Search Results (868)

Endogenous safety and security (ESS), which advocates for designing systems that are inherently safe and secure by nature, has emerged as a pivotal paradigm for addressing the inherent vulnerabilities of information systems. The Dynamic Heterogeneous Redundancy (DHR) architecture serves as its typical implementation by introducing dynamic, heterogeneous, redundant executors with equivalent function (EF) into the information system. However, the functional equivalence property explicitly connects the system’s output to that of the individual executors, thereby creating potential security risks that adversaries could exploit. In addition, EF-DHR faces an inherent contradiction between functional equivalence and heterogeneous implementations (HIS), leading to high engineering costs and limited applicability. To address these problems, this paper proposes the Non-Equivalent Functional DHR (NEF-DHR) architecture, leveraging function secret sharing (FSS) theory to replace EF executors with NEF components, which fundamentally eliminates the EF-HIS contradiction. Specifically, we propose the concept of `terminal executor output information entropy loss’ to formalize the risk of output information interception by adversaries and theoretically prove that NEF-DHR improves unpredictability and resistance to attacks. Experimental results further validate that NEF-DHR exhibits lower error rates under various attack levels, with enhanced robustness and superior ESS performance. Additionally, we generalize the DHR architecture based on three core properties (indistinguishability, output recoverability, verification) and classify ESS into three types with corresponding DHR variants. This work advances the application of entropy theory in ESS and provides a novel entropy-enhanced solution for the large-scale deployment of DHR security systems. Full article

Background: Hippotherapy, a sensorimotor-rich intervention proposed for children with Autism Spectrum Disorder (ASD), is suggested to influence executive function (EF). However, the underlying electrophysiological mechanisms, particularly changes observed in resting-state Electroencephalography (EEG), remain underexplored. Methods: A total of forty-eight children with ASD, aged 9–12 years, participated in this quasi-experimental, non-randomized pre-test–post-test study. Participants were assigned to either a standardized 12-session hippotherapy program (n = 24) or a waitlist Control group (n = 24). EF was evaluated pre- and post-intervention using validated measures: the Wisconsin Card Sorting Test, Stroop Color–Word Test, Corsi Block-Tapping Task, and Tower of London. Resting-state EEG data (19 channels, 250 Hz) were recorded before and after the intervention and analyzed for spectral power, pairwise Pearson correlation, phase-based functional connectivity using the Phase Lag Index (PLI), and directed effective connectivity using Phase Transfer Entropy (PTE). EEG effects were tested with linear mixed models in MATLAB (fitlme), with the measured values in each ROI as the dependent variable, group and time as fixed effects, and SubjectID included as a random intercept; EF outcomes were analyzed with ANCOVA/MANCOVA, adjusting post-test scores for baseline. The assumptions of homogeneity of slopes, Levene’s test, and the Shapiro–Wilk test were examined, and the Holm–Bonferroni correction together with partial η² effect sizes were reported. Results: Following baseline adjustment, the hippotherapy group showed substantial and statistically significant improvements across all EF measures compared with controls partial η² range = 0.473–0.855; all adjusted p < 0.001; e.g., Stroop Incongruent Reaction Time (F(1,45) = 265.80, p < 0.001, ηp² = 0.855). EEG analyses revealed localized Group × Time interaction effects involving frontal delta power as well as selected alpha-, theta-, and beta-band connectivity measures within frontally anchored networks. In addition to these focal interaction effects, the hippotherapy group exhibited a narrower distribution of pre–post EEG changes across spectral power and connectivity metrics compared with controls, indicating greater temporal consistency in resting-state electrophysiological dynamics across sessions. Because group allocation was non-random (based on scheduling feasibility and parental preference), results should be interpreted as associations rather than causal effects. While the hippotherapy group exhibited significant EF improvements and relative stabilization in EEG spectral and connectivity metrics, particularly in frontal delta/theta/alpha/beta bands, a direct mapping between individual EEG changes and behavioral gains was not observed. Conclusions: A standardized 12-session hippotherapy program was associated with substantial improvements in EF and with relative stabilization of resting-state electrophysiological dynamics in children with ASD. However, the direct mechanistic link between these EEG and behavioral changes warrants further investigation. Larger randomized trials employing active control conditions, task-evoked electrophysiological measures, and extended longitudinal follow-up are needed to confirm efficacy, clarify mechanisms, and establish the durability of effects. Full article

