Search Results (16,952)

Background/Objectives: The use of quantitative neuroimaging to establish objective biomarkers in neurodegenerative diseases (NDD) has attracted increasing interest over the last decade. Advanced magnetic resonance imaging (MRI) such as arterial spin labeling (ASL) and diffusion tensor imaging (DTI), as well as [¹⁸F]fluorodeoxyglucose ([¹⁸F]FDG) positron emission tomography (PET), could provide clinically meaningful biomarkers and may support differential diagnosis. The aim of this investigator-initiated, single-center, retrospective comparative study was to implement a framework for multimodal neuroimaging to evaluate cases with rare NDD, using a methodological approach that integrates metabolic, perfusion, and microstructural parameters from simultaneous FDG-PET/MRI, and to investigate its potential to facilitate diagnosis. Methods: Three patients with pathological motor signs (1f/2m; 63, 73, and 52 years) and 19 control subjects with subjective cognitive deficits (SCDs) underwent combined FDG-PET/MRI with pseudo-continuous ASL and DTI. Standardized uptake values (SUVs), relative cerebral blood flow (rCBF), and fractional anisotropy (FA) were calculated to identify pattern alterations in individual patients based on parameterization mapping. The final diagnosis was corticobasal degeneration (CBD, n = 1) or primary lateral sclerosis (PLS, n = 2). Results: At the individual patient level, disease-specific changes in defined brain regions could be demonstrated and quantified compared to control subjects. All three patients showed significantly decreased FA, primarily along parts of the course of the corticospinal tract (CST). In the patient with CBD, asymmetric SUVR and rCBF decreases were observed, mostly overlapping with motor regions. In the two patients with PLS, SUVR revealed mostly unspecific findings (hypothetically due to a slow progression rate or due to potentially early disease stages), while ASL indicated decreased rCBF primarily overlapping within the motor cortex. Changes at the gray matter level were primarily located adjacent to changes in white matter, as indicated by the multimodal analysis approach using simultaneously acquired FDG-PET/MRI data. Conclusions: According to this proof-of-concept study, multimodal neuroimaging by the combination of quantitative MRI and FDG-PET has the potential to guide differential diagnosis in rare NDDs, especially if clinical diagnosis is not straightforward to achieve. Since particularly early diagnosis remains essential for patient counseling, effective treatment, and clinical management, the present framework appears helpful to be developed further until it aligns and integrates with clinical routine. Full article

The Sustainable Development Goals (SDGs) are widely presented as an integrated framework for social, economic, and environmental progress, yet recent assessments indicate substantial implementation shortfalls. This scoping review maps post-2015 scholarship on one of the framework’s most contested fault lines: the relationship between Goal 8 (economic growth) and the ecologically oriented goals, especially Goals 6, 12, 13, 14, and 15. Following established scoping review guidance, 32 sources published between 2015 and 2026 were identified from Scopus, Web of Science, Google Scholar, citation searching, and selected grey literature. The synthesis indicates four main patterns in the included corpus. First, a substantial share of the reviewed literature characterizes continued growth-centred development and ecological sustainability as difficult to reconcile under current technological and institutional conditions, particularly given evidence on material throughput, emissions, and planetary boundaries. Second, the corpus recurrently describes three mechanisms through which this tension is muted within the SDG architecture: the rhetorical absorption of ecological limits into “green growth” discourse, strategic vagueness in targets and indicators, and the marginalization of alternative development ontologies. Third, the review synthesizes these mechanisms under the interpretive concept of paradigmatic stacking. Fourth, the corpus identifies alternative resources for a successor framework, including relational and plural conceptions of well-being associated in the reviewed literature with Ubuntu, Buen Vivir, and Gross National Happiness. Taken together, the findings suggest that debates about SDG underperformance cannot be reduced to implementation alone but also involve questions of conceptual design. The article concludes by outlining ontological pluralism as a possible direction for post-2030 framework design. Full article

