Search Results (297)

To mitigate the position divergence resulting from attitude error amplification in conventional fully coupled architectures, this study proposes a decoupled navigation architecture based on the Versatile Quaternion-based Filter (VQF). This architecture removes attitude estimation from the state vector, forming a two-layer structure comprising an independent attitude module and a navigation filter. The VQF is integrated as a standalone attitude module via a standardized interface. An uncertainty quantification model is developed by extracting the VQF’s internal correction states, which maps deviations among intermediate quaternion values to a measurable uncertainty metric. To compensate for the loss of cross-covariance induced by decoupling, a dual-layer compensation mechanism is introduced: a base layer adjusts the overall uncertainty using innovation statistics, while a compensation layer explicitly propagates attitude uncertainty through parameterized noise matrices. Experimental results demonstrate that the proposed method achieves notable improvements in positioning accuracy and significantly suppresses extreme errors in high-curvature scenarios. The approach is particularly effective for high-curvature, high-dynamic applications where process noise modeling is inherently difficult. Compared to traditional fully coupled architectures, the decoupled architecture offers enhanced robustness. The complementary characteristics identified between the two architectures provide valuable insights for expanding the operational envelope of underwater navigation systems. Full article

The sustainability of resources and ecological integrity are significantly influenced by land use and land cover change (LULCC) dynamics, particularly in ecotonal semi-arid regions where biome transitions are highly sensitive to anthropogenic disturbance and climatic variability. This study aims to assess historical LULCC dynamics and spatial reconfiguration across nine classes (grassland, shrubland, wetlands, forestland, waterbodies, farmed land, built-up land, bare land, and mines/quarries) in the C5 Secondary Drainage Region of South Africa over the three periods 1990–2014, 2014–2022, and 1990–2022. Using the South African National Land Cover datasets and the TerrSet liberaGIS v20.03 Land Change Modeller, this research applied post-classification comparison, transition matrices, asymmetric gain–loss metrics, and patch-based landscape analysis to quantify the magnitude, direction, source–sink dynamics, and spatial reconfiguration of LULCC. Results showed that between 1990 and 2014, Shrubland expanded markedly (+49.1%), primarily at the expense of Grassland, Wetlands, and Bare land, indicating bush encroachment and hydrological stress. From 2014 to 2022, the trend reversed as Grassland increased substantially (+261.2%) while Shrubland declined sharply (−99.3%). Forestland also regenerated extensively (+186%) along riparian corridors, and Waterbodies expanded more than fivefold (+384.6 km²). Over the long period between 1990 and 2022, Built-up land (+30.6%), Cultivated land (+16%), Forestland (+140%), Grassland (+94.4%), and Waterbodies (+25.6%) increased, while Bare land (−58.1%), Mines and Quarries (−56.1%), Shrubland (−98.9%), and Wetlands (−82.5%) decreased. Asymmetric analysis revealed strongly directional transitions, with early Grassland-to-Shrubland conversion likely driven by grazing pressure, fire suppression, and climate variability, followed by a later Shrubland-to-Grassland reversal consistent with fire, herbivory, and ecotonal climate sensitivity. LULC dynamics in the C5 catchment show class-specific spatial reconfiguration, declining landscape diversity (SHDI 1.3 → 0.9; SIDI 0.7 → 0.43), and patch metrics indicating urban and cultivated fragmentation, shrubland loss, and grassland consolidation. Based on these quantified trajectories, we recommend targeted catchment-scale land management, shrubland restoration, and monitoring of anthropogenic hotspots to support ecosystem services, hydrological stability, and sustainable land use in ecotonal regions. Full article

Two novel spacetimes are introduced in this work as anisotropic preferred reference frames tailored for applications in relativistic quantum information systems. The resulting anisotropic geometry arises intrinsically from the underlying algebraic structure of spin matrices rather than being imposed through external prescriptions, background fields, or perturbative approximations. The associated Lorentz factors, symmetry group structure and metric-preserving transformations are systematically analyzed within the context of relativistic quantum information theory. Full article