There are numerous broadband resonance phenomena during the operation of new energy grid-connected systems. Therefore, the performance and adaptability of resonance suppression strategies for different resonance scenarios are of great significance. This paper proposed a comprehensive evaluation method based on the entropy weight method to assess the performance and robustness of resonance suppression strategies for photovoltaic (PV) grid-connected systems. Corresponding performance indicators were constructed considering the dynamic response characteristics of PV grid-connected systems. The six suppression strategies were comparatively analyzed in terms of performance and robustness under three scenarios: the LCL (inductor–capacitor–inductor)-type PV grid-connected system, the PV grid-connected system with SVG, and the newly built PV grid-connected system with SVG. This work effectively evaluates the performance and robustness of different suppression strategies, identifies the deficiencies of individual strategies, and provides a theoretical basis for designing flexible resonance suppression strategies with parameter adaptability. Full article

Urban areas across Europe are undergoing rapid morphological transformations driven by densification, redevelopment, and infrastructure expansion. Monitoring these urban changes requires operational, harmonized, and reproducible approaches grounded in Earth Observation. This study presents a Copernicus use case demonstrating how the High-Resolution Layer Imperviousness Change (2015–2018) and Urban Atlas datasets can be integrated with the Guidos Toolbox (GTB) to quantify structural urban change across six metropolitan areas (Milan, Sofia, Riga, Warsaw, Viseu, Santander). Morphological Spatial Pattern Analysis (MSPA) and entropy-based indicators were applied to characterize land take, fragmentation, compaction, and internal reorganization of impervious surfaces. The combined framework captured both configurational morphology and spatial disorder, revealing divergent development patterns: pronounced heterogeneity and fragmentation in Sofia, stabilization or compact growth in Milan, Warsaw, and Santander, controlled densification in Riga, and localized intensification without outward expansion in Viseu. All analyses rely on openly accessible Copernicus data and open-source tools, ensuring full reproducibility and transferability. Outputs were disseminated through a FAIR-compliant geoportal developed within a Copernicus FPCUP project, supporting transparency and reuse. The findings underscore the value of Copernicus services for operational urban monitoring and provide a scalable methodology to support European land-use policies, including the Zero Net Land Take 2050 target and the EU Soil Strategy. Full article

In the context of the dynamical systems of classical mechanics, we introduce two new notions called “algorithmic fine-grain and coarse-grain entropy”. The fine-grain algorithmic entropy is, on the one hand, a simple variant of the randomness tests of Martin–Löf (and others) and is, on the other hand, a connecting link between description (Kolmogorov) complexity, Gibbs entropy and Boltzmann entropy. The coarse-grain entropy is a slight correction to Boltzmann’s coarse-grain entropy. Its main advantage is its less partition-dependence, which is because algorithmic entropies for different coarse grainings are approximations of one and the same fine-grain entropy. It has the desirable properties of Boltzmann entropy in a wider range of systems, including those of interest in the “thermodynamics of computation”. It also helps explain the behavior of some unusual spin systems arising from cellular automata. Full article

Living systems are open nonequilibrium systems that continuously exchange energy, matter, and information with their environments, leading to stochastic dynamics with memory and active fluctuations. In this study, we derive a microscopic non-Markovian description of entropy dynamics in living systems using the Keldysh functional formalism, providing a quantitative foundation for the entropy bathtub picture. The approach naturally incorporates colored environmental noise, memory-dependent dissipation, and many-body interactions, yielding generalized Langevin dynamics and non-Markovian master equations. Within this framework we derive an exact frequency-domain expression for the entropy production rate and show that violations of the fluctuation–dissipation relation provide a direct thermodynamic signature of active biological fluctuations. We further demonstrate that environmental memory enhances low-frequency fluctuations and entropy production, leading to critical slowing down near dynamical instability. These results provide a microscopic physical foundation for the entropy “bathtub” picture of living systems and connect entropy evolution with development, aging, and death in nonequilibrium dynamics. Full article

