Search Results (5,033)

This paper establishes a rigorous mathematical foundation for modeling scientific research design as a dynamic, decision-centric system. We introduce the Scientific Decision Architecture for Complex Systems (SDA-CS), formalizing research configurations as trajectories within a complete neutrosophic metric space $\mathcal{D}$. By employing the Banach Fixed-Point Theorem, we prove that the research evolution operator $\Psi$ acts as a contraction mapping, ensuring convergence toward a unique, stable methodological state ${\mathrm{V}}_{\mathrm{d}}^{\ast }$ even under conditions of high initial indeterminacy. The framework integrates neutrosophic logic to explicitly characterize indeterminacy ($I$), and local stability is analyzed through the spectral radius of the methodological Jacobian matrix $J_{\Psi }$. Furthermore, we examine the system through information theory, demonstrating that the SDA-CS architecture acts as an entropy-reduction mechanism that promotes information gain by pruning inconsistent decision paths. These theoretical results provide a cybernetic basis for ensuring reproducibility and structural robustness in complex scientific investigations. Full article

Detecting small ship targets in sea–sky background environments is challenging due to interference from clouds, islands, sea clutter, and the limited spatial information in long-range infrared imagery. To address these issues, this paper proposes a robust detection framework that integrates multi-scale spatial feature enhancement with temporal trajectory analysis. First, a candidate target extraction method based on a multi-scale differential histogram of oriented gradients is introduced. By exploiting gradient distribution differences between targets and surrounding backgrounds, our method effectively enhances target responses while suppressing structured background edges. This response is further fused with a log-spectrum-based saliency map to improve target contrast and reduce clutter. Next, a candidate trajectory extraction algorithm based on inverse optical flow matching is developed to utilize temporal consistency. Optical flow-based grayscale compensation predicts target intensity changes between frames, while Kalman filtering estimates motion states and performs trajectory association. Finally, a multi-feature trajectory filtering strategy is designed, combining motion entropy stability, peak signal-to-noise ratio, and trajectory lifecycle to distinguish true targets from false alarms. Experimental results on eight infrared maritime sequences demonstrate superior performance. The proposed method achieves an average Background Suppression Factor (BSF) of 45.2 and an average Signal-to-Clutter Ratio Gain (SCRG) of 22.3 × 10³, representing a substantial improvement over all baseline algorithms. Receiver Operating Characteristic analysis further confirms a mean detection rate exceeding 90% at a false-alarm rate of 10⁻³ across all sequences, confirming improved detection performance and robustness in complex maritime environments. Full article

Proactive defense planning in Industrial Control Systems (ICS) is critically constrained by two intertwined types of uncertainty: epistemic uncertainty, arising from the defender’s limited observability of the system state and incomplete knowledge of attacker strategies, and aleatoric uncertainty, stemming from the stochastic nature of state transitions and the propagation of disturbances through inter-device dependencies. These factors significantly complicate the quantitative assessment of defense strategies before deployment. To address this challenge, this study proposes a Dynamic Bayesian Network (DBN)-based framework that explicitly models four sources of uncertainty. Within this framework, the expectation of the effectiveness differential is coupled with its information entropy to jointly quantify expected performance and prediction uncertainty. A casestudy on a typical substation automation system, complemented by systematic ablation experiments, demonstrates that the framework can effectively distinguish the relative effectiveness of defense strategies. The framework maintains robust assessment results under up to 15% noise in Conditional Probability Tables (CPTs). The ablation experiments further quantify the individual contributions of observability, dependency propagation, and attacker strategy to prediction uncertainty, and reveal a non-trivial coupling between epistemic and aleatoric uncertainty. This research provides theoretical and methodological support for resilience-oriented cyber defense planning in ICS. Full article