Condition monitoring and diagnosis in a permanent magnet synchronous generator (PMSG) are crucial for ensuring its service continuity and reliability. Recent advancements have introduced innovative, non-invasive techniques for detecting mechanical and electrical faults in this machine. This paper proposes a novel application of the discrete wavelet transform and binary classifiers for diagnosing interturn short-circuit faults in a PMSG with high accuracy and low computational burden. The objective of fault diagnosis is to detect the presence of an interturn short-circuit fault (fault vs. no-fault) under different fault severities and operating speeds. Multiple binary models were trained separately for each fault scenario. The three-phase currents from the PMSG are processed using the discrete wavelet transform to extract features, which are then fed into a binary classifier based on a Random Forest algorithm. Optimization techniques are used to improve the performance of the binary classifiers. Experimental results obtained under various stator fault conditions in the PMSG are presented. Metrics such as accuracy and confusion matrices are used to evaluate the performance of binary classifiers. Full article

Optimizing the “production–living–ecological” space (PLES) is critical for achieving the UN Sustainable Development Goals (SDGs), particularly in ecologically sensitive mountainous border regions. This study investigates the spatial patterns and dynamic evolution of PLES in Lincang City (2010–2020) to reveal the trade-offs between development and conservation. Methodologically, we proposed a coupling-coordination-based grid-level PLES identification framework. This framework integrates the coupling coordination degree model (CCDM) directly into the functional classification process at a 600 m grid scale—a resolution selected to balance the capture of spatial heterogeneity with the maintenance of functional integrity in complex terrains. Spatiotemporal dynamics were further quantified using transition matrices and a dimension-based landscape metric system. The results reveal that (a) ecological space and production–living–ecological space represent the predominant categories in the study area. During the study period, ecological space continued to decrease, while production–living space increased steadily, and other PLES categories showed only marginal variations. (b) Mutual transitions among PLES types primarily occurred among ecological space, production–ecological space, and production–living–ecological space. These transitions intensified markedly between 2015 and 2020 compared to the 2010–2015 period. (c) From 2010 to 2020, the landscape in Lincang evolved towards lower ecological risk yet higher fragmentation. High fragmentation values, often associated with grassland, cropland, and forested areas, were evenly distributed across northeastern and northwestern regions. Likewise, high landscape dominance and isolation appeared in these regions as well as in the southeast. Conversely, landscape disturbance remained relatively uniform throughout the city, with lower values detected in forested land. Full article

Fungi have emerged as versatile biotechnological platforms for addressing environmental challenges with potential co-benefits for human health. Among them, Pleurotus ostreatus stands out for its ligninolytic enzyme systems (notably laccases), capacity to valorize lignocellulosic residues, and ability to form functional mycelial materials. We conducted an evidence-mapped review, based on a bibliometric analysis of the Scopus corpus (2001–2025; 2085 records), to characterize research fronts and practical opportunities in environmental remediation and sustainable bioprocesses involving P. ostreatus. The mapped literature shows sustained growth and global engagement, with prominent themes in: (a) oxidative transformation of phenolic compounds, dyes and polycyclic aromatic hydrocarbons; (b) biodegradation/bioconversion of agro-industrial residues into value-added products; and (c) development of bio-based materials and processes aligned with the circular bioeconomy. We synthesize how these strands translate to real-world contexts, reducing contaminant loads, closing nutrient loops, and enabling low-cost processes that may indirectly reduce exposure-related risks. Key translational gaps persist: standardization of environmental endpoints, scale-up from laboratory to field, performance in complex matrices, life-cycle impacts and cost, ecotoxicological safety, and long-term monitoring. A practical agenda was proposed that prioritizes field-scale demonstrations with harmonized protocols, integration of life-cycle assessment and cost metrics, data sharing, and One Health frameworks linking environmental gains with plausible health co-benefits. In conclusion, P. ostreatus is a tractable platform organism for sustainable remediation and bio-manufacturing. This evidence map clarifies where the field is mature and where focused effort can accelerate the impact of future research. Full article

