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We formulate a thermodynamic theory applicable to both classical and quantum systems. These systems are depicted as thermodynamic system–bath models capable of handling isothermal, isentropic, thermostatic, and entropic processes. Our approach is based on the use of a dimensionless thermodynamic potential expressed as a function of the intensive and extensive thermodynamic variables. Using the principles of dimensionless minimum work and dimensionless maximum entropy derived from quasi-static changes of external perturbations and temperature, we obtain the Massieu–Planck potentials as entropic potentials and the Helmholtz–Gibbs potentials as free energy. These potentials can be interconverted through time-dependent Legendre transformations. Our results are verified numerically for an anharmonic Brownian system described in phase space using the low-temperature quantum Fokker–Planck equations in the quantum case and the Kramers equation in the classical case, both developed for the thermodynamic system–bath model. Thus, we clarify the conditions for thermodynamics to be valid even for small systems described by Hamiltonians and establish a basis for extending thermodynamics to non-equilibrium conditions. [ABSTRACT FROM AUTHOR]

Nonlinear optical methods, such as vibrational sum frequency generation (vSFG) and second harmonic generation (SHG), are powerful techniques to study elusive structures at charged buried interfaces. However, for the separation and determination of the Stern and diffuse layer spectra at these charged interfaces, complex vSFG spectra and, hence, the absolute phase need to be retrieved. The maximum entropy method is a useful tool for the retrieval of complex spectra from the intensity spectra; however, one caveat is that an understanding of the error phase is required. Here, for the first time, we provide a physically motivated understanding of the error phase. Determining the error phase from simulated spectra of oscillators with a spectral overlap, we show that for broadband vSFG spectra, such as for the silica/water interface, the diffuse and Stern layers' spectral overlap within the O–H stretching window results in a correlation between the error phase and the phase shift between the responses of these layers. This correlation makes the error phase sensitive to changes in Debye length from varying the ionic strength among other variations at the interface. Furthermore, the change in the magnitude of the error phase can be related to the absolute SHG phase, permitting the use of an error phase model that can utilize the SHG phase to predict the error phase and, hence, the complex vSFG spectra. Finally, we highlight limitations of this model for vSFG spectra with a poor overlap between the diffuse and Stern layer spectra (silica/HOD in D2O system). [ABSTRACT FROM AUTHOR]

As the global shift from fossil fuels to the Paris Agreement has accelerated, wind energy has become a key alternative to hydroelectric power. However, existing research often needs to improve in integrating diverse environmental, economic, and climate-related variables when modeling wind energy potential, particularly under future climate change scenarios. Addressing these gaps, this study employs the maximum entropy (MaxEnt) method, a robust and innovative tool for spatial modeling, to identify optimal wind farm sites in Türkiye. This research advances site selection methodologies and enhances predictive accuracy by leveraging a comprehensive dataset and incorporating climate change scenarios. The results indicate that 89% of the current licensed projects will maintain compliance in the future, while 8% will see a decrease in compliance. Furthermore, the wind energy potential in Türkiye is expected to increase because of climate change. These results confirm the suitability of existing project locations and identify new high-potential areas for sustainable wind energy development. This study provides policymakers, investors, and developers actionable insights to optimize wind energy integration into the national energy portfolio, supporting global climate goals by accelerating the adoption of renewable energy sources. [ABSTRACT FROM AUTHOR]

Garnet-type Li7La3Zr2O12 (LLZO) is a promising solid electrolyte for all-solid-state batteries due to its structural stability and high Li+ ionic conductivity, but high-purity LLZO crystallizes in a low-conductivity tetragonal phase at room temperature (RT). Al doping stabilizes the cubic structure, yet its impact on Li+ migration is not fully understood. Using Li6.25La3Zr2Al0.25O12 (LLZAO) as a model, we conducted temperature-dependent neutron powder diffraction (NPD), neutron pair distribution function (nPDF), and density-functional theory (DFT) computations. NPD results, supported by nPDF, show Li+ ions at 24d and 96h sites, excluding 48g. Al at 24d adjusts the distribution of Li, improving ionic conductivity near RT. Maximum Entropy Method analyses indicate a temperature-driven 3D Li diffusion pathway of 24d-96h-96h-24d channels, confirmed by DFT. This work will enhance the understanding of Li diffusion and the optimization of ionic conductivity in garnet-type solid electrolytes. [ABSTRACT FROM AUTHOR]

Garnet-type Li7La3Zr2O12 (LLZO) is a promising solid electrolyte for all-solid-state batteries due to its structural stability and high Li+ ionic conductivity, but high-purity LLZO crystallizes in a low-conductivity tetragonal phase at room temperature (RT). Al doping stabilizes the cubic structure, yet its impact on Li+ migration is not fully understood. Using Li6.25La3Zr2Al0.25O12 (LLZAO) as a model, we conducted temperature-dependent neutron powder diffraction (NPD), neutron pair distribution function (nPDF), and density-functional theory (DFT) computations. NPD results, supported by nPDF, show Li+ ions at 24d and 96h sites, excluding 48g. Al at 24d adjusts the distribution of Li, improving ionic conductivity near RT. Maximum Entropy Method analyses indicate a temperature-driven 3D Li diffusion pathway of 24d-96h-96h-24d channels, confirmed by DFT. This work will enhance the understanding of Li diffusion and the optimization of ionic conductivity in garnet-type solid electrolytes. [ABSTRACT FROM AUTHOR]