Cells operate as open thermodynamic systems where energy transformations and transport processes occur across membranes, exhibiting distinct thermo-electro-biochemical behaviours in healthy versus diseased states. Living organisms generate waste heat due to internal irreversibility, which dissipates into the environment and serves as an observable flow of information. By analysing this heat loss and its changes under external influences, new insights into cellular behaviour can be gained. This paper highlights recent advances in this thermodynamic approach, which frames living systems as black boxes, focusing on their input–output dynamics and introducing the emerging field of bioengineering thermodynamics. A key challenge in applying extremely low-frequency electromagnetic fields (ELF-EMF) to proliferative disorders has been the empirical selection of effective field parameters. To address this, we employed a bio-thermodynamic engineering model to calculate the ELF frequency that maximizes mean entropy changes based on cellular biophysical parameters. This entropy change corresponds to a metabolic shift that reduces cell proliferation. Experimental validation was performed on six human cancer cell lines, where proliferation rates served as indicators confirming the model’s predictions. For the first time, this approach enabled the calculation and experimental validation of ELF frequencies selectively effective on different cell types, demonstrating a promising method for targeted therapeutic applications. Full article

Imprecise probability models generated from data represent epistemic uncertainty by replacing the precise empirical distribution with a set of compatible probability distributions. When this set is described by reachable probability intervals, the induced bounds are tight, so the represented imprecision is not inflated by unattainable interval limits. This paper studies the informational effect of this replacement through the epistemic entropy gap, defined as the difference between the maximum entropy over the induced credal set and the Shannon entropy of the empirical distribution. The gap is a differential quantity: it measures the additional uncertainty introduced by the imprecise model beyond the observed frequencies. We analyze it for three reachable interval models generated from multinomial data: the Imprecise Dirichlet Model, the $ϵ$-contamination model and the approximated Non-Parametric Predictive Inference model. The analysis covers its main properties, its asymptotic behavior and its role in entropy equivalent calibration of model parameters. The results show that the entropy gap offers a common informational scale for comparing how different imprecise models represent the same empirical evidence, and helps interpret the degree of caution associated with limited data reliability and with empirical distributions that may otherwise lead to overconfident uncertainty assessments. Full article

The Gibbs Paradox (concerning the entropy of mixing and entropic extensivity) was explored in depth by Edwin Jaynes (1992). We take up Jaynes’ treatment, considering the special cases for which entropy is (approximately) extensive, and the general case in which it is not. We also explore the Holographic Principle which (strictly speaking) excludes the extensivity of entropy. The formalism of Quantitative Geometrical Thermodynamics shows that, being isomorphic to energy, it is entropy production (not entropy) that is extensive. As a corollary, Shannon information is also not extensive, although information production is extensive. Full article

Road safety is a global priority, and driver behavioral factors are among its most critical determinants. Although the literature has advanced in characterizing driving styles using psychometric instruments such as the Multidimensional Driving Style Inventory (MDSI), an empirical gap persists in the simultaneous integration of latent behavioral profiles, driving exposure, and road safety outcomes, particularly in Latin American contexts and across generational extremes. This study examined the relationship between latent driving style profiles and road safety outcomes among young (18–25 years) and older (≥65 years) licensed drivers in Ecuador, while evaluating the moderating role of driving exposure. A structured survey based on the MDSI was administered to 833 active drivers, and data were analyzed using Latent Profile Analysis (LPA) and binary logistic regression. The six-profile solution was selected according to the Bayesian Information Criterion (BIC = 11,655.07), with acceptable classification quality (entropy = 0.860; minimum posterior probability = 0.808); for inferential parsimony, these profiles were subsequently consolidated into three analytically interpretable categories: Predominantly Careful, Predominantly Risky, and Distress-Reduction. The Predominantly Risky profile was significantly associated with higher odds of traffic accident involvement (OR = 2.76, 95% CI [1.55, 4.93]), whereas the Distress-Reduction profile showed substantially higher odds of receiving traffic infraction fines (OR = 4.74, 95% CI [1.69, 13.34]). The composite driving exposure index was a robust predictor across both models (accident model: OR = 2.82, 95% CI [1.60, 5.14]; fine model: OR = 1.87, 95% CI [1.29, 2.74]). In addition, a significant interaction was observed between the Predominantly Risky profile and driving exposure in the model predicting traffic infraction fines, suggesting that exposure amplified sanction risk within this behavioral category. Older drivers showed a substantially higher representation of the Distress-Reduction profile than young drivers. These findings underscore the utility of person-centered approaches for identifying heterogeneous driver configurations and for designing profile-differentiated road safety interventions; from a practical perspective, these results support the development of targeted road safety programs that integrate behavioral profile screening with exposure-based risk management for young and older drivers. Full article