Triply periodic minimal surface (TPMS) gyroid lattices are promising lightweight and energy-absorbing polymer structures, but their manufacturability by material extrusion (MEX) depends strongly on cell size, grading direction, and relative density. This study investigates PLA gyroid lattices with uniform and graded cell-size configurations using initial and final cell sizes of 1, 1.5, and 2 mm and target relative densities of 10, 20, and 30%. A full-factorial design was used to construct a printability map, followed by quasi-static compression testing, areal surface-roughness characterization, and SEM observation of representative specimens. The printability results showed that low-density fine-cell configurations were most prone to incomplete wall formation and collapse, whereas the 30% relative-density group was printable for all investigated cell-size combinations. Under compression, the 30% relative-density uniform 1 mm gyroid showed the highest maximum stress among the tested configurations, while graded structures terminating in smaller cells also provided favorable load bearing and energy-absorption behavior. The plateau stability index, calculated from stress fluctuations between collapse and densification, helped distinguish stable progressive collapse from more oscillatory deformation. Surface roughness and SEM observations further indicated that smoother, more continuous wall surfaces were associated with more uniform deformation, whereas rougher and defect-rich surfaces promoted localized buckling, cracking, and brittle collapse. Overall, the results identify experimentally supported relationships between gyroid cell-size configuration, printability, surface integrity, and compressive response within the investigated PLA MEX design space. Full article

This paper presents a metrology-oriented review of vision-based displacement and deformation measurement for civil structural health monitoring (SHM), with an emphasis on field robustness and uncertainty quantification (UQ). The review focuses on image- and video-based methods that convert visual information into quantitative physical measurements, such as displacement, strain, or derived dynamic indicators. The literature is organized according to the main stages of the measurement chain: image formation, image-plane motion estimation, and geometric conversion to metric motion. Within this framework, measurement pipelines are interpreted through three levels of geometric mapping, namely, a scalar scale-factor model, a planar homography-based model, and a full Jacobian-based model. The review synthesizes major method families, including marker-based and markerless tracking, feature-based tracking, optical flow, digital image correlation (DIC), phase-based motion magnification, edge-based estimators, fixed- and moving-camera configurations, UAV-based acquisition with ego-motion compensation, hybrid vision–sensor fusion, and deep-learning-enhanced pipelines. A structured taxonomy of uncertainty sources is then presented along the processing chain, covering camera geometry and calibration, imaging noise and blur, quantization, timing and synchronization, environmental disturbances, optical turbulence and heat haze, platform motion, algorithmic failure modes, and reference-sensor uncertainty. The paper also compares UQ practices, including GUM-aligned analytical propagation, Monte Carlo methods, DIC-specific error budgets, bootstrap and resampling strategies, and probabilistic deep learning. The main contribution of this review is to connect computer-vision-based displacement pipelines with metrological requirements by explicitly linking measurement models, uncertainty sources, UQ methods, and field-validation evidence within a unified framework. A practical uncertainty-budget template is compiled to support traceable reporting across different pipelines and deployment scenarios. The paper concludes with prioritized research gaps and future directions, including standardized benchmarks and datasets, traceable UQ for moving-camera systems, multi-sensor fusion with end-to-end uncertainty propagation, long-term drift characterization, optical-turbulence and adverse-weather modeling, validated subpixel limits at extreme range, probabilistic deep learning–metrology integration, and standardized reporting practices. Full article

Spontaneous movement analysis provides valuable information about the maturation of the central nervous system and the emergence of motor control strategies in very young babies. Nonlinear measures capture dynamic aspects of movement that cannot be represented by linear methods. However, their implementation in clinical practice faces challenges, including the lack of standardized protocols and accessible tools for routine use. This scoping review aimed to map and characterize the nonlinear measures used to analyze spontaneous infant movement, including assessment context, instruments, data collection protocols, and main variables. The review followed JBI methodology and PRISMA-ScR guidelines. Searches were conducted in PubMed^®, Web of Science™, IEEE Xplore^®, ScienceDirect^®, and Google Scholar for studies published from 1 January 2005 to 31 December 2025. Of 1166 records identified, 18 met the inclusion criteria. The nonlinear measures were grouped into five main methodological families: entropy-based measures (n = 10), state-space and dynamical systems measures (n = 4), recurrence-based analysis (n = 3), symbolic and discrete-state approaches (n = 3), and variance and frequency-based nonlinear descriptors (n = 1). Studies were conducted in laboratory settings (n = 6) and in hospital and/or home environments (n = 10). Two studies did not clearly specify the assessment context. Kinematic assessment was mainly performed using video-based systems (n = 7), accelerometers (n = 4), and wearable sensors (n = 2), with most studies focusing on the upper and lower limbs. Several investigations extended beyond single-joint analyses to examine inter-limb relationships and whole-body configurations, capturing spatial coordination patterns across multiple body segments. Kinetic assessment was conducted using pressure mats (n = 4) and force platforms (n = 1), with the center of pressure displacement as the primary outcome. Future research should prioritise methodological harmonisation and theoretical clarity. Consensus is needed regarding minimal data requirements, parameter selection, and reporting standards for commonly used nonlinear measures. Studies should also move beyond single-metric approaches and adopt multivariate frameworks that integrate complementary nonlinear metrics. The absence of standardised acquisition and analytical protocols currently limits cross-study comparability and hinders the clinical translation of nonlinear movement metrics as objective tools for early neurodevelopmental assessment. Full article

