Search Results (657)

The reinforced concrete (RC) Diaojiao frame structure is a widely used building form in the Luding red bed area. A large area of damage occurred in the Luding earthquake in 2022. It is very important to carry out seismic fragility research for damage evaluation and post-earthquake emergency management. Based on the incremental dynamic analysis (IDA), this paper explores the dynamic response law of the structure: the structural damage is distributed in Floor 1 > Floor 2 > Floor 3, and the damage of the C1_1 component is the most serious. Through the quantitative analysis of the structural damage matrix, the probability of structural damage under frequent earthquakes of 7 degrees and 8 degrees can be ignored. The probability of severe damage (SD) of Floor 1, Floor 2, Floor 3 and the building under maximum considered earthquakes of 9 degrees is 58.25%, 53.03%, 2.71% and 36.79%, respectively. In this paper, PGA is used as an index to divide the damage state into four categories: elastic state, elastic-plastic state, plastic state and large deformation state. Based on the actual earthquake PGA, the structural damage can be determined quickly and accurately, which provides scientific support for the formulation of emergency measures. Full article

This study addresses the challenge of designing an adaptive sliding mode controller for a class of nonlinear Markov jump systems. These systems are characterized by unmeasurable states, partially unknown transition probabilities, and uncertainties arising from matched external disturbances and modeling inaccuracies. In control design and analysis, the nonlinear Markov system in which both the linear term and specific information about the upper bound in the external disturbance term are unknown. To enable descending equivalent sliding mode motion to regulate the dithering phenomenon in a controlled system, an integral sliding surface is established to achieve chattering suppression via descending equivalent sliding motion. A key theoretical contribution is the rigorous proof that the proposed control law ensures both finite-time reachability of the sliding surface and mean-square stability of the closed-loop trajectories. Comparative simulation results demonstrate that the proposed approach achieves a state estimation RMSE of 0.175, which is 48.0% lower than conventional sliding mode control (0.337) and 3.3% lower than observer-based sliding mode control without fault compensation (0.181). The controller reduces control chattering by 75.2% compared to conventional SMC (total variation from 64.4 to 16.0), achieves sliding surface reachability within 0.42s, and maintains effective fault estimation with an average RMSE of 0.138 for time-varying actuator efficiency factors. These quantitative improvements validate the effectiveness of the proposed fault-tolerant mechanism. Full article

In this paper, we propose an analytic deformation acting on probability measures, designed to encompass and extend two fundamental operators in free probability: the $(a,b)$- and the $T_c$-deformations. This unified operator, indicated by ${\mathcal{X}}_{(a,b,c)}$, is introduced through a functional relation for the Cauchy–Stieltjes transform. We have ${\mathcal{X}}_{(a,b,0)}={\tilde{U}}^{(a,b)}$ and ${\mathcal{X}}_{(1,1,c)}=T_c$. We examine the structural properties of this transformation within the setting of Cauchy–Stieltjes kernel (CSK) families, with special emphasis on the behavior of the associated variance functions (VFs). An explicit formula for the VF corresponding to measure deformed by ${\mathcal{X}}_{(a,b,c)}$ is established. This result allows us to demonstrate a key invariance property: the free Meixner class of probability measures remains stable under the ${\mathcal{X}}_{(a,b,c)}$-transformation. Furthermore, a novel characterization of the semicircle law is obtained through the action of ${\mathcal{X}}_{(a,1,c)}$, highlighting the role of symmetry in the deformation and preservation of free-probabilistic distributions. Full article

Prior research on power-law distributions has primarily focused on modeling frequency patterns, with less attention given to rank distributions and how ranked positions reflect relative importance among elements. In discrete power-law distributions, frequency-based metrics often provide limited discrimination in the tail, where elements may exhibit similar counts but differ in relative dominance. These patterns are especially evident, for instance, in academic publishing, where keywords, affiliations, and citations commonly exhibit power-law behavior. To address this limitation, we introduce the Relative Importance Factor (RIF) Index, a statistical measure derived from the estimated discrete power-law rank distribution rather than an additional independent parameter. The RIF Index compares the probability of an element at a given rank with its probabilities at lower ranks, enabling explicit pairwise statistical comparison, particularly within the tail. We formalize the mathematical framework for discrete rank modeling and apply RIF to synthetic data and a Scopus dataset on social resilience. Our results show that RIF clarifies dominance relationships among ranked elements, providing stronger discrimination in the tail than frequency-based measures alone. We further introduce the RIF matrix and RIF network to represent these pairwise relationships structurally, supporting interpretation of prominence patterns. Although demonstrated in academic publishing, the method generalizes to domains where categorical variables follow discrete power-law behavior under appropriate model-fit validation. Full article

