Search Results (101)

The shift from fifth-generation (5G) systems to new architectures based on the sixth-generation (6G) paradigm changes network slicing from a semi-static resource partitioning model to a fully dynamic Slice-as-a-Service (SlaaS) model. This model is characterized by instantiating, scaling, migrating, and terminating slices using cloud-native orchestration frameworks, which offer considerable operational flexibility at the cost of increased attack surface. The current security design, which is mainly based on authentication-based security and conventional isolation design principles that are standardized by the 3rd Generation Partnership Project (3GPP), fails to consider the risks of runtime behavioral drift, cross-slice lateral movement, as well as metadata inference in a multi-tenant environment of SlaaS in 5G-Advanced and 6G networks. This paper introduces Trust-Aware Security Orchestration (TASO), a probabilistic, runtime-responsive security scheme for SlaaS in 5G-Advanced and 6G. The TASO models treat cut integrity as a posterior trust probability based on multidimensional telemetry flows. Trust is updated via Bayesian inference, and its temporal dynamics are studied using the Markov stability model to ensure convergence and bounded behavior. The structure also integrates entropy-based monitoring controls to reduce privacy leakage during telemetry collection. Large-scale multi-tenant simulations with NS-3 yield statistically significant results compared to baselines of statistically isolated and machine-learning-only. TASO has a 94.6% detection rate, 96.2% smaller isolation attacks, 93.6% smaller inference leaks, and SLA-conformant latency. The findings confirm that probabilistic trust modeling is a potential, theoretically sound security mechanism for dynamic slicing in future 6G systems. Full article

Continuous dequantization embeds discrete data into a continuous space, but relaxation can alter the statistical information carried by the original categories. We study a complementary regime in which a specified weighted discrete law is the target and the dequantizer is required to be lossless under a prescribed quantizer. Any partition-respecting, parameter-independent kernel produces a continuous experiment Blackwell equivalent to the original discrete one, so likelihood ratios, maximum-likelihood estimators, score and Fisher information, Bayes posteriors, and optimal risks are unchanged before downstream approximation. We then characterize what happens after a finite-capacity continuous model is fitted. A Kullback–Leibler (KL) chain rule separates continuous approximation error into the categorical error of the requantized model plus a within-cell shape term; the information-preservation guarantee therefore does not extend automatically to an arbitrary fitted downstream model. We also provide a plug-in analysis for empirically estimated weights, showing that dequantization preserves rather than removes the discrete estimation error. Within each cell, maximizing entropy minus a displacement penalty yields a truncated Gibbs kernel. The apparent concentration and geometric-scale parameters reduce exactly to one effective concentration, $\alpha =\lambda /s_c^2$. We further derive exact transport costs, a sharp $W_1=\Theta \left(h^2\right)$ reconstruction rate for the special reconstruction-from-exact-bin-masses setting, leakage-specific and geometry-perturbation bounds, and a bounded-domain multidimensional formulation. A finite-candidate Monte Carlo selector satisfies a nonasymptotic $O\left(m^{-1/2}\right)$ oracle inequality. Experiments verify the identities and show that exact dequantization can materially reduce requantized error when a smooth continuous downstream model is imposed; this is a conditional representational advantage, not a claim that dequantization dominates an unrestricted categorical model. Full article

In Non-Intrusive Load Monitoring (NILM), adaptive segmentation of electricity consumption time series is critical for appliance recognition. However, prevailing methods face challenges including heuristic parameter tuning, boundary sensitivity, and metric saturation. This paper proposes BayesSeg, a unified framework integrating time-series segmentation, multi-dimensional evaluation, and automatic parameter optimization. The segmentation layer employs a dual steady-state criterion based on the tail value and mean of preceding subsequences, combined with a sequential extraction and complement-set parsing strategy, to achieve precise unsupervised partitioning of steady-state and transition-state segments. The evaluation layer maps segmentation results to binary state sequences and formulates a composite metric integrating an event-level F1 score (event_F1) with Normalized Mutual Information (NMI). The event_F1 quantifies switching-event precision and recall via tolerance matching, while NMI captures global structural consistency, jointly overcoming the boundary sensitivity and limited discriminability of pointwise metrics. In the optimization layer, the composite score serves as the objective function for Bayesian optimization, which constructs a TPE surrogate model for efficient global parameter-space exploration. Experiments on the SustDataED2 dataset demonstrate that Bayesian optimization requires only ~100 objective evaluations to locate a parameter region within 0.35% deviation of the exhaustive grid-search optimum. The framework achieves a weighted composite score of 0.7149 and an event_F1 of 0.9340 while reducing optimization latency from ~5300 s to under 1 s, a speedup exceeding 5700×. BayesSeg automates segmentation configuration and provides a scalable, efficient solution for time-series analysis in NILM and related domains. Full article