The large-scale integration and grid connection of renewable energy sources and charging stations introduce a multitude of nonlinear and impact loads, resulting in more severe distortion and higher complexity of disturbance signals in power systems. As a consequence, power quality disturbances (PQDs) in active distribution networks, including overvoltage and harmonics, display greater randomness and diversity, which increases the challenge of PQD identification. To tackle this problem, this study presents a dual-channel early-fusion approach for PQD recognition based on Spatial Post-MultiScale Fusion Entropy (SMFE). SMFE is used as an entropy-based feature-construction pipeline in which a time–frequency representation is formed prior to spatial post-multiscale aggregation to produce a compact complexity map complementary to waveform morphology. Subsequently, a dual-channel model is constructed by integrating waveform-morphology input with SMFE-derived complexity features for joint learning. By leveraging the ConvNeXt architecture and a Squeeze-and-Excitation (SE) mechanism, a multimodal channel-recalibration model is implemented to emphasize informative feature responses during PQD recognition. Experimental verification with simulated signals shows that the proposed approach achieves an identification accuracy of 97.83% under an SNR of 30 dB, indicating robust performance under the tested noise settings. Full article

This paper presents a conceptual architecture for an AI-Driven Adaptive Encryption Device (AI-AED), a tri-modular hardware platform embodied in a registered industrial design. The device integrates a Secure Input Module, an AI-Enhanced Central Processing Unit with biometric authentication, and a Secure Output Module connected by unidirectional buses. We formalise the adaptive encryption policy as a constrained Markov decision process (CMDP) over a discrete action space of 216 cryptographic configurations, with safety constraints that provably prevent convergence to insecure states. A formal threat model based on extended Dolev–Yao assumptions with four physical access tiers defines attacker capabilities, and anti-downgrade safeguards enforce a monotonically non-decreasing security floor during threat escalation. An information-theoretic analysis shows that adaptive algorithm selection contributes an additional entropy term H(α) to ciphertext uncertainty, upper-bounded by log₂(|L_enc|) ≈ 1.58 bits, while noting this represents increased attacker uncertainty rather than a strengthening of any individual cipher. A component-level latency model estimates 0.91–1.00 ms pipeline latency under normal operation and 3.14–3.42 ms under active threat, including integration overhead. Simulation validation over 1000 episodes compares a tabular Q-learning baseline against the proposed Deep Q-Network operating on the continuous state space: the DQN achieves 82% fewer constraint violations, 6× faster threat response, and more stable policy switching, demonstrating the advantage of continuous-state reinforcement learning for safety-critical adaptive encryption. All claims are positioned as theoretical contributions requiring empirical validation through prototype implementation. Full article

Coral reefs in the South China Sea (SCS) are critical for regional marine biodiversity and ecosystem services but face escalating threats from climate change and anthropogenic stressors. However, a holistic evaluation of habitat suitability spanning the distinct environmental gradients from low-latitude deep-water atolls to high-latitude marginal reefs remains limited. This study utilized high-resolution remote sensing data and the MaxEnt (Maximum Entropy) model combined with Principal Component Analysis (PCA) to systematically map potential habitat suitability and elucidate the multi-scale environmental drivers shaping the realized niche of SCS corals. The results revealed significant spatial heterogeneity characterized by a distinct “High South, Low North” latitudinal gradient, with Unsuitable areas dominating 85.5% of the study region, followed by Marginally Suitable habitats at 5.0%, while the northern Nansha Islands were identified as the core distribution area with the highest suitability and continuity. Minimum Phosphate (Min. Phos.) concentration and Sea Surface Temperature (SST) were identified as the core environmental factors determining the spatial distribution of coral reefs in the South China Sea. The optimal environmental ranges were identified as: SST between 28.52 °C and 29.41 °C, water depth shallower than 34 m, extremely low phosphate (0–0.005 mmol/m³), and low cumulative thermal stress (DHW < 0.83 °C-weeks). Crucially, PCA further quantified two potential climate refugia: low-latitude thermal refugia in the southern Nansha Islands, characterized by high environmental stability, and high-latitude marginal refugia in the Beibu Gulf, which offer physical buffering against warming, while necessitating targeted efforts to mitigate the risks of habitat degradation and eutrophication driven by intensifying anthropogenic activities These findings challenge the traditional conservation view relying solely on high-latitude migration, advocating for a climate-resilient spatial planning strategy that prioritizes strict protection of southern biodiversity source banks while enhancing the connectivity of northern marginal stepping stones. Full article