This study evaluates an Adaptive Monte Carlo Localization-Extended Kalman Filter (AMCL-EKF) pose-estimation stack for repeatable 2D LiDAR SLAM in GPS-denied indoor inspection scenarios. AMCL was used as an online map-referenced correction source fused with LiDAR odometry and Inertial Measurement Unit (IMU) data, and the resulting pose estimate was supplied online to three SLAM backends: Cartographer, GMapping, and SLAM Toolbox. Experiments were performed with a wheeled Husarion Panther and a quadruped Boston Dynamics Spot in three indoor environments of different geometric complexity, producing 720 SLAM executions. Trajectory repeatability was assessed using SE(2)-aligned pairwise and centroid-based ATE-style dispersion and translational RPE, while map repeatability was evaluated with occupied-cell IoU. Accordingly, the metrics were used to quantify between-run dispersion rather than absolute accuracy against external ground-truth data. The results show that AMCL-EKF fusion is highly dependent on the environment, platform, and SLAM backend. AMCL improved selected configurations, especially for Spot in structured environments and for Panther map consistency, but degraded others in geometrically repetitive corridors and mixed-structure spaces. The study also shows that the presence of AMCL-assisted odometry correction alone does not determine final trajectory repeatability, because each SLAM backend incorporates the supplied fused pose estimate differently. The findings support confidence-aware AMCL integration and motivate integrated SLAM architectures resistant to over-correction. These results provide guidance for robust autonomous mapping and inspection with heterogeneous mobile robotic platforms in real environments. Full article

Background/Objectives: Diagnostic metabolites measured in newborn screening, inherited metabolic disease, lysosomal storage disease, oncometabolite testing and routine clinical biochemistry are direct read-outs of human metabolic state. Their mechanistic interpretation requires linking measured metabolites to enzymes, pathways, regulatory context, disease knowledge and, increasingly, AI-assisted quantitative systems pharmacology (AI-QSP) workflows. We developed EHMN2026^®T as a license-aware AI-QSP integration framework that connects the EHMN2026^® metabolic backbone with licensed geneXplain knowledge resources while keeping ownership, licensing and redistribution constraints explicit. Methods: EHMN2026^®T integrates the SBML-encoded EHMN2026^® metabolic backbone with licensed TRANSFAC^® 2025.2, TRANSPATH^® 2025.2 and HumanPSD™ 2025.2 resources. TRANSFAC^® position weight matrices were used for promoter-level analysis of EHMN metabolic genes. The resulting transcription factor (TF)–gene connections were mapped to EHMN genes, TRANSPATH^® signalling/molecular-state entries and HumanPSD™ disease/drug context. The framework is positioned as a controlled component of the IQANOVA AI-QSP environment, but only aggregate statistics, non-proprietary EHMN-derived summaries and manuscript-level examples are reported publicly unless separate permission is obtained from the relevant rightsholders. Results: Promoter analysis of 1681 EHMN2026^® metabolic genes using 1147 mapped TRANSFAC^® matrices identified 291,387 ENSG-level TF–gene regulatory-potential connections involving 398 TFs and 1,107,264 predicted binding sites. The diagnostic panel contained 80 covered genes (63.5%), including complete coverage of oncometabolite enzymes and high coverage of organic acidaemia, steroidogenesis and fatty-acid oxidation categories. Mapping to TRANSPATH^® expanded the EHMN genes into 144,529 molecular-state representations and 14,879 gene–pathway or gene–chain pairs. HumanPSD™ was used as a licensed translational context layer; EHMN-specific HumanPSD™ outputs are treated as license-controlled derived outputs and are therefore not redistributed as open detailed tables in this manuscript. Conclusions: EHMN2026^®T provides a license-aware AI-QSP integration framework for tracing a diagnostic metabolite from a measured clinical value to candidate enzyme nodes, regulatory potential, signalling/molecular-state context and disease or therapeutic interpretation. PWM-derived TF–gene links are presented as regulatory hypotheses, not proof of active regulation. Public release should be limited to aggregate statistics and non-proprietary EHMN-derived components; detailed TRANSFAC^®, TRANSPATH^® and HumanPSD™-derived edges, mappings, annotations and SBML outputs remain subject to geneXplain ownership and licensing terms. Full article