Perfluorooctane sulfonate (PFOS), a persistent and bioaccumulative perfluoroalkyl substance, poses significant environmental and human health risks due to the extraordinary stability of its C–F bonds. Conventional remediation strategies largely fail to achieve mineralization, instead transferring contamination or producing secondary waste streams. In this study, we investigate neutron irradiation as a potential destructive approach for PFOS remediation in both solid and aqueous matrices. Samples were exposed to thermal neutrons (flux: 3.2 × 10⁹ n·cm⁻²·s⁻¹, 0.0025 eV) at the Argonauta reactor for 6 h. Raman and FTIR spectroscopy revealed that PFOS in powder form remained largely resistant to degradation, with only minor structural perturbations observed. In contrast, aqueous PFOS solutions exhibited pronounced spectral changes, including attenuation of C–F and S–O vibrational signatures, the emergence of carboxylate and carbonyl functionalities, and enhanced O–H stretching, consistent with radiolytic oxidation and partial defluorination. Notably, clear peak shifts were predominantly observed for PFOS in aqueous solution after irradiation (overall displacement toward higher wavenumbers), whereas in powdered PFOS the main spectral signature of irradiation was the attenuation of CF₂ and S–O related bands with comparatively limited band relocation. To evaluate the biological relevance of these structural alterations, cell viability assays (MTT) were performed using human umbilical vein endothelial cells. Non-irradiated PFOS induced marked cytotoxicity at 100 and 50 μg/mL (p < 0.0001), whereas neutron-irradiated PFOS no longer exhibited significant toxicity, with cell viability comparable to the control. These findings indicate a matrix-dependent response: neutron scattering in solids yields negligible molecular breakdown, whereas radiolysis-driven pathways in water facilitate measurable PFOS transformation. The cytotoxicity assay demonstrates that neutron irradiation promotes sufficient molecular degradation of PFOS in aqueous media to suppress its cytotoxic effects. Although complete mineralization was not achieved under the tested conditions, the combined spectroscopic and biological evidence supports neutron-induced radiolysis as a promising pathway for perfluoroalkyl detoxification. Future optimization of neutron flux, irradiation duration, and synergistic catalytic systems may enhance mineralization efficiency. Because PFOS concentration, fluoride release (F⁻), and TOC were not quantified in this study, remediation was assessed through spectroscopic fingerprints of transformation and the suppression of cytotoxicity, rather than by mass-balance mineralization metrics. This study highlights neutron irradiation as a promising strategy for perfluoroalkyl destruction in contaminated water sources. Full article

The widespread use of antibiotics in human medicine, veterinary care, and livestock production has resulted in their frequent detection in diverse environmental and food matrices, making continuous surveillance of antibiotic residues in food products essential for consumer protection. In this study, a sustainable analytical method based on dispersive liquid–liquid microextraction (DLLME) coupled with UHPLC–MS/MS was developed for the trace determination of sulfamethoxazole, sulfadimethoxine, and enrofloxacin in commercial cow milk and chicken eggs. A natural deep eutectic solvent (NADES) composed of anisaldehyde and octanoic acid (2:1, molar ratio) was employed as a biodegradable extraction solvent, and key extraction parameters were systematically optimized. Under optimized conditions, the method demonstrated excellent linearity (R² ≥ 0.9982), recoveries of 89.5–98.7%, and RSDs ≤ 6.04%. Application to 44 commercial samples from the Saudi market revealed sulfamethoxazole as the most frequently detected antibiotic, occurring in 90% of egg samples (2.17–13.76 µg kg⁻¹) and 70.8% of milk samples (0.26–26.67 µg L⁻¹). A comprehensive evaluation using ten metrics confirmed the method’s greenness, practicality, analytical performance, and innovation. Overall, the proposed NADES–DLLME–UHPLC–MS/MS approach offers a rapid, cost-effective, and environmentally friendly alternative for routine monitoring of antibiotic residues in food matrices. Full article