Rain gauge networks are the core infrastructure for hydrological and water resource monitoring, flood control and disaster mitigation early warning, and water resource planning and regulation. The rationality of their layout directly determines the accuracy, representativeness, and economy of regional precipitation data acquisition. Considering that information entropy can accurately characterize the spatial distribution law and information complexity of rainfall, and spatiotemporal deep learning models have strong capabilities in fitting spatiotemporal features, this paper couples mutual information entropy with a spatiotemporal deep learning model and proposes a novel optimal layout method for rain gauge networks. Daily observed rainfall data from 50 ground-based rain gauges in the upper reaches of the Tuojiang River during 2015–2024, as well as the PERSIANN-CCS remote sensing precipitation product for the same period, were used in the study. A CNN-LSTM spatiotemporal deep learning model integrating spatial features and temporal dependence was constructed, coupled with the mutual information entropy index, and the GA-PSO hybrid optimization algorithm was applied for solution. The superiority of the proposed method was verified by comparison with the calculation results of the traditional mutual information entropy-based greedy optimization algorithm. The results show that the hybrid optimization algorithm driven by the spatiotemporal deep learning model coupled with mutual information entropy is significantly superior to the comparison algorithm in terms of the rationality of the station network structure, the ability to characterize spatial rainfall distribution, the control of average relative error, and the improvement of total information entropy. After optimization, the number of rain gauges in the upper reaches of the Tuojiang River can be reduced from 50 to 25. While greatly reducing the number of stations, the optimized network can still relatively accurately reflect the spatiotemporal characteristics of rainfall in the basin, which can provide a theoretical basis and technical support for the optimal layout of basin rain gauge networks and water resource management. Full article

We investigatewhether a finite local information capacity of spacetime can account for the gravitational phenomena commonly attributed to cold dark matter. Starting from a covariant effective-field-theory description, we modelcoarse-grained entropy deposition as a dynamical scalar field $S\left(x\right)$ whose stress–energy tensor contributes to structure formation. The macroscopic action contains a single dimensionless coupling $\lambda$ multiplying the canonical kinetic term, ensuring ghost-free dynamics and conservation of the associated stress–energy tensor. In a slow-roll regime, defined by a covariant source term $\Gamma \equiv \ddot{S}+3H\dot{S}=0$, where H is the Hubble parameter and overdot denotes derivative with respect to cosmic time, and $|\ddot{S}|\ll H|\dot{S}|$, the entropy sector behaves as pressureless dust at background and in linear order. Implemented in a modified Cosmic Linear Anisotropy Solving System (CLASS) Boltzmann solver, the entropy component fits Planck satellite 2018 cosmic microwave background (CMB) data, baryon acoustic oscillation (BAO) measurements, and the Pantheon + Type Ia supernova sample for $0.5\lesssim \lambda \lesssim 2$, while preserving the linear growth factor to within $0.2\%$ over Euclid space telescope scales. To regulate ultraviolet contributions, we introduce a holographically motivated prescription in which gravitationally active entropy deposition is confined to causal two-surfaces, yielding a $\rho \propto r^{-2}$ halo envelope with a finite-density core determined by local entropy saturation. Fixing the flux scale $\mathcal{A}$ from astrophysical entropy budgets reproduces Milky-Way-mass halos without introducing fine-tuned length scales. Pilot N-body simulations that evolve the entropy field on a staggered grid reproduce the halo mass function down to ${10}^{10.5}{M}_{\odot }$, mitigate the cusp–core and missing-satellite tensions, and remain consistent with cluster lensing constraints. On linear scales, the model predicts percent-level, scale-dependent deviations in the lensing convergence and matter power spectra, testable by Euclid space telescope, the Roman Space Telescope High Latitude Survey, and the CMB-S4 experiment. Full article