This study addresses the lack of integrated methodologies for qualifying additively manufactured polymer lifting attachments for UAV cargo operations under the EASA Specific category. A Design–Verify–Validate framework was developed to combine operational requirements, regulatory mapping to SORA Operational Safety Objective #05, material and manufacturing considerations, nonlinear finite element analysis, and experimental validation. The framework was demonstrated through the complete development of a 241 g FDM-printed PLA+ dual-bill gravitational hook for 50 kg Working Load Limit operations on the DJI Agras T50 platform (DJI, Shenzhen, China). Nonlinear finite element analysis was used to identify critical stress concentrations, while quasi-static testing of three identical specimens yielded an average failure load of 183 ± 9 kg, corresponding to an experimental safety factor of 3.66 ± 0.17. Functional testing on a suspended UAV platform confirmed reliable kinematic performance at incremental loads of 5 kg, 25 kg, and 50 kg. The results demonstrate that the proposed framework can generate coherent, standards-aligned verification evidence under quasi-static loading conditions. Structural validation in this study was limited to this loading regime. While demonstrated on a 50 kg WLL gravitational hook using unreinforced PLA+ as a proof-of-concept material, the methodology can be adapted in future work to other UAV platforms, geometries, and higher-performance materials. Full article

Urban Green Spaces (UGSs) monitoring usually focuses on the extraction of vegetation in the physical layer, while neglecting their functional attributes. This renders the monitoring results unable to objectively reflect the rationality of UGS planning. To address these issues, this study proposes a multi-level classification method integrating multi-source remote sensing and geospatial big data to bridge the semantic gap between the physical layer and the functional layer. In this method, a strategy of prior knowledge injection and semantic reconstruction was developed through the fine-tuning of a BERT model with cross-mapping rules. This strategy aims to classify the urban area into 24 functional categories, generating the social-functional basemap in a functional layer, based on Point of Interest (POI), OpenStreetMap (OSM), and Global Urban Boundary (GUB). Meanwhile, a novel deep learning architecture, namely the Multi-Shape and Spectral Aware Network (MSSANet), was designed for precise vegetation classification of UGSs in the physical layer. Finally, a “function-first, vegetation-second” coupling paradigm containing three functional attribute layers, referring to the Code for Classification of UGS in China (CJJ/T 85-2017), was established. This paradigm integrates the social-functional basemap with physical vegetation patches to build a multi-level UGS classification framework, i.e., the 5 major UGS categories, 11 intermediate UGS categories, and 24 fine-grained UGS sub-categories. Experiments conducted in Jinan and Qingdao, China, demonstrate the efficacy of the proposed method for refined multi-level UGS mapping. Full article

Accurate monitoring of inundation in rice paddies is essential for optimizing water use efficiency and mitigating methane emissions; yet, detecting water beneath dense rice canopies remains a major challenge. This study proposed a reliable classification approach applied to the Winter–Spring 2025 season in An Giang province, Vietnam, by integrating multi-temporal ALOS-2 PALSAR-2 (L-band) and Sentinel-1 (C-band) SAR data with in situ field surveys. Time-series Sentinel-1 observations were used to estimate rice phenology (rice age), while multi-polarization backscatter from ALOS-2 PALSAR-2 was analyzed to discriminate inundated from non-inundated conditions across different growth stages. Results demonstrated that L-band signals, particularly in VV polarization, penetrated dense vegetation effectively, enabling classification of inundated vs. non-inundated fields with an overall accuracy of 81% and a Kappa coefficient of 0.77. The resulting multi-date inundation maps revealed distinct flooding regimes consistent with local field survey observations. These findings demonstrated the potential of L-band VV SAR data for characterizing sub-canopy inundation conditions under rice canopies. Crucially, the approach provides essential data for greenhouse gas inventories and supports the verification of low-emission water management practices, such as Alternate Wetting and Drying (AWD). Overall, the study demonstrated the value of multi-frequency SAR integration for advancing agricultural monitoring and climate-smart management in rice-growing regions. Full article