Estimating the probability and consequences of drought disasters is an important task in drought risk assessment, which contributes to the development of mitigation strategies. Based on rainfall data from 2481 stations and the drought-affected arable land of each province from 1961 to 2021, a probabilistic analysis model of drought duration, drought severity and the proportion of affected farmland area (PAFA) was constructed by a three-dimensional copula function. The results show that the distribution functions of drought duration, drought severity and the PAFA are well given based on the principle of maximum entropy and can pass the Kolmogorov–Smirnov (K-S) distribution test with a significance level of 0.05. Among the three Archimedean copulas, Frank's method has a relatively better accuracy, suggesting that it captures the dependencies between drought characteristics better and is more suitable for constructing joint distribution functions. The value of PAFA in Gansu, Inner Mongolia, Shanxi and Liaoning is about 0.3, which is higher than other provinces. Drought duration and drought severity levels of 3 to 4 are the main causes of a PAFA greater than 0.3, which can be used as an early warning line for drought risk. At the same level of PAFA, the drought in the southern region lasted longer and was more intense. [ABSTRACT FROM AUTHOR]

Since the 1990's, it has been recognized that the full explanation of cosmic rays (CR) and their spectrum may require some new physics. The debate on the origin of CR has led to the conclusion that while most CR come from supernova explosions in the Galaxy, CR with very high energies are likely of extragalactic origin. However, a response to several open questions, still unanswered, concerning CR above 1013 eV is required. We herewith study the temporal evolution of the observational CR using data collected by several stations of the ground-based network. The obtained result states that the power spectral density of the CR temporal evolution, especially with a frequency less than 0.1 Hz, exhibits the Kolmogorov-Obukhov 5/3 law that exhibits the energy spectrum of many geophysical quantities. Any small difference found from the 5/3 exponent can be attributed to intermittency corrections and the stations' characteristics. Moreover, natural time analysis applied to the CR time series showed the critical role of the quasi-biennial oscillation to the entropy maximization which occurs following the 5/3 Kolmogorov-Obukhov power law. These findings can be used to more reliably predict extreme CR events that could have an impact even at the molecular level. • The power spectral density of the observed time evolution of CRs obeys the 5/3 law. • The CRs entropy is maximized following the Kolmogorov-Obukhov 5/3-power law. • Small differences from the 5/3 law can be attributed to intermittency corrections. • The Natural Time Analysis of CRs data can lead to the prediction of its extremes. [ABSTRACT FROM AUTHOR]

We consider a stationary Markovian evolution with values on a finite disjointly partitioned set space I ⊔ E . The evolution is visible (in the sense of knowing the transition probabilities) on the states in I but not for the states in E . One only knows some partial information on the transition probabilities on E , the input and output transition probabilities and some constraints of the transition probabilities on E . Under some conditions we supply the transition probabilities on E that satisfies the maximum entropy principle. [ABSTRACT FROM AUTHOR]

A novel structural reliability analysis method that combines the improved beluga whale optimization (IBWO) and the arctangent function‐based maximum entropy method (AMEM) is proposed in this paper. It aims to augment the accuracy of failure probability prediction in structural reliability analysis based on the traditional maximum entropy method (MEM). First, the arctangent function is introduced to avoid the effects of truncation error and numerical overflow in the traditional MEM. The arctangent function can nonlinearly transform the structural performance function defined on the infinite interval into a transformed performance function defined on the bounded interval. Subsequently, the undetermined Lagrange multipliers in the maximum entropy probability density function (MEPDF) of the transformed performance function are obtained using IBWO at a swifter convergence speed with heightened convergence accuracy. Finally, the MEPDF of the transformed performance function can be obtained by combining the IBWO and AMEM, and the structural failure probability can be predicted. The analysis of the metro bogie frame as an engineering example reveals that compared with the traditional MEM using the genetic algorithm to solve the Lagrange multipliers, the proposed method diminishes the relative error in failure probability prediction from 20.51% to only 0.09%. This method significantly enhances the prediction accuracy of failure probability. [ABSTRACT FROM AUTHOR]

Microsimulation models are effective for analysing road users' interaction behaviour and assessing different facilities' performance. However, only a few studies have developed simulation models for studying motorized and non-motorized vehicles conflicts. This is likely due to mixed traffic's complexity and heterogeneity and the difficulty in accurately capturing road users' avoidance maneuver. This study aims to adopt a multiagent simulation model to replicate road users' microscopic behaviour and collision avoidance mechanisms in traffic conflict scenarios. Road users' reward functions are recovered by the multiagent inverse reinforcement learning approach. The multiagent Actor-Critic deep learning algorithