantial integration between these domains, challenging strict dichotomies. The MEC encodes elapsed time through persistent firing and time cell sequences, while the LEC signals temporal context via rate remapping; their convergent projections to the hippocampus enable the formation of temporally structured episodic memories. The review assesses recent findings on memory, navigation, and time processing, and highlights how the EC supports each through its layered architecture, local microcircuitry, and widespread interactions with HPC, cortical, and subcortical networks. Moreover, alterations in EC activity patterns emerge as the earliest signs of pathologies such as Alzheimer’s disease and temporal lobe epilepsy. Altogether, this review offers an up-to-date view of the EC not as a set of parallel modules, but as a highly interactive and dynamic system essential for structuring experience across space, time, and context. Full article

Wildlife is a common asset to which the local community has the right to consume. To achieve sustainable management of this resource, a community forest (CF) with a wildlife vocation has been set up as part of the “Sustainable management of wildlife and the bushmeat sector in Central Africa” project. Given the constraints faced by these community forests (CFs), we conducted a study to assess their governance in Gabon. Our objective was to examine whether their current mode of operation would allow them to survive in the long term, with a view to integrating sustainable hunting practices. To do this, we constructed a SWOT matrix (strengths, weaknesses, opportunities and threats) to determine their strengths and weaknesses, from which we carried out a factorial correspondence analysis (FCA) to identify potentially viable CFs. This enabled us to understand that most of the difficulties encountered by these CFs stem from the low level of appropriation of this concept by local communities, which is due to the low level of intervention by the forestry administration in raising awareness of CF management. This study shows that local communities must first take ownership of how CFs work so that they can better apply their success factors. Full article

Target detection in synthetic aperture radar (SAR) images is constrained by three primary challenges. First, speckle noise overlaps heavily with the high-frequency features of target edges in the frequency domain, so standard convolutions cannot suppress noise without sacrificing edge texture. Second, the scattering signature of a SAR target varies markedly with viewing angle, and a fixed-parameter convolution kernel cannot accommodate this spatial non-stationarity. Third, deep and shallow levels of the feature pyramid differ in semantics and resolution, and a naive element-wise sum either introduces noise interference or loses small-target signals. We propose the Frequency–Spatial Detection Network (FSDNet), whose core FSDBlock cascades three operators to address these failure modes in turn. Wavelet Convolution (WTConv) projects features into Haar sub-bands and applies independent low- and high-frequency kernels prior to inverse-DWT reconstruction, suppressing noise while preserving edges. Receptive-Field Attention Convolution (RFAConv) generates location-conditional kernels and so adapts to non-stationary scattering. Spatial Context Self-Attention (SCSA) aggregates discrete scattering points into coherent target representations via long-range grouped attention. At the fusion stage, CGAFusion replaces FPN element-wise addition with a channel–spatial–pixel triple-attention soft switch that mitigates deep–shallow semantic mismatch. On HRSID, FSDNet attains mAP₅₀ $= 92.3$% and mAP_50:95 $= 68.6$%. On SSDD, it attains mAP₅₀ $= 98.7$% and mAP_50:95 $= 74.2$%. Both sets of results consistently surpass the baseline methods. Against the strongest YOLO baseline (YOLOv11n), FSDNet improves HRSID mAP₅₀ by $+1.7$ percentage points (pp) and mAP_50:95 by $+2.3$ pp, and SSDD mAP₅₀ by $+0.5$ pp and mAP_50:95 by $+2.7$ pp; against the capacity-fair YOLOv11s reference (∼51% more parameters), FSDNet still leads on mAP₅₀, mAP_50:95, recall, and F1. Ablation studies and power-spectral-density analyses corroborate the contribution of each module and confirm WTConv’s role in preserving high-frequency target features. Full article