As a pilot-scale feasibility study, aging bridge infrastructure and limited inspection resources have created an urgent need for automated and reliable bridge condition assessment systems. Most existing deep learning-based inspection approaches detect damage types from images without considering the structural member on which the damage occurs, limiting their practical utility for maintenance decision-making. This study proposes a structure-aware deep learning framework for automated bridge inspection that integrates structural member segmentation, two-class damage detection, and spatial damage-to-member association within a unified pipeline. A SegFormer-based semantic segmentation model was trained on a custom bridge inspection dataset comprising 1339 images to identify three primary structural member classes—main girder, deck slab, and abutment—achieving a test mean Intersection over Union (mIoU) of 0.851. Boundary refinement using the Segment Anything Model (SAM) in mask-prompt mode was applied to improve mask precision during training data preparation. A YOLOv8s object detection model was trained on a custom bridge damage dataset of 9142 annotated images (6531 training, 1740 validation, and 871 test images) to detect two damage classes—crack and corrosion—achieving a mean Average Precision (mAP50) of 0.445 at a confidence threshold of 0.30. The framework associates detected damage with segmented structural members using a region-based spatial assignment strategy, enabling structure-aware outputs such as “crack on main girder” and “corrosion on deck slab.” Manual evaluation on 100 bridge inspection images demonstrated a fully correct damage detection accuracy of 70.0% and a fully correct member assignment accuracy of 62.0%. When partially correct predictions were additionally considered for qualitative analysis, the corresponding accuracies increased to 84.0% and 87.0%, respectively. The main girder class achieved the highest combined accuracy for both damage detection (90.9%) and member assignment (93.9%). These results demonstrate the potential of the proposed framework as a first layer for AI-assisted bridge inspection by associating detected damage with structural members, providing structured inspection information to support subsequent maintenance assessment and infrastructure monitoring. Full article

To achieve non-contact and continuous monitoring of body length in Micropterus salmoides and overcome the stress damage and subjective error associated with traditional manual measurement, this paper proposes an improved YOLOv8-based multi-target tracking framework for intensive recirculating aquaculture systems. The system employs a geometric measurement framework based on monocular vision that achieves conversion from pixel coordinates to actual body length through camera calibration, water-surface refraction correction, and pose projection correction. Under a collaborative optimization framework integrating detection and tracking, the model incorporates multi-scale feature enhancement, lightweight re-identification (ReID), and a robust data association mechanism, which improves system stability under conditions of high fish density, variable illumination, and turbid water. A shallow feature fusion path is introduced to enhance small-target perception, and a MobileNetV3_ReID model is adopted to extract highly discriminative appearance features, which improves identity consistency while maintaining model compactness. In the data association stage, a hybrid cost matrix integrating IoU, cosine similarity, and motion consistency is constructed, and optimal matching is realized through the Hungarian algorithm. Dynamic threshold adjustment and an exponential moving-average feature-update strategy are introduced to effectively suppress identity switching. Experiments were conducted on an overhead video dataset of Micropterus salmoides collected at a recirculating aquaculture system facility. The results show that the proposed method achieves 82.7% mAP50 while maintaining a real-time throughput of 88 FPS, with MOTA reaching 76.9% and IDF1 reaching 81.5%—the latter representing an improvement of 3.2 percentage points over BoT-SORT and 5.3 percentage points over the YOLOv8 baseline tracker. The number of identity switches (IDSW) decreased from 89 in the baseline configuration to 39, a reduction of 56.2%. Crucially, these component-level improvements translate into a body length error (BLE) of 5.2 ± 1.8% (MAE = 1.35 cm, Pearson r = 0.972), representing a 38.8% improvement over the baseline BLE of 8.5% and satisfying the 5–10% tolerance required for aquaculture growth monitoring. Ablation analysis confirms that both detection enhancements (contributing −1.3% BLE) and tracking optimizations (contributing −2.0% BLE) are necessary to achieve this application-level accuracy. Full article