In the era of the digital economy, data has become a fundamental resource, a critical factor of production, and a key driver of socio-economic development. As the volume of data generated and collected increases, issues concerning data security and privacy protection have gained widespread attention. To enhance data security during storage and retrieval, this study proposes an improved ciphertext index construction scheme based on the Verifiable Delay Function (VDF) and Learning with Errors (LWE). The scheme employs k-flat partitioning to organize multidimensional structured data and constructs group-level ciphertext indexes based on attribute coverage values. VDF-generated salts and attribute-specific LWE keys derived through a Key Derivation Function (KDF) are combined with randomized encryption to reduce the correlation leakage associated with deterministic indexes. During retrieval, the server performs homomorphic subtraction on ciphertext indexes, while the client determines equality by comparing the resulting noise with a decision threshold. Role-based access control (RBAC) is incorporated to enforce attribute-level key isolation and unauthorized-access rejection. Experimental results demonstrate that the proposed scheme achieves effective ciphertext equality queries without directly exposing plaintext index values, while maintaining acceptable computational overhead. Full article

Velocity distributions in curved open-channel flows exhibit strong three-dimensionality and nonlinear behavior, posing challenges to both accurate prediction and physical interpretation. Using measured velocity data from nine discharge–water-depth combinations in a laboratory 180° open-channel bend, this study developed an integrated eXtreme Gradient Boosting (XGBoost)–SHapley Additive exPlanations (SHAP) framework, with multiple linear regression (MLR), random forest (RF), and a back-propagation neural network (BPNN) used for comparison. Leave-one-condition-out cross-validation was used to evaluate the predictive accuracy and stability of the four models. A stratified sampling strategy was then adopted to construct the training dataset, allowing information from all flow regimes to contribute to robust parameter calibration; the two data-partitioning strategies yielded broadly comparable predictive performance. Using models trained with stratified sampling, multidimensional model evaluation was further conducted using global statistical metrics, segment-wise predictive performance, held-out extreme-condition tests, and measured–predicted agreement, among other criteria, with XGBoost consistently showing the best performance. Multilevel SHAP analyses quantified global feature importance, pairwise interactions, streamwise variations in feature contributions, SHAP–PDP dependence relationships, and condition-specific attribution. The SHAP results indicate a two-level attribution structure in the model: hydraulic variables jointly define the global velocity baseline, and their contribution signs can switch between positive and negative. Spatial variables characterize cross-sectional velocity redistribution. Strong discharge–depth interaction is associated with width-to-depth-ratio-dependent adjustment of the bend flow field. The proposed framework establishes a complete experiment-driven prediction–mechanism interpretation workflow for sharply curved open-channel flow and provides new quantitative insight into model-represented multifactor hydrodynamic interactions in open-channel bends. Full article

Background: WDBC is a small historical benchmark, and near-ceiling performance alone provides limited evidence of transportability. Methods: We evaluated five model families for discrimination, calibration, and paired statistical testing. We utilized sensitivity-oriented out-of-fold (OOF) thresholds and decision curve analysis (DCA) and conducted an independent TOMPEI-CMMD methodological replication (larger two-center mammography cohort with biopsy-confirmed diagnoses; final cohort: 1358 patients, 1380 breasts; held-out: 272 patients, 279 breasts). Results: Across 20 additional stratified WDBC partitions, ROC-AUC remained consistently high (model means 0.988–0.995), but the criterion-specific nominal leader changed across partitions. Held-out TOMPEI ROC-AUC ranged from 0.775 to 0.804. No model demonstrated statistically significant superiority in ROC-AUC or frozen-threshold balanced accuracy after patient-cluster paired bootstrap comparison and Holm correction. The independent methodological replication yielded more moderate discrimination than the WDBC benchmark. Frozen sensitivity-oriented OOF thresholds transported reasonably but not uniformly. Conclusions: Multi-dimensional evaluation is more defensible than nominal AUC ranking. Neither the WDBC benchmark results nor the TOMPEI methodological replication establish population screening performance or clinical deployment readiness; further prospective clinically representative validation is required. Full article

