Search Results (500)

We present a geodesic dynamics framework for discrete-time state evolution on the unit sphere $S^{N-1}$ that maintains exact unit-norm constraints through Riemannian exponential mapping. Given an input sequence and an initial state, the method constructs trajectories by projecting inputs to tangent spaces and updating states along geodesics, incorporating temporal memory via approximate parallel transport of velocity directions. Unlike traditional approaches requiring post hoc normalization of linear updates, the geodesic formulation preserves $\parallel {\mathbf{x}}_t\parallel =1$ to machine precision while eliminating explicit $N\times N$ transition matrices in favor of $D\times N$ input embeddings when the intrinsic input dimension D is much smaller than the ambient dimension N. The update corresponds to a first-order exponential integrator on the sphere. We establish local Lipschitz continuity of the exponential map on positively curved manifolds with careful treatment of basepoint dependence, derive perturbation bounds showing linear-to-exponential growth transitions via Grönwall-type estimates, and we prove third-order asymptotic equivalence with normalized linear systems under appropriate scaling. Numerical experiments on synthetic data validate exact norm preservation over extended time horizons, confirm theoretical perturbation growth predictions, and demonstrate the effectiveness of the temporal memory mechanism in reducing long-horizon prediction errors. The framework provides a principled geometric approach for applications requiring exact directional or compositional constraints. Full article

This study delves into how the literature conceptualizes banking efficiency as a capability shaping sustainability-oriented pathways under conditions of systemic uncertainty, including recurrent economic–financial disruptions and geopolitical shocks. Using records indexed in the Web of Science Core Collection, the study combines bibliometric mapping with conceptual structuring to examine publication dynamics, collaboration networks, and the thematic evolution of research linking bank efficiency, green finance intermediation, sustainable digital innovation, and risk governance. The study reveals a multidimensional knowledge base organized around two converging streams: (i) research on efficiency, stability, and crisis transmission emphasizing intermediation quality, performance under stress, and prudential responses; and (ii) sustainability and innovation scholarship focusing on how financial systems enable eco-innovation diffusion and low-carbon transition through capital allocation, governance mechanisms, and digitally enabled transformation. Across these streams, banking efficiency is increasingly discussed not merely as a performance ratio, but as a strategic capability that becomes particularly salient in crisis environments: it can reduce intermediation frictions when funding conditions tighten, strengthen screening and monitoring of green projects amid elevated uncertainty, and support the continuity and scaling of eco-innovations by improving decision speed and resource allocation through digital tools. Collaboration patterns indicate growing interdisciplinary engagement—especially among European and Asian institutions—where crisis, sustainability, and innovation perspectives are integrated into systems-based approaches to green finance. Building on these insights, the article outlines a research agenda oriented toward innovation outcomes in turbulent contexts, emphasizing (a) measurement strategies that connect efficiency to eco-innovation diffusion and adoption rates during stress periods; (b) comparative analyses of how policy incentives and green market signals interact with bank efficiency across crisis episodes; and (c) hybrid methodological designs combining econometric identification, network analytics, scenario-based stress framing, and AI-enabled analytical tools to capture nonlinear dynamics in efficiency–innovation linkages. Overall, the study clarifies how banking efficiency may condition the capacity of financial institutions to sustain green investment intermediation and advance eco-innovation pathways when uncertainty is systemic rather than episodic. Full article

This article addresses a major challenge in circular economy accounting: assessing the social dimension, particularly social ties, which are often immaterial and difficult to capture. It examines a case study of how a local project managing organic waste and unsold goods fosters social ties in a priority urban neighborhood in France, and how these dynamics can be apprehended through an alternative qualitative accounting approach. The study draws on an ethnographic case of the MatOrGa project, combining participant observation, semi-structured interviews, discourse grounded analysis, and actor and flow mapping. Situated within counter-accounting and critical accounting, the research emphasizes social ties that extend beyond purely economic logic, spanning social, ecological, and economic dimensions. The new concept of counter-accounting utterances is introduced to describe empirical accounts that make visible practices, relationships, and social effects often overlooked in conventional accounting and sustainability reporting. The study shows how ethnography can function as a form of counter-accounting, producing qualitative representations of social impact that resist standardization. The findings advance social and sustainability accounting by offering a situated and reflexive approach to assessing the social impact of circular economy initiatives, while also opening the way for context-sensitive non-financial reporting. Full article