Non-parametric information geometry has long faced an “intractability barrier”: in the infinite-dimensional setting, the Fisher–Rao metric is a weak Riemannian metric functional that lacks a bounded inverse, rendering classical optimization and estimation techniques computationally inaccessible. This paper resolves this barrier by building the statistical manifold on the Orlicz space $L_0^{\mathsf{\Phi }}\left(P_f\right)$ (the Pistone–Sempi manifold), which provides the necessary exponential integrability for score functions and a rigorous Fréchet differentiability for the Kullback–Leibler divergence. We introduce a novel Structural Decomposition of the Tangent Space ($T_{f}M=S\oplus S^{\perp }$), where the infinite-dimensional space is split into a finite-dimensional covariate subspace (S)—representing the observable system—and its orthogonal complement ($S^{\perp }$). Through this decomposition, we derive the Covariate Fisher Information Matrix (cFIM), denoted as $G_f$, which acts as the computable “Hilbertian slice” of the otherwise intractable metric functional. Key theoretical contributions include proving the Trace Theorem ($H_G\left(f\right)=\mathrm{Tr}\left(G_f\right)$) to identify G-entropy as a fundamental geometric invariant; demonstrating the Geometric Invariance of the Covariate Fisher Information Matrix (cFIM) as a covariant $(0,2)$-tensor under reparameterization; establishing the cFIM as the local Hessian of the KL-divergence; and characterizing the Efficiency Standard through a generalized Cramer–Rao Lower Bound for semi-parametric inference within the Orlicz manifold. Furthermore, we demonstrate that this framework provides a formal mathematical justification for the Manifold Hypothesis, as the structural decomposition naturally identifies the low-dimensional subspace where information is concentrated. By shifting the focus from the intractable global manifold to the tractable covariate geometry, this framework proves that statistical information is not a property of data alone, but an active geometric interaction between the environment (data), the system (covariate subspace), and the mechanism (Fisher–Rao connection). Full article

As a key spatial platform for implementing China’s Northeast Revitalization Strategy, coordinated development of production–living–ecological (PLE) functions in the Changchun Metropolitan Area is crucial for high-quality regional development. This study uses 24 counties (districts) in the metropolitan area as analytical units and develops a quantitative indicator system to evaluate PLE functions. We integrate the entropy-weighted TOPSIS method, social network analysis (SNA), and geographically and temporally weighted regression (GTWR) to examine the spatiotemporal dynamics, spatial correlation networks, and driving mechanisms of the three functions from 2013 to 2023. Temporally, the production function follows a growth–decline–recovery trajectory, the living function increases overall despite fluctuations, and the ecological function strengthens continuously. Overall, the three functions increasingly exhibit coupling and synergy. Spatially, the production function concentrates in core areas and diffuses along major axes. The living function is led by the core and followed by county-level catch-up. The ecological function is higher in the east, relatively stable in the west, and connected by corridors, together forming a multi-center, axis-based synergistic pattern. In the spatial correlation networks, densities of the production and ecological networks remain largely stable, whereas the living network becomes markedly denser. The three networks display distinct topologies and continue to evolve structurally. For driving mechanisms, the GTWR model provides the best fit. Geographic proximity positively contributes to the formation of all three functional networks, while the eight explanatory factors show pronounced spatiotemporal heterogeneity. These findings provide an evidence base for optimizing functional coordination and implementing differentiated spatial governance in metropolitan areas. Full article