Nonlinear memristors frequently contribute to enhancing the dynamical richness of chaotic systems, yet their complexity and flexibility have often been overlooked. In this work, a piecewise non-smooth threshold memristor model is proposed, which is coupled as a nonlinear term into the Sprott C system, yielding a novel four-dimensional memristive chaotic dynamical system. From a theoretical perspective, stability analysis reveals that unstable index-2 saddle-focus equilibrium points are governed by the memristive piecewise parameter, and the topological invariance of the system is verified. In numerical simulations, bifurcation diagrams, Lyapunov exponents, and phase portraits are employed to reveal the mechanism of novel tunable chaotic dynamics. The results demonstrate that memristive coupling strength can induce the system to generate double-scroll, double-wing, and double-butterfly chaotic attractors; the piecewise parameter of the memristor can control the system to produce multi-structure attractors with expanded quantity, and the initial condition of the memristor can regulate the system to generate offset-boosted chaotic attractors. Finally, the novel tunable dynamics is applied to water facility image encryption. Experimental results demonstrate that the proposed algorithm possesses a key space of $2^{100}$, a correlation coefficient of only 0.0002, and information entropy close to the ideal value of eight. The NPCR and UACI reach 99.6161% and 33.4669%, respectively, the key sensitivity is up to ${10}^{-16}$, and all p-values from the NIST tests are greater than 0.01, confirming that the algorithm achieves excellent security performance. Full article

From an information geometric perspective, this study considers a natural foliation of dualistic structures associated with escort distributions of exponential families. We propose an extended divergence on this foliation by continuous-type escort distributions. Specifically, we investigate the foliation formed by escort distributions to analyze the transition of q-parameters, rather than relying on a fixed parameter. Within this foliation, distinct q-parameters and their corresponding dualistic $\alpha$-parameters were defined on each leaf. Finally, we present a decomposition of the extended divergence on this foliation, providing an analog to the method previously established for discrete escort distributions. Full article

Engineering consultants and design firms are central to the success of construction projects. However, the systematic evaluation of their performance in the Saudi Arabian context remains methodologically fragmented and empirically underdeveloped. Existing prequalification frameworks rely predominantly on administrative criteria and single-method ranking approaches that cannot adequately differentiate between high- and low-performing firms. To address this gap, the study proceeds in two distinct parts. Part I—Literature Review: A PRISMA-compliant systematic literature review across five major academic databases was conducted to map the existing evidence base, identify three substantive gaps in the Saudi and GCC engineering firm evaluation literature, and derive a consensus-based set of 29 performance criteria grouped into seven dimensions. This review constitutes an independent contribution: it establishes the gap that motivates the empirical work and provides the criterion framework on which that work is built. Part II—Practical Application: A structured questionnaire was administered to 288 construction professionals in Saudi Arabia (Cronbach’s α = 0.936), and the collected data were analyzed through a hybrid RII–Shannon Entropy Weighting (EWM)–TOPSIS pipeline that produced a Composite Priority Index (CPI) for each criterion, enabling a stable and discriminating ranking that integrates subjective expert consensus with objective distributional information. The main finding revealed that five criteria attained Very High Priority status (CPI > 0.70): Supervisory Experience (CPI = 0.740), Engineers’ Capability Index (CPI = 0.717), License Class (CPI = 0.709), Client Satisfaction Index (CPI = 0.708), and Average Delay Time (CPI = 0.705). These top-ranked criteria collectively center on technical leadership, regulatory standing, client-reported outcomes, and schedule reliability, indicating that procurement decisions should prioritize demonstrable competence over structural size or geographic footprint. The consistently lower importance of physical branch networks and headquarters location further suggests that remote management capabilities and digital coordination tools are reshaping performance expectations under Saudi Vision 2030. The Quality Indicators dimension achieved the highest mean CPI across all seven dimensions. The findings provide actionable evidence for procurement authorities, regulatory bodies, and engineering firms seeking to strengthen performance-evaluation practices in the Saudi construction sector. Full article