(1) Background: The purpose of this paper is to explicate the logic of community engagement in American policing. In the United States, the police are organized for crime control and social order through law enforcement. In fact, the terms police and law enforcement are often used interchangeably. This linguistic trap reifies the law-enforcer identity and disposition, while producing a logic of professional practice that prioritizes enforcement over more effective crime prevention activities. We ask, “Are there better ways to organize the police to make communities safer?” If so, what could the police do and why? To answer these questions, we first explore the structure of American policing and the logic it creates. We then examine latent community dynamics and their impact on public safety. (2) Methods: Using survey data from a statewide probability sample of households, the authors examine the impact of these dynamic processes on crime, informal social control, and support for those returning to the community from prison. (3) Findings: The findings demonstrate, in measurable ways, the essential function of community-engagement in creating safe, strong neighborhoods. (4) Conclusions: The study’s findings suggest a new framework for policing that prioritizes community engagement for relationship building and problem-oriented policing over more aggressive law enforcement campaigns. Full article

Restoring the ecological function of degraded wetlands from the perspective of hydrological connectivity is of great significance for maintaining the stability of wetland ecosystem and biodiversity. Taking Zoige Wetland as the study area, this paper quantitatively analyzed the changing law of hydrological connectivity of wetland from 2000 to 2020 in terms of structural connectivity and functional connectivity by using the landscape index and the landscape connectivity index, and identified the important habitat patches, as well as the main influencing factors of hydrological connectivity. The results showed that functional connectivity increased slightly overall, with Probability of Connectivity (PC) and Integral Index of Connectivity (IIC) showing synchronized interannual fluctuations and higher mean levels in 2010–2020 than in 2000–2009. Patch-importance analysis (dPC) identified connectivity “backbone” areas along the Yellow River main channel and Central Zoige County. Pearson correlations (n = 21) indicated that PC was positively associated with precipitation (r = 0.77) and runoff (r = 0.68), and negatively associated with temperature (r = −0.41), vegetation cover (FVC; r = −0.68), and human disturbance proxy (PAFRAC; r = −0.66). These results help elucidate degradation processes and drivers in the Zoige Wetland and inform protection and restoration. Future studies should combine denser time series with field surveys to reduce uncertainties in remote-sensing water mapping. Full article

This paper presents the development and investigation of an intelligent control system for a high-frequency ozone generator integrated into an IoT-based and telecommunication environment. A cyber-physical nonlinear mathematical model combining the electrical, thermal, gas-dynamic, and chemical subsystems of the ozone generation process is proposed. The model was implemented in discrete-time form and experimentally validated using the corona–discharge-based high-frequency ozonator ETRO-02. The deviation between simulation and experimental results did not exceed 5.3% for settling time, 6.7% for overshoot, 1.6% for steady-state ozone concentration, and 0.9% for gas temperature, confirming the adequacy of the proposed model. Based on this model, a hierarchical two-level intelligent control architecture is synthesized, consisting of a fast local control loop with a cycle time of 1–5 ms and a supervisory monitoring layer. The proposed adaptive state-feedback control law with online gain adjustment ensures stable real-time operation under nonlinear dynamics, ±20% parameter variations, network delays of 1–10 ms, and packet loss probabilities of up to 5%. As a result, the settling time is reduced from 420 ms to 160 ms, the overshoot from 12.5% to 3.1%, and the steady-state error from 6.5% to 1.6%, while the specific energy consumption decreases from 11.8 to 6.2 Wh/m³. The obtained results demonstrate that the integration of a cyber-physical model with a millisecond-level intelligent control system significantly improves the dynamic performance, robustness, and energy efficiency of high-frequency ozone generators compared to classical control and monitoring-oriented IoT systems. Unlike cloud-centric IoT monitoring architectures that operate at second-level update cycles, the proposed system closes the control loop locally at the millisecond scale, enabling stabilization of fast nonlinear electro-plasma dynamics. The results demonstrate that edge-intelligent adaptive control significantly enhances both dynamic performance and energy efficiency, confirming the feasibility of millisecond-level cyber-physical regulation for industrial ozone generation systems. Full article