In response to problems inherent in conventional hydraulic engineering education including compartmentalized courses, fragmented knowledge delivery, overlapping and omitted content, and insufficient development of students’ integrated practical competencies this study develops an instructional model for a coordinated curriculum group based on the OBE-BOPPPS teaching theory. The curriculum cluster model aims to integrate interdisciplinary course content, restructure curriculum structure hierarchy, eliminate disciplinary barriers, and establish clear stratified and interrelated knowledge relationships. The model centers on competency development, constructing a three-dimensional “agent–objective” system that connects “teacher–student–curriculum” with “knowledge–competency–literacy.” It further establishes a multi-indicator evaluation system encompassing teachers, students, and courses. The comprehensive evaluation employing Principal Component Analysis, Entropy Weight Method, and CRITIC method demonstrates that the curriculum group teaching model significantly outperforms traditional course-based instruction in transcending disciplinary boundaries, enhancing knowledge systematicity, improving teaching precision, and strengthening knowledge acquisition as well as students’ comprehensive competencies. This approach achieves dynamic optimization and precision feedback in the teaching process, effectively facilitating the systematic transfer of knowledge and the holistic development of students’ innovative practical abilities. It thereby provides a scientific pathway and empirical support for the reform of hydraulic engineering education and the cultivation of high-quality talent. Full article

Urban integration can lower average pollution, yet environmental benefits may be unevenly shared across cities within the same urban agglomeration. Such within-agglomeration disparities can weaken joint prevention and control, sustain unequal health risks, and hinder inclusive urban sustainability even when overall concentrations fall. Using a panel of Chinese metropolitan areas from 2005 to 2023, we examine whether metropolitan integration is associated with a more even distribution of pollution burdens among constituent cities. We measure within-agglomeration inequality using entropy-based indices for total emissions and emissions intensity, and capture integration intensity using cumulative policy attention and the years since integration began. We find that deeper integration is associated with lower pollution inequality, with larger reductions for inequality in total emissions than for inequality in emissions intensity. The decline emerges after integration begins and persists over time, and it remains robust to alternative measures and to an identification strategy that leverages predetermined historical connectivity. The equalizing association is most evident in metropolitan areas featuring high-primacy and high-ranking core cities, is reinforced by greater fiscal capacity and factor market integration, and is moderated by industrial lock-in. These results suggest that metropolitan integration, when supported by credible cross-city coordination and transition support in regions facing industrial lock-in, can promote cleaner and more equitable environmental outcomes within urban agglomerations. Full article

Shannon’s differential entropy for continuous variables suffers from a fundamental limitation: it is not invariant under scale transformations. This makes entropy values dependent on the choice of measurement units rather than reflecting intrinsic properties of distributions. While Jaynes proposed the limiting density of discrete points (LDDP) as a theoretical solution, a concrete method for computing the required invariant measure has been lacking. This paper establishes a rigorous connection between Kullback–Leibler divergence and the invariant measure, providing theoretical proofs of invariance under affine transformations and a practical computational method. We prove that entropy normalized by the median of k-nearest neighbor distances is invariant under affine transformations (Theorems 1 and 2). The non-negativity of the resulting entropy has been validated empirically across all tested distribution families, though a complete theoretical proof remains an open question. This approach extends naturally to multivariate settings, enabling scale-invariant mutual information estimation. We provide open-source implementations in Julia (EntropyInvariant.jl) and Python (entropy_invariant) and demonstrate their advantages over traditional approaches, particularly for variables with disparate scales. Full article

In autonomous driving, motion sickness (MS) arises from physical or visual stimuli, or a combination of both. However, objective quantification of MS level (MSL) remains limited beyond questionnaire-based assessments. Using multimodal human signals (physiological and behavioral) collected in an autonomous driving simulator, this study addresses the association between these signals and MSL, across these MS types, by (i) screening and curating a decade of human-signal MS studies (HS-Set) to establish a data-driven foundation for selecting target sensor domains and features, (ii) constructing a dataset with subjective measures of MSL (fast motion sickness scale and simulator sickness questionnaire (SSQ)), alongside human signals (electroencephalogram (EEG), photoplethysmogram (PPG), electrodermal activity (EDA), skin temperature, and head/eye movement), (iii) conducting a correlation analysis between MSL and the identified features from HS-Set, and (iv) quantifying multivariable contributions at the feature and sensor domains through an explainable boosting machine (EBM). Key correlations include head amplitude/energy (pitch/surge) with SSQ total/oculomotor, eye entropy with nausea/oculomotor (positive), and EDA with nausea (negative). The EBM-based contribution analysis highlights EEG connectivity and head kinematics as dominant contributors; excluding EEG, the interpretability of single-domain models remains limited. Additionally, a combination of Head, PPG, and EDA domains retains over 80% of the full model’s interpretability. Full article