Understanding the ecological drivers shaping animal foraging and interactions remains a central question in ecology. Among the most studied systems in this field are the ant–plant interactions, although disentangling the complexity of factors acting in different contexts remains a relevant question. This study investigated how habitat structure, liquid food rewards, and interspecific competition interact to modulate the foraging patterns of the abundant ant Camponotus termitarius on the rosette-shaped plant Eryngium chamissonis in the Brazilian Pampa, where ant–plant interactions are still poorly studied. We monitored 115 plants across three sampling events, measuring ant foraging, trophobiont abundance, vegetation density, plant size, and local nest distributions, and analyzed the relationships using Piecewise Structural Equation Modeling (pSEM). The pSEM revealed that surrounding vegetation density negatively affected C. termitarius nest density, nest extensions, and hemipteran trophobionts. Conversely, denser vegetation and larger plants favored the aggressive competitor Camponotus rufipes. While trophobiont presence and proximal nesting infrastructure directly facilitated C. termitarius activity, hostplant inflorescences promoted the construction of nest extensions on plants. We conclude that C. termitarius foraging is regulated by a multidimensional network where microhabitat complexity mediates spatial niche partitioning and competitive dynamics between sympatric ants. Full article

Shipbuilding task packages are commonly defined from professional experience, which can produce heterogeneous work content within packages, dense coordination interfaces across packages, and uneven labor-time allocation. This study formulates task-package partitioning as a multi-objective combinatorial optimization problem with multi-dimensional engineering features and labor-time constraints. Four features of ship intermediate products—construction stage, structural type, spatial area, and functional system—are used to construct a model that increases intra-package cohesion, reduces inter-package coupling, and improves workload balance. A collaborative method combines genetic-algorithm global search with budget-constrained branch-and-bound refinement of boundary tasks. In a case containing 68 construction tasks from an 11,000 DWT bulk carrier, GA-B&B achieved a mean composite evaluation of 0.8403 over five independent runs, improvements of 18.9%, 3.4%, and 3.0% over a manual-rule baseline, pure GA, and NSGA-II, respectively. Its hypervolume was 0.6% higher than that of NSGA-II, while the number of nondominated solutions was reduced by 97.1%. The method improved partition quality and reduced the number of candidate schemes requiring engineering review, although local refinement increased computational cost. It therefore provides quantitative support for task release, crew organization, and labor-time allocation. Full article

Vegetation–hydrothermal trend patterns in semiarid mountains are inherently multidimensional across altitudinal gradients, requiring probabilistic frameworks to characterize overlapping vegetation–hydrothermal trend patterns. We develop a Gaussian mixture model (GMM)-based framework to classify vegetation trend regimes from multidimensional trend variables and to characterize their climate–regime associations across elevation gradients in the semiarid Al Baha region, Saudi Arabia. Landsat time-series data (2014–2025) were used to derive trends in the Normalized Difference Vegetation Index (NDVI), normalized difference water index (NDWI), and land surface temperature (LST) using the Mann–Kendall test. These standardized trend variables were integrated within a probabilistic GMM framework, with candidate models evaluated using Bayesian information criterion (BIC), Integrated Completed Likelihood (ICL), classification entropy, resampling-based partition stability, and held-out predictive performance. Although BIC favored the eight-component VVV model, the six-component solution was retained as a balanced intermediate-complexity representation based on partition reproducibility, near-optimal predictive performance, classification ambiguity, and parsimony. The results reveal a pronounced elevation-dependent reorganization of vegetation–hydrothermal trends, characterized by High Relative LST Trend dominance at low elevations (~44%), Mixed Trend State at mid elevations (~40.8%), and Low Relative NDWI Trend at higher elevations (>48%), with High Relative LST Trend becoming negligible at the highest altitudes. This spatial organization showed a moderate association with elevation (Cramér’s V = 0.299; χ² = 74,265.54, p < 0.001), with statistical significance interpreted cautiously given the large, spatially autocorrelated sample. Climate–regime analysis further reveals that precipitation variability exhibits the strongest association at mid elevations, whereas temperature shows stronger associations at both low- and high-elevation extremes. Cross-sensor comparison with VIIRS showed substantial classification consistency after class-label alignment (overall agreement ≈ 0.76; κ ≈ 0.64). These findings demonstrate systematic variation in vegetation–hydrothermal trend regimes and their climate associations along elevation gradients. The proposed GMM-based framework provides a scalable and uncertainty-aware approach for assessing climate–vegetation associations in semiarid mountain systems and other data-scarce dryland environments. Full article