Bipolar Polymer Membranes (BPMs) enable the creation of large, stable pH gradients by driving water dissociation (WD) at the cation/anion junction under reverse bias, a process central to electrodialysis, CO₂ capture, and emerging acid–alkaline water electrolysis. Yet despite decades of study, the mechanism by which intense interfacial electric fields accelerate WD remains debated and is often modeled with ad hoc assumptions. In this study, we present a power dissipation model in which minority ions from water autoprotolysis act as carriers that continuously dissipate field-supplied power in the hydrated nanometric junction. This dissipative input increases the local probability of heterolytic O–H bond cleavage and analytically leads to a quadratic dependence of the dissociation rate constant on the field. Without adjustable parameters, the model reproduces the required orders of magnitude for the enhancement ratio k_d(E)/k_d(0), where k_d(E) is the field-enhanced water dissociation rate constant and k_d(0) is its zero-field value across typical BPM fields, and yields a quadratic current–voltage junction law. A proof-of-principle measurement on a commercial Fumasep^® FBM bipolar membrane confirms the quadratic current–voltage trend, supporting a power-dissipation-driven water dissociation mechanism and providing a concise, falsifiable baseline for future studies. Full article

This study investigates the impact of urban meteorological factors on road crash severity in Dhaka, Bangladesh. Using police crash data, and meteorological data from NASA POWER database for years 2011–2022, a generalized ordered logit model was used to analyze crash severity, and interpreted using odds ratio, log odds ratio, predicted probabilities and marginal effects. The results show that land surface temperature (LST), relative humidity, precipitation, surface pressure, and wind speed have significant association with crash severity. Relative humidity, surface pressure and LST exhibited positive relation with higher severity levels of crashes, whereas precipitation had a negative relation. We recommend three actions to lessen the severity of crashes during inclement weather based on the findings: (i) weather-responsive transport safety policies, which incorporate real-time weather data into intelligent transport systems; (ii) law enforcement-oriented policy implications, which include using automated speed cameras and red-light violation cameras to improve compliance consistency and updating driver training courses to include modules on risk perception across various environmental conditions; and (iii) infrastructure and vehicle-related policy implications, which include designing road geometries and surface conditions to prevent the effects of adverse weather conditions and utilizing safety equipment, such as electronic stability control and anti-lock braking systems. Full article

Household food expenditure reflects not only income differences, but also how demographic, economic, and institutional factors interact within the household. This study examines household food expenditure in Türkiye from a systems perspective, with a particular focus on the role of gender in shaping economic vulnerability. Using microdata from the 2018 Household Budget Survey, the analysis employs a two-stage framework that combines Artificial Neural Networks and a Tree-Augmented Bayesian Network. In the first stage, non-linear relationships among household characteristics are identified and the most influential determinants of food expenditure shares are determined. In the second stage, a probabilistic system model is constructed to conduct counterfactual “what-if” simulations. The results show that household food expenditure emerges as a systemic outcome influenced by income, education, employment stability, savings capacity, and asset ownership, with gender playing a central role. Female-headed households—particularly those living alone and facing limited education and unstable employment—have a substantially higher probability of allocating a large share of their budget to food. These findings are consistent with Engel’s law and highlight the gendered nature of economic vulnerability. The study suggests that food security policies should address employment stability, human capital, and access to productive assets alongside income support. Full article

This paper continues the development of information-geometric models for data analysis and physics by focusing on their formulation and interpretation through variational principles. Building on the geometric framework introduced previously, we investigate how fundamental variational structures—such as information-theoretic functionals—naturally encode the laws of nature. In the first manuscript, we showed that a wide class of physical problems can be expressed as constrained variational problems on spaces of probability distributions, leading to geodesic flows, gradient dynamics, and generalized Hamiltonian formulations on statistical manifolds. In this second part, we extend the variational formalism by utilizing an extended metric, clarifying the geometric origin of the dynamical equations commonly used in modern physics and providing a coherent interpretation of physical laws in terms of information optimization. By emphasizing variational foundations, this paper strengthens the conceptual and mathematical links between information geometry, data analysis, and physics, and it provides a flexible framework for extending geometric methods to complex, high-dimensional, and dynamical systems. Full article