To address the limitations of static and coarse-grained analysis in mining online tourism reviews, this study proposes a gated attention-based multi-model fusion framework for dynamic topic evolution and complaint-driven latent issue pattern mining. Using 300,000 reviews from Ctrip and Meituan, we fuse global semantics from Sentence-BERT with attention (SBERT-Attention), local features from Bidirectional Encoder Representations from Transformers–Text Convolutional Neural Network (BERT-TextCNN), and topic distributions from the Biterm Topic Model (BTM) via a learnable gating mechanism. The fused model achieves an F1-score of 92.3% in review classification. We partition the corpus quarterly and apply Uniform Manifold Approximation and Projection (UMAP) followed by K-means++ clustering to the fused vectors, yielding interpretable topics, including Scenery, Transportation, Amenities, Management, Culture, and Value for Money, and enabling dynamic topic discovery over time. River map visualizations and negative review analysis reveal seasonal evolution patterns and recurring complaint patterns associated with specific topics. The framework enables dynamic, interpretable semantic mining, advancing intelligent processing of short-text user content and offering a generalizable approach for temporal knowledge discovery in smart tourism and beyond. Full article

Land use/land cover (LULC) change significantly influences a range of environmental and socio-economic issues, including climate change, deforestation, biodiversity loss, soil degradation, ecosystem services, and food security, at local, regional, and global levels. In the northwestern Himalayan region, particularly in Rajouri district of Jammu and Kashmir (J&K), LULC change has profound environmental and socio-economic implications. Understanding the temporal and spatial dimensions of LULC change is crucial for assessing the impact of human activities on the region’s environment. The present study aimed to analyze LULC change in Rajouri district of J&K, India over a 30-year period from 1990 to 2020 and to project future LULC dynamics for the next 30 years up to 2050. Landsat imagery with a supervised classification technique was used for classification and generation of LULC maps. Moreover, CA Markov model was used to predict the future LULC status of the area. The model validation exhibited strong performance, with Kappa statistics exceeding 0.90, indicating a high level of reliability in the projections. The results indicate considerable changes in different land use classes from 1990 to 2020. Over the 30-year period, dense forest showed the maximum reduction of about −20.69 Km², followed by open forest (−15.87 Km²) and grassland (−13.75 Km²). Wasteland showed the maximum increase of about +28.24 Km², followed by built-up (+17.90 Km²) and cropland (+12.50 Km²). The cumulative impact of deforestation from 1990 to 2020 amounts to approximately 43.17 Km², while afforestation efforts only managed to reclaim 6.61 Km² of land. The future prediction using the CA Markov model suggests further changes in LULC patterns, with built-up, cropland, and wasteland projected to increase exponentially by 2050, accompanied by sharp declines in forests. Therefore, policymakers should prioritize sustainable land management and forest conservation strategies to mitigate the potential negative impacts of LULC changes on the environment, ensuring balanced and sustainable development. Full article

This paper studies constrained model reference adaptive control (MRAC) for a planar unmanned ground vehicle (UGV) subject to actuator limits and safety requirements. First, we establish a double-integrator model by applying dynamic feedback linearization to a nonholonomic kinematic model with acceleration input, while simultaneously accounting for external disturbances. A constrained MRAC scheme is developed that enforces constraints at two levels: (i) actuator constraints are guaranteed by saturating the physical inputs after mapping the adaptive virtual control through the inverse kinematic transformation, and (ii) safety constraints are enforced via componentwise control barrier function (CBF) on the tracking error, which induces explicit bounds on the plant state. A projection-based adaptive law is introduced to keep parameter estimates bounded and to ensure well-posedness under saturation-induced mismatch. Moreover, we propose a sufficient feasibility condition that explicitly relates safety margins, disturbance bounds, and available actuator authority, thereby forming a guideline for feasible region design. Simulation studies demonstrate that the proposed method achieves constraint-satisfying tracking under bounded disturbances while respecting physical actuator constraints. Full article