The rapid prolife ration of digital twin cities has resulted in an unprecedented increase in the volume, heterogeneity, and dynamics of space-time data, posing significant challenges to scalable storage, efficient indexing, and high-performance query processing in distributed environments. Existing distributed frameworks generally treat data partitioning and indexing as independent processes, making it difficult to simultaneously preserve space-time locality, achieve balanced data distribution, and support efficient multidimensional queries. To address these challenges, this paper proposes a distributed space-time storage and indexing framework based on hierarchical space-time grid encoding. The proposed framework integrates Hadoop distributed file system (HDFS) and HBase to establish a unified architecture for data partitioning, storage organization, indexing, and query processing. Specifically, a space-time grid–based partitioning strategy is designed to preserve space-time locality while maintaining load balance across distributed storage nodes. Furthermore, a hybrid interleaved space-time encoding scheme together with an optimized HBase RowKey is developed to construct a unified space-time index, enabling efficient space-time range queries and space-time k-nearest neighbor (KNN) queries in large-scale distributed environments. Extensive experiments conducted on the T-Drive trajectory dataset demonstrate that the proposed framework consistently outperforms several representative distributed space-time indexing approaches in terms of query efficiency, scalability, and overall system performance while maintaining stable indexing performance under increasing data volumes. The proposed framework provides an efficient and scalable solution for the management and retrieval of massive space-time data and offers a practical infrastructure for digital twin city applications. Full article

Background and Objectives: COPD is a heterogeneous disease in which conventional clinical classifications may not fully capture the complexity of patient profiles. This exploratory study aimed to examine whether multidimensional COPD phenotypes could be identified using unsupervised cluster analysis integrating clinical, functional, radiological and laboratory features. Materials and Methods: We enrolled 161 patients with confirmed COPD evaluated between January 2020 and January 2024. Demographic, clinical, functional, radiological and laboratory findings were collected. Mixed-type data were analyzed using Gower distance and Partitioning Around Medoids (PAM) clustering. The optimal solution was selected by average silhouette width; stability was assessed by 1000 bootstrap resamples and sensitivity analyses. Results: The two-cluster solution had the highest silhouette width (0.184), although separation was modest. Cluster 1 (n = 78) was characterized by greater symptom and exacerbation burden, worse lung function, greater static hyperinflation, shorter 6-min walking distance and more frequent emphysema than cluster 2 (n = 83). Bootstrap resampling indicated internal stability, although concordance with the primary partition varied across sensitivity analyses. After correction for multiple post hoc comparisons, only LAMA/LABA/ICS use differed between clusters, whereas demographic characteristics, comorbidity burden and blood eosinophil levels were comparable. Conclusions: These exploratory findings suggest multidimensional assessment may complement conventional classifications, but external and longitudinal validation is needed before clinical implementation. Full article

Approach channels are affected by sedimentation and scour, resulting in continuous changes in underwater topography. Such processes tend to generate shallow spots and inadequate navigable dimensions, posing safety hazards that undermine both waterway resilience and navigation capacity. To address these issues, this paper proposes a multi-level grid-based spatiotemporal indexing method for bathymetric data, aiming to support resilience-oriented management by improving the effectiveness of bathymetric data management. First, a channel-segment-section partitioning strategy is designed to construct hierarchical progressive grids for the efficient organization of massive bathymetric data. Second, a multi-dimensional spatiotemporal integrated query method is developed to meet diverse analytical and retrieval requirements. Third, a digital depth model (DDM) construction method is introduced that integrates boundary-constrained terrain reconstruction with efficient mesh optimization, enabling underwater terrain representation that adapts to the elongated and irregular morphology of approach channels. The contribution of this work lies not in proposing new individual algorithms but in the tailored integration of these techniques to address the specific challenges of approach-channel bathymetric data. Experimental results demonstrate that the proposed method achieves high construction efficiency across different storage and query schemes. The method enhances the retrieval and analytical capabilities of bathymetric data in representative application scenarios, such as shallow spot identification, critical section analysis, dredging analysis, and erosion–deposition evolution. Consequently, these improvements provide technical support for resilience-oriented channel management and ensure navigational safety. Full article

This study examines how the quality of corporate environmental information disclosure, jointly with governance structure characteristics, shapes the input–output efficiency of green innovation in Chinese A-share listed firms, and whether industry- and region-level conditions moderate that relationship. Drawing on a panel of 2847 firms spanning 2014–2024, we construct a multidimensional disclosure quality index through content analysis across completeness, verifiability, quantification depth, and forward-looking commitment, and measure green innovation efficiency through a super-efficiency slacks-based DEA model accommodating undesirable outputs. A three-level hierarchical linear model partitions variance across firm, industry, and provincial layers and permits the disclosure–efficiency slope to vary with industry regulation intensity and provincial marketization. The results indicate that higher disclosure quality is associated with greater green innovation efficiency, a link we attribute to financing-constraint relief, reputational accumulation, and intensified external monitoring, offered as interpretive channels rather than as separately tested mediators. Board independence, environmentally experienced executives, and institutional shareholding amplify the conversion, while ownership concentration dampens it. Cross-level evidence shows that industry regulation intensity and regional marketization further steepen the firm-level slope. Findings remain stable across alternative measurement, restricted sampling, propensity score matching, and instrumental variable identification. The analysis offers a multilevel reframing of disclosure–innovation research and informs the design of mandatory disclosure rules, governance reform, and green finance infrastructure in transitioning economies. Full article