Wildland fire behaviour is strongly governed by the coupled effects of wind and terrain slope, yet the literature remains fragmented across experimental, empirical, mathematical, and physics-based modelling traditions. A systematic scoping review with narrative synthesis was performed (Web of Science, Scopus, and Google Scholar plus citation chaining), screening studies for explicit wind–slope treatment with reported forcings and outcomes. Across more than 150 studies, slope benches, wind tunnels, trenches/canyons, and field burns show that upslope–wind alignment promotes flame attachment and a shift from radiation-led to convection-led preheating (often near 20–30° slopes and moderate winds), whereas opposing or downslope forcing lifts flames and suppresses spread; confined geometries can trigger eruptive acceleration. Mathematical analogues and empirical models provide fast predictions using compact wind/slope modifiers and enable scenario and burn-probability mapping but typically prescribe coupling and miss regime transitions. Physics-based LES/CFD and coupled atmosphere–fire systems resolve terrain–flow feedback sand can yield reduced-order laws suitable for embedding into operational tools, albeit at higher computational cost and with validation gaps. Benchmarks are consolidated, approaches are compared using a common rubric (fidelity, validation, applicability, cost, and operational utility), and priorities are identified for cross-scale datasets, firebrand transport in complex terrain, and real-time coupled prediction. Full article

We study strong laws of large numbers in a non-linear framework based on conditional sub-additive expectations and conditional sub-additive capacities. Using an axiomatic approach to conditional sub-additive expectation, we establish a conditional Hájek–Rényi-type maximal inequality assuming a general conditional Kolmogorov-type maximal inequality but without imposing any weak dependence structure on the underlying sequence. As a consequence, we derive a general conditional strong law of large numbers. Finally, we introduce a notion of conditional negative dependence under sub-additive expectations and obtain the corresponding conditional Kolmogorov-type maximal inequality, leading to a conditional strong law of large numbers for conditionally negatively dependent random variables. Full article

The biological variability of apatites in different hard tissues of organisms was the starting point for this investigation. Materials such as whale rostrums, ganoine, and some fish bones were analyzed. It has been proven that different organisms select specific kinds of apatites for the construction of their hard organs at the level of the crystal cell. This probably results from the long-lasting adaptation of the construction to their environmental needs. The materials are characterized by the parameters Δd and ΔE, being the real and apparent deviations from Bragg’s dimension d and the energy of excitation in XRD—E. This study is based on previously published, verified results from a number of researchers and research groups. The derivation of expressions was possible due to an original approach to Bragg’s equation, finally finished in the reformulation of the law, which describes the interplay between the absolute value of the probing excitation energy E and the crystal cell’s internal volume V. It enabled the classification of apatite biomaterials in living and fossil organisms, as well as the classification of the apatite excretions. In addition, the following different possible modes of changes in Bragg’s dimension d were illustrated—spontaneous geometrical expansion, thermal action, pressurization, and single- and multiple-ion exchanges. The contributions of such expansions were estimated. We can steer the cell volume of apatites in various ways. It has been proven that the volume expansion is linearly coupled with the expansion of Bragg’s d parameter in the hexagonal system. Full article

We present a quantum cognitive model that integrates the influence of cognitive control into human perceptual decision-making. The model employs a multiple-square-well potential, where each well corresponds to a distinct decision outcome. In this framework, well depth encodes signal strength, while well width represents the domain generality of the outcome. The probability of particle localization within each well determines the subjective probability, which subsequently drives a standard Markovian evidence accumulation process to predict empirical choice and response times. We validate the model using the classic dot motion two-alternative forced-choice (2AFC) task. The model successfully replicates key empirical findings of the task, such as the correlation between motion coherence and drift rates. Furthermore, we apply the model to the Yerkes–Dodson law, capturing the approximate inverted U-shaped relationship between task accuracy and cognitive arousal. We compare two theoretical approaches to modeling arousal (1) as eigenenergy values and (2) as kinetic energy terms, contrasting their qualitative predictions regarding the Yerkes–Dodson law. Our work provides the first quantitative model of arousal’s influence on human perceptual decision-making and establishes a foundation for determining the exact functional form of the Yerkes–Dodson law. Full article