The thermal consequences of industrial land transformation remain underexplored in rapidly urbanizing regions of Bangladesh. This study presents a novel approach of how extensive industrial expansion in Narayanganj, a major manufacturing hub dominated by textile, knitwear and dyeing industries, has altered land surface temperature (LST) dynamics over the past three decades, including its variation across classes, relationships with biophysical indices and future patterns. Landsat 5 TM and Landsat 8 OLI imagery from 1991, 2007, and 2023 were utilized to map LULC using winter-season images through supervised classification, while multi-seasonal thermal bands were used to derive LST. LST variations were further evaluated using cross-sectional profiles across different land cover types, and correlations were examined with indices including the greenness index (NDVI), moisture index (NDMI), built-up index (NDBI), and barrenness index (NDBAI). Additionally, a future LST map for 2039 was generated using the cellular automata–artificial neural network (CA-ANN) model. Results show that between 1991 and 2023, built-up area and bare land expanded by 16.72% and 14.15%, while vegetation area and water bodies decreased by 26.62% and 4.25%. Average LST increased from 25.94 °C in 1991 to 28.68 °C in 2023, with projections indicating an additional 2 °C rise by 2039. Cross-sectional analysis found that built-up areas consistently showed the maximum surface temperatures, followed by bare land, vegetation and water bodies. In addition, correlation analysis revealed that LST showed an inverse relation with NDVI and NDMI, while showing a positive relationship with NDBI and NDBAI. These findings show the necessity of sustainable urban planning and green infrastructure to reduce surface heating in rapidly urbanizing areas. Full article

Fractional vegetation cover of crops (CropFVC) is a critical indicator for remote sensing-based crop monitoring. However, existing inversion models are largely developed for general vegetation types, limiting their effectiveness for crop-specific applications. Here, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model. The model was validated with 43343 CropFVC samples of four major crops (winter wheat, rice, maize, and soybean) across China during March to August 2024, spanning key phenological stages, and further compared against SNAP (10 m) and GEOV3 (300 m) products. Results showed that (1) the proposed model achieved stable performance across diverse canopy structures, with average RMSE < 9.3% for wheat, rice, maize, and soybean; (2) compared with SNAP (10 m), RMSE decreased by 4.83%, 3.10%, 7.51%, and 8.63% for wheat, rice, maize, and soybean, respectively; compared with GEOV3 (300 m), reductions reached 7.88%, 9.49%, 13.63%, and 19.75%, respectively. Further observations showed that the model-derived CropFVC captured intra-field variability and abnormal crop conditions well, enabling more accurate monitoring of crop-specific FVC dynamics across phenological stages. The proposed operational framework enhances CropFVC estimation by improving canopy structural representation and reducing retrieval bias. By enabling more accurate 10 m CropFVC mapping at the field scale, the crop-specific approach provides practical support for precision agriculture and crop-related food security monitoring. Full article

Background: Existing XR simulators for enteral feeding rely mainly on self-reported learning outcomes and procedural checklists. As a result, they offer limited opportunities to capture objective behavioral data or to present dynamic patient reactions. This two-stage pilot study evaluated an XR-based gastrostomy tube-feeding simulator (ESTE-TF) that integrates sensor-derived performance metrics and two biological-reaction presentation modalities (projection mapping and tablet display). Methods: In Experiment 1, nursing students completed pre- and post-experience questionnaires assessing perceived learning across seven domains, alongside sensor-based measurements of feeding-start timing, dosing-rate characteristics, and total procedure time. Experiment 2 employed a tablet-based version with four learning items assessed for students and post-experience evaluations collected from registered nurses. Participants also compared the two XR presentation methods. Results: Students demonstrated perceived learning gains of small-to-large magnitude across both experiments (Experiment 1: d = 0.455–0.974; Experiment 2: d = 0.014–0.886), with wide 95% confidence intervals reflecting the exploratory nature of this pilot work. Sensor-derived data showed greater dosing-rate variability and longer procedure times among students than nurses. Participants reported that projection mapping offered a more embodied experience, whereas tablet displays provided clearer visibility. Conclusions: These findings indicate the feasibility and preliminary educational potential of integrating sensing technologies with XR-based biological-reaction presentation for gastrostomy-feeding training. Given the small samples and non-validated measures, results should be interpreted as exploratory. Future research will refine sensor accuracy, establish standardized performance metrics, and evaluate learning outcomes using validated instruments and controlled study designs. Full article

Precise Estimated Time of Arrival (ETA) prediction in Terminal Maneuvering Areas (TMA) constitutes a prerequisite for efficient arrival sequencing and airspace capacity management. While data-driven approaches outperform kinematic models, conventional Recurrent Neural Networks (RNNs) exhibit limitations in modeling complex multi-aircraft spatial interactions and lack the capability to quantify predictive uncertainty. Conversely, Spiking Neural Networks (SNNs) enable energy-efficient event-driven computation, yet their applicability to continuous trajectory regression is hindered by “input starvation,” where normalized state vectors fail to induce sufficient neural firing rates. This study proposes a Probabilistic Spiking Transformer (PST) architecture to integrate neuromorphic sparsity with global attention mechanisms. An Adaptive Spiking Temporal Encoding mechanism incorporating learnable linear projections is introduced to resolve the regression-spiking incompatibility, facilitating the autonomous mapping of continuous trajectory dynamics into sparse spike trains without heuristic scaling. Concurrently, a Distance-Biased Multi-Aircraft Cross-Attention (MACA) module models air traffic conflicts by weighting spatial interactions according to physical proximity, thereby embedding separation constraints into the feature extraction process. Evaluation on large-scale real-world ADS-B datasets demonstrates that the PST yields a Mean Absolute Error (MAE) of 49.27 s, representing a 60% error reduction relative to standard LSTM baselines. Furthermore, the model generates well-calibrated probabilistic distributions (Prediction Interval Coverage Probability > 94%), offering quantifiable uncertainty metrics for risk-based decision support while ensuring real-time inference suitable for operational deployment. Full article