Terraced vineyard landscapes represent complex cultural systems shaped by long-term interactions between geomorphology, agriculture and rural heritage. This study investigates the terraced viticultural landscape of Castiglione di Sicilia, located on the northern slope of Mount Etna (Sicily, Italy), through an integrated GIS-based framework combining Landscape Character Assessment (LCA), rural micro-structure mapping, visibility analysis and slow mobility assessment. Attention was given to dry-stone walls and traditional rural micro-structures, recognized as key components of the historical landscape identity of Etna. The methodology integrated geomorphological variables, vineyard distribution, dry-stone wall density and slope suitability within a Rural Micro-Structure Intensity Framework (RMSI) developed to identify areas characterized by stronger continuity of terraced rural landscapes. A sensitivity analysis was performed to evaluate the robustness of the weighting scheme. Slow mobility corridors were subsequently analyzed according to their relationship with landscape continuity and scenic visibility. The results reveal a strong spatial association between vineyards, dry-stone wall systems and historically structured terraced landscapes. Visibility analysis highlighted that highly visible areas do not fully correspond to the highest RMSI values, reflecting the geomorphological complexity of the volcanic environment. The corridor providing the best balance between scenic visibility, rural heritage continuity and route accessibility emerged as the most suitable option for landscape-oriented planning. The proposed framework supports landscape-sensitive planning and sustainable territorial valorization in Mediterranean terraced agricultural systems. Full article

This study presents a critical historical, bibliometric, and technological overview of the evolution of postharvest science, emphasizing the transition from classical physiology-based approaches to emerging predictive and technology-driven systems. Scientific production related to postharvest research was analyzed using the Scopus and Web of Science databases, while bibliometric mapping and co-occurrence networks were generated using VOSviewer to identify thematic trends, emerging research areas, and structural scientific clusters. In parallel, a technological foresight analysis was conducted through the Lens.org platform to investigate the temporal evolution of patent deposits, the geographical distribution of innovation, the leading institutional applicants, and the predominant technological domains according to the Cooperative Patent Classification (CPC). The results revealed a substantial global expansion of postharvest research over recent decades. This growth was accompanied by increasing technological diversification and stronger integration between scientific knowledge and intellectual property protection. The analysis also highlighted the progressive incorporation of advanced methodologies into postharvest science, including biochemical approaches, non-destructive technologies, artificial intelligence, predictive modeling, and digital tools for quality assessment and shelf-life management. Overall, the study demonstrates that postharvest science is undergoing a paradigmatic transition toward integrated, multidisciplinary, and data-driven systems aligned with current demands for sustainability, food security, innovation, and reduction of postharvest losses. Full article

The Mangyahedong gold deposit—recently discovered in the Qimantage segment of the East Kunlun orogenic belt—is a high-priority exploration target. Key unknowns include its mineralization age, the sources of sulfur and gold, and the tectonic–magmatic–hydrothermal controls on formation. These gaps have hindered genetic classification and stage-specific research. We addressed them through integrated petrography, TIMA mineral mapping, in situ LA-ICP-MS analysis of pyrite from three mineralization stages, and U-Pb dating of hydrothermal apatite spatially and temporally linked to the main sulfide-precipitation event. The stages are: (I) early sericite–quartz alteration; (II) main ore stage—carbonate–chlorite–sulfide + native gold; and (III) late calcite–pyrite veins. Pyrite zoning shows that early pyrite cores are enriched in As and Au. In contrast, the main-stage pyrite has As-poor cores, with As, Au, and Co progressively enriched toward the rims. This zoning pattern indicates evolving fluid redox conditions and metal complexation during ore deposition. A ²⁰⁷Pb/²⁰⁶Pb age of 406 ± 13 Ma from apatite in gold-bearing quartz–sulfide veins constrains gold deposition to the Late Silurian–Early Devonian transition. Age, texture, and geochemistry collectively support a regional metamorphic–deformational origin, consistent with the orogenic gold model. Isotopic and elemental data point to the Qimantage Group volcanic rocks as the dominant source of ore-forming elements—indicating strong potential for discovery along strike and at depth. Full article