Curiosity empowers humans to ask questions about the world and explore it without relying on extrinsic, encouraging rewards such as money. To investigate how this mechanism drives exploration, we implement a curiosity-based approach and test it in a reinforcement learning environment. We define curiosity using a hybrid intrinsic signal based on prediction error and the rarity of state–action pairs. To address the curse of dimensionality in raw pixel inputs, we adopt the Information Bottleneck (IB) principle to learn low-dimensional representations that are both compact and predictive. We introduce two formulations for computing mutual information—one based on entropy decomposition and the other on matrix-based Rényi entropy—and compare their effectiveness. Experiments on Acrobot show substantially improved exploration efficiency over Intrinsic Curiosity Module (ICM), Random Network Distillation (RND), and a k-NN novelty baseline, while results on MountainCar indicate that the proposed method is not uniformly superior in low-dimensional environments. These findings suggest that IB-shaped representations and matrix-based information objectives are most beneficial when observations are high-dimensional or dynamics are structurally complex. Full article

Classification of ground and low-altitude targets in radar remote sensing is challenging because environmental clutter and noise can significantly degrade the discriminability of target echoes, especially under complex outdoor observation conditions. To improve the classification performance for humans, vehicles, and unmanned aerial vehicles (UAVs), this paper proposes a velocity-aware multi-feature fusion method based on measured radar echo data. First, radar echoes are preprocessed using a wavelet-decomposition-based strategy to suppress clutter and noise while preserving useful target information. Then, multiple complementary features, including wavelet packet energy distribution, spectral entropy, spectral standard deviation, temporal standard deviation, amplitude dispersion coefficient, and relative radar cross-section (RCS), are extracted to characterize the target echoes from different perspectives. Considering the influence of target velocity on Doppler distribution and class separability, the measured data are further divided into different velocity intervals for stratified classification. Based on the fused feature vectors, a long short-term memory (LSTM) network is employed to model feature relationships and perform target classification. Experiments conducted on real measured radar echo data demonstrate that the proposed method achieves classification accuracies of 97.82% for UAVs, 96.00% for vehicles, and a mean interval-level accuracy of 96.94%, indicating its effectiveness for ground and low-altitude target classification in cluttered radar remote sensing environments. Full article

The rapid advancement of Generative Artificial Intelligence (Gen-AI) is fundamentally reshaping labor markets, creating an urgent need for higher education institutions to adapt their program capacities and curricula. This paper proposes a data-driven Robust Multi-Objective Planning (RMOP) framework to translate heterogeneous Gen-AI labor shocks into actionable, program-level decisions regarding enrollment scaling and curriculum design. Grounded in O*NET micro-task structures, we model occupational evolution as a dynamic system of substitution, augmentation, and insulation driven by logistic technology diffusion. Our simulations across STEM, trade, and arts occupations reveal sharply divergent trajectories: Information Security Engineers face a 62% total impact dominated by substitution, whereas Electricians retain over 80% insulation, and Musicians experience high exposure but low substitution. To bridge these macro-level forecasts with immediate institutional maneuvers, the framework couples an AI-adjusted Grey Model (GM(1,1)) demand model with a Program Effectiveness Index (PEI) to yield discrete enrollment policy levers (Expand, Contract, and Adjust). For curriculum optimization, we employ Ridge regression to rank employability-related curriculum drivers and NSGA-II to generate Pareto portfolios under competing institutional objectives, including employability, instructional cost, ethics, and environmental impact. Final implementable recommendations are selected through entropy-weighted TOPSIS, where student well-being and education equity are treated as supplementary decision criteria rather than direct prediction targets. In addition, an Automation Risk Score (ARS) and a K-means TC clustering module are used to illustrate potential transfer paths across broader institutional settings. Internal scenario checks show that the AI-adjusted GM(1,1) reduces average hold-out MAPE from 7.0% to 5.8% relative to the baseline GM(1,1), and that NSGA-II achieves slightly stronger Pareto coverage than MOPSO and MODE under the same curriculum-portfolio setting. These checks are interpreted as preliminary decision-support evidence rather than external predictive validation. Overall, RMOP is presented as a scenario-based decision-support framework that links Gen-AI occupational exposure, enrollment adjustment, and curriculum portfolio design. Full article