Effective collective motion hinges on the seamless transfer of local information, yet vision-based mechanisms, while potent for generating rapid consensus, are inherently fragile. Visual links can be severed instantly by occlusions, leading to a phenomenon characterized as “sensory amnesia.” Seeking to fortify this vulnerability, Pheromone-Modulated Body Orientation Change (PM-BOC) is introduced as a dual-channel framework that fuses transient visual cues with a persistent environmental memory. Rather than treating these inputs in isolation, motion salience is quantified via BOC and mapped onto a decaying virtual pheromone field, dynamically modulating interaction weights by coupling instantaneous visual projections with local pheromone concentrations. This strategy effectively constructs a temporal buffer, bridging the informational voids left by blind spots. Validation, spanning from systematic physics simulations to high-fidelity simulations with a swarm of 50 UUVs, reveals that PM-BOC sustains superior cohesion in obstacle-laden environments where baseline visual models falter. Notably, this coupling suppresses high-frequency sensory noise while inducing resilient, scale-free velocity correlations that scale linearly with system size. By reconciling the trade-off between the immediacy of visual responsiveness and the robustness of environmental memory, this study offers a scalable paradigm for engineering resilient swarm systems capable of navigating the uncertainties of perception-limited environments. Full article

Under global climate change, the Tibetan Plateau, as a sensitive and ecologically vulnerable region, exhibits vegetation dynamics that significantly influence regional ecological security and hydrological cycles. This study aims to project the dynamic changes in vegetation on the Tibetan Plateau under climate change and assess the associated uncertainties in projections. Coupled Model Intercomparison Project Phase 6 (CMIP6) models were used to provide climate change outputs in the future under different greenhouse gas emission scenarios. The vegetation dynamics were described by the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data. By integrating a Random Forest model with the output climate data of CMIP6 models and training the model based on the historical observation data, NDVI changes under future emission scenarios were simulated and evaluated. The key findings of this study are as follows: (1) The multimodel ensemble (MME) performed best in simulating environmental variables, while certain individual models showed significant deviations in simulating specific variables; the Random Forest model demonstrated reliable capability in NDVI simulation and prediction. (2) The future NDVI was projected to increase persistently in the central and eastern plateau but decrease along the northern and southeastern margins, with variability in the trend projections between different models. (3) The MME model indicated an overall NDVI increase in the future, with higher values under SSP245 before the 2060s and stronger increases under SSP585 thereafter; humid basins exhibited more pronounced increases, while arid/semiarid basins showed limited changes. (4) The uncertainty in the NDVI projections showed a sustained increasing trend under both scenarios, with a stronger rise under the SSP585 scenario; spatially, the uncertainty remained low across most of the Tibetan Plateau but was relatively higher in the central–eastern region and major humid basins. These results provide a scientific basis for understanding alpine ecosystem responses to future climate change and for regional ecological risk management. Full article