In the realm of precision agriculture, agricultural UAV path planning is challenging because the UAV must avoid obstacles, follow uneven terrain, and satisfy multiple flight constraints simultaneously. Differential evolution (DE) has been widely adopted for this problem because of its simple structure and effective optimization capability. However, existing DE-based methods often become trapped in local optima and cannot effectively balance exploration and exploitation in complex search environments. To address these issues, this paper proposes a reinforcement learning-based multi-strategy differential evolution algorithm, named PPOMSDE. By introducing Proximal Policy Optimization (PPO) to construct a multi-dimensional state pool and an action pool, PPOMSDE enables adaptive strategies for individuals, improving strategy selection during the search process. An independent multi-buffer is adopted to ensure strict data isolation and efficient learning to avoid strategy confusion. In addition, an adaptive triplet mechanism which partitions the population into fitness-based tiers (best, medium, and worst) assigns different control parameters and mutation strategies to individuals with different fitness levels, improving the balance between global exploration and local exploitation. Extensive experiments on the CEC’2014 and CEC’2017 benchmark suites demonstrate the effectiveness of PPOMSDE. The proposed method achieves the lowest average performance ranks of 1.39 on the combined 10-D and 30-D CEC’2014 benchmarks and 1.03 on the 10-D CEC’2017 benchmarks. In agricultural UAV path planning, PPOMSDE generates safer and smoother flight paths while maintaining accurate terrain-following flight, reducing the overall cost by an average of 22.42% compared with ISDE, L-SHADE, SHADE, and ISHACDE. Full article

To address the limitations of existing approaches that rely primarily on individual operational indicators and provide limited insight into the dynamic evolution of airport operational states, this study develops a two-stage analytical framework integrating unsupervised state identification and supervised temporal classification. Using monthly operational data from seven major Japanese airports between January 2010 and March 2024, the proposed framework first applies K-means clustering to identify latent operational states based on multidimensional indicators reflecting growth dynamics, operational efficiency, and relative operational level. Three distinct states are identified: a low-activity state, a recovery state, and a rapid-rebound state. Here, the rapid-rebound state denotes a temporary operational regime characterized by exceptionally strong year-on-year growth from a comparatively depressed base, rather than the highest absolute utilization rates or 2019-relative operational level. The results reveal clear stage-specific characteristics and transition patterns, with the recovery state serving as the most persistent and relatively stable operational regime, while rapid-rebound episodes are less frequent, less stable, and often transition back to the recovery state. The supervised component subsequently evaluates whether recovery and rapid-rebound labels retrospectively identified through full-sample clustering can be distinguished using lagged operational characteristics. Six classification algorithms, including logistic regression, radial basis function support vector machine (RBF-SVM), random forest, gradient boosting, XGBoost, and LightGBM, are compared using a chronologically ordered 60/20/20 partition applied only at the supervised-classifier stage, with SMOTE applied exclusively to the training subset. Because the standardization parameters, K-means solution, and target state labels were derived from the complete 2010–2024 sample, and because several 2019-relative variables were constructed using an ex post benchmark, the reported results represent classifier-stage temporal evaluation of retrospectively identified full-sample labels rather than end-to-end out-of-sample validation or prospective real-time prediction. Under the prespecified common SMOTE-based six-model comparison, LightGBM yields the highest point estimates for macro-F1 and rapid-rebound-state recall, reaching 0.6864 and 0.5588, respectively. Given that the training subset contains only seven rapid-rebound observations, these estimates should be interpreted cautiously. An exploratory sensitivity analysis using alternative imbalance-handling strategies produces materially different point estimates, indicating that minority-state classification performance is sensitive to the selected imbalance treatment. The SHAP-based feature attribution results indicate that short-term lagged growth indicators and twelve-month 2019-relative operational-level variables contribute strongly to the fitted distinction between the recovery and rapid-rebound state labels. Although the supervised analysis is restricted to distinguishing the recovery and rapid-rebound state labels, the proposed framework provides a multidimensional approach for airport state identification, temporal evolution analysis, retrospective operational monitoring, and post-shock recovery assessment. A genuinely prospective application would require reconstructing all input variables using information available at each forecast origin and re-estimating the models accordingly. Full article