Background: Tuberculosis (TB) is a significant global health issue, particularly in resource-limited regions where radiological expertise is constrained. This project aims to develop a scalable deep learning system that safeguards privacy and achieves high accuracy in the early identification of tuberculosis using chest X-ray images. The objective is to implement federated learning with an adaptive-weighted ensemble optimised by a Genetic Algorithm (GA) to address the challenges of centralised training and single-model approaches. Method: We developed an ensemble learning method that combines multiple locally trained models to improve diagnostic consistency and reduce individual-model bias. An optimisation system that autonomously selected the optimal ensemble weights determined each model’s contribution to the final decision. A controlled augmentation process was employed to enhance the model’s robustness and reduce the likelihood of overfitting by introducing realistic alterations to appearance, geometry, and acquisition conditions. Federated learning facilitated collaboration among universities for training while ensuring data privacy was maintained during the establishment of the optimal ensemble at each location. In this system, just model parameters were transmitted, excluding patient photographs. This enabled the secure amalgamation of global data without revealing sensitive clinical information. Standard diagnostic metrics, including accuracy, sensitivity, precision, F1 score, AUC, and confusion matrices, were employed to evaluate the model’s performance. Results: The proposed federated, GA-optimized ensemble demonstrated superior performance compared with individual models and fixed-weight ensembles. The system achieved 98% accuracy, 97% F1 score, and 0.999 AUC, indicating highly reliable discrimination between TB-positive and typical cases. Federated learning preserved model robustness across heterogeneous data sources, while ensuring complete patient privacy. Conclusions: The proposed federated, GA-optimized ensemble achieves highly accurate and robust early tuberculosis detection while preserving patient privacy across distributed clinical sites. This scalable framework demonstrates strong potential for reliable AI-assisted TB screening in resource-limited healthcare settings. Full article

This paper develops a quantitative framework for climate–financial risk measurement that combines a spatially explicit jump–diffusion asset–loss model with prudentially aligned risk metrics. The approach connects regional physical hazards and transition variables derived from climate-consistent pathways to asset returns and credit parameters through the use of climate-adjusted volatilities and jump intensities. Fat tails and geographic heterogeneity are captured by it, which conventional diffusion-based or purely narrative stress tests fail to reflect. The framework delivers portfolio-level Spatial Climate Value-at-Risk (SCVaR) and Expected Shortfall (ES) across scenario–horizon matrices and incorporates an explicit robustness layer (block bootstrap confidence intervals, unconditional/conditional coverage backtests, and structural-stability tests). All ES measures are understood as Conditional Expected Shortfall (CES), i.e., tail expectations evaluated conditional on climate stress scenarios. Applications to bank loan books, pension portfolios, and sovereign exposures show how climate shocks reprice assets, alter default and recovery dynamics, and amplify tail losses in a region- and sector-dependent manner. The resulting, statistically validated outputs are designed to be decision-useful for Internal Capital Adequacy Assessment Process (ICAAP) and Pillar 2: climate-adjusted capital buffers, scenario-based stress calibration, and disclosure bridges that complement alignment metrics such as the Green Asset Ratio (GAR). Overall, the framework operationalises a move from exposure tallies to forward-looking, risk-sensitive, and auditable measures suitable for supervisory dialogue and internal risk appetite. Full article

This paper presents a PRISMA-ScR scoping review of 20 digital maturity assessment tools in the architecture, engineering, and construction (AEC) sector and wider business domains. The objective is to compare key features including assessment matrices, maturity dimensions, completion time, accessibility, and platform availability. The review follows predefined eligibility criteria and a structured screening process to identify and analyse tool frameworks in terms of scope, metrics, platform type, usability, and focus areas. Most tools primarily target the AEC sector, while some address broader organisational digital transformation. Common maturity areas include technology, organisation, data management, and processes. The analysis highlights limitations such as underemphasis on people, strategy, policy, skills development, standards, and financial resources, as well as the fragmented integration of maturity components. A combined bottom-up (scoring) and top-down (dimension structuring) approach is recommended for future tool development. The review provides insights for practitioners when selecting tools and proposes guidelines for creating more comprehensive and integrated maturity models. Full article

This study proposes a novel high-performance computational framework to address the computational challenges in probabilistic large-deformation landslide analysis. By integrating a GPU-accelerated material point method (MPM) solver with a parallelized covariance matrix decomposition (CMD) algorithm for decomposing symmetric matrices, the framework achieves exceptional efficiency, demonstrating speedups of up to 532× (MPM solver) and 120× (random field generation) compared to traditional serial methods. Leveraging this efficiency, extensive Monte Carlo simulations (MCSs) were conducted to quantify the effects of spatial variability in soil properties on landslide behaviors. Quantitative results indicate that runout and influence distances follow normal distributions, while sliding mass volume exhibits log-normal characteristics. Crucially, deterministic analysis was found to systematically underestimate the hazard; the probabilistic mean sliding volume significantly exceeded the deterministic value, with 73–80% of stochastic realizations producing larger failures. Furthermore, sensitivity analyses reveal that increasing the coefficient of variation (COV) and the cross-correlation coefficient (from −0.5 to 0.5) leads to a monotonic increase in both the mean and standard deviation of large-deformation metrics. These findings confirm that positive parameter correlation amplifies failure risk, providing a rigorous physics-based basis for conservative landslide hazard assessment. Full article

We propose a general methodology for constructing dynamic performance indicators (or strength metrics) in any sport that relies on comparative outcomes among competitors, using chronological positional data. Specifically, we develop a family of strength indicators for harness trotting races based on time-weighted, head-to-head results. Using the official Balearic trotting records (1990–2023), we construct win, draw, and confrontation matrices up to each event and apply a triweight kernel to reduce the influence of older results. From these matrices, we derive a family of five bounded, interpretable indicators on the interval $[0,1]$: an overall average win rate, a category-adjusted version, and three distance-specific versions (short, medium, and long). Indicator validation is performed via predictive validation, employing regularized logistic regression models (Elastic Net) based on indicator differences between horse pairs. Standard metrics (accuracy, calibration, discrimination, and Brier score) are used for the validation analysis. The results confirm that the indicators are coherent, stable, and interpretable, demonstrating that the generic construction procedure yields robust outcomes. We conclude that these indicators establish a solid and easily updatable foundation for developing dynamic ranking systems and practical selection/handicap procedures in trotting. Full article

Forest cover dynamics strongly influence ecological integrity and resource sustainability, particularly in ecotonal landscapes, where vegetation is highly sensitive to climate variability, long-term climate change, and anthropogenic disturbances. This study examined Forest Land (FL), representing all areas of dense, canopy-forming woody vegetation with forest-like structure, aggregated from SANLC classes, in relation to eight other land cover classes across three periods: 1990–2014, 2014–2022, and 1990–2022. The study used South African National Land Cover datasets and the TerrSet–LiberaGIS Land Change Modeller to quantify changes in magnitude, direction, and source–sink relationships. Analyses included post-classification comparison to determine spatial changes, transition matrices to identify land-cover conversions, and asymmetric gain–loss metrics to reveal sources and sinks of forest change. The result shows that between 1990 and 2014, forests remained marginal and fragmented in the eastern central part of the study area, while shrubland increased from 40.4% to 60.2% at the expense of grasslands, cultivated land, bare land, wetlands, and forest land. From 2014 to 2022, FL regeneration was pronouncedly increased from 2% to 6%, especially along riparian corridors and reservoir margins, coinciding with shrubland decline (99.3%) and grassland recovery (261.2%). Over the entire 1990–2022 period, FL increased from 2.4% to 6% expanding into bare land, cultivated land, grassland, shrubland, and wetlands. Asymmetric analysis indicated that forests acted as a sink during the first period but as a source of ecological resilience in the second and final. These findings demonstrate strong vegetation feedback to hydrological and anthropogenic drivers. Overall, the findings underscore the potential for forest recovery to enhance biodiversity, ecosystem services, carbon storage, and hydrological regulation, while identifying priority areas for riparian conservation and integrated catchment management. Full article

Domain adaptation methods have been extensively studied for rolling bearing fault diagnosis under various conditions. However, some existing methods only consider the one-way embedding of original space into a low-dimensional subspace without backward validation, which leads to inaccurate embeddings of data and poor diagnostic performance. In this paper, a rolling bearing fault diagnosis method based on multi-source domain joint structure preservation transfer with autoencoder (MJSPTA) is proposed. Firstly, similar source domains are screened by inter-domain metrics; then, the high-dimensional data of both the source and target domains are projected into a shared subspace with different projection matrices, respectively, during the encoding stage. Finally, the decoding stage reconstructs the low-dimensional data back to the original high-dimensional space to minimize the reconstruction accuracy. In the shared subspace, the difference between source and target domains is reduced through distribution matching and sample weighting. Meanwhile, graph embedding theory is introduced to maximally preserve the local manifold structure of the samples during domain adaptation. Next, label propagation is used to obtain the predicted labels, and a voting mechanism ultimately determines the fault type. The effectiveness and robustness of the method are verified through a series of diagnostic tests. Full article