Large-scale terrestrial laser scanning (TLS) point clouds are increasingly used for applications such as digital twins and cultural heritage documentation; however, removing unwanted human points captured during acquisition remains a largely manual and time-consuming process. This study proposes a geometry-aware framework for automatically removing human noise from TLS point clouds by projecting 2D instance segmentation masks (obtained using You Only Look Once (YOLO) v8 with an instance segmentation head) into 3D space and validating candidates through multi-stage geometric filtering. To suppress false positives induced by reprojection misalignment and planar background structures (e.g., walls and ground), we introduce projection-followed geometric validation (or “geometric gating”) using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and principal component analysis (PCA)-based planarity analysis, followed by cluster-level plausibility checks. Experiments were conducted on two real-world outdoor TLS datasets—(i) Osaka Metropolitan University Sugimoto Campus (OMU) (82 scenes) and (ii) Jinaimachi historic district in Tondabayashi (JM) (68 scenes). The results demonstrate that the proposed method achieves high noise removal accuracy, obtaining precision/recall/intersection over union (IoU) of 0.9502/0.9014/0.8607 on OMU and 0.8912/0.9028/0.8132 on JM. Additional experiments on mobile mapping system (MMS) data from the Waymo Open Dataset demonstrate stable performance without parameter recalibration. Furthermore, quantitative and qualitative comparisons with representative time-series geometric dynamic object removal methods, including DUFOMap and BeautyMap, show that the proposed approach maintains competitive recall under a human-only ground-truth definition while reducing over-removal of static structures in TLS scenes, particularly when humans are observed in only one or a few scans due to limited revisit frequency. The end-to-end processing time with YOLOv8 was 935.62 s for 82 scenes (11.4 s/scene) on OMU and 571.58 s for 68 scenes (8.4 s/scene) on JM, supporting practical efficiency on high-resolution TLS imagery. Ablation studies further clarify the role of each stage and indicate stable performance under the observed reprojection errors. The annotated human point cloud dataset used in this study has been publicly released to facilitate reproducibility and further research on human noise removal in large-scale TLS scenes. Full article

The “Great Bend” of the Yellow River, a region characterized by the tension between ecological fragility and economic growth, faces dual pressures from physical water scarcity and stringent policy redlines. Traditional allocation models often struggle to operationalize the rigid boundaries of the “Four Determinants” policy (water determines production, city, land, and population) and suffer from computational inefficiencies under high-dimensional non-linear constraints. To address these issues, this study proposes a policy-driven “Four-Determinant, Three-Multiple” (FDTM) rigid constraint optimization framework. First, a multi-level boundary system is constructed based on water-carrying capacity, thereby converting the policy into dynamic interaction constraints among industry, city, land, and population. Second, to overcome potential computational bottlenecks, an Improved Adaptive Cheetah Optimization Algorithm (IA-COA) is developed. By integrating chaos mapping initialization and an adaptive penalty function mechanism, the algorithm exhibits enhanced global search capability and convergence speed within confined search spaces. Using Baotou City as a representative case study, the model simulates scenarios for the 2030 planning horizon. The results indicate that (i) the integration of rigid constraints effectively identifies development bottlenecks, capping projected water demand at 1.075 × 10⁹ m³ and preventing ecological overdraft despite a 5.15% theoretical deficit; (ii) through IA-COA optimization, a balanced trade-off between economic benefits and ecological security is achieved. The comprehensive water supply guarantee rate increased to over 90%, and satisfaction levels for all sectors exceeded 0.8, demonstrating improved allocation efficiency. This study elucidates the marginal transformation mechanism of the water–economy–ecology nexus under rigid constraints and demonstrates the applicability of IA-COA in solving complex basin allocation problems constrained by strict boundaries. It provides a methodological reference for sustainable water management in similar resource-stressed arid regions. Full article

To address the strong nonstationarity and complex multi-source interference in vibration signals of bogie axle-box bearings, a fault diagnosis method combining Improved Empirical Wavelet Transform (IEWT) and a Convolutional Neural Network (CNN)–Transformer model is proposed. First, the vibration signals are decomposed using the IEWT method, where dynamic frequency-band division adaptively determines the decomposition bands. This yields multiple intrinsic mode functions, and key modes containing fault features are selected based on information entropy. Next, the selected key modes are fused and transformed into polar coordinate projection maps, further enhancing the distinctiveness of fault data features. Finally, CNN is employed to extract local features from the vibration signals, while the Transformer captures long-range dependencies through the self-attention mechanism, significantly improving feature modeling for complex signals. To validate the fault diagnosis performance of the IEWT and CNN–Transformer model, vibration signals from bogie axle-box bearings in urban railways are analyzed. Analysis of the experimental data suggests that the adaptive decomposition of bearing signals using IEWT effectively overcomes the fixed band boundary limitations of traditional EWT, enhancing the precision of signal feature extraction. The integration of polar coordinate projection maps more accurately illustrates frequency variations and amplitude differences in the signals, fully capturing their nonstationary characteristics. Among the five fault categories of bogie axle-box bearings, the proposed method achieves an accuracy of 99.46%, a recall rate of 99.52%, and an F1-score of 0.995, significantly outperforming five classic comparison methods. This demonstrates that the combined strengths of CNN and Transformer yield higher classification accuracy and better robustness in handling complex fault patterns, effectively solving the fault diagnosis challenges for bogie axle-box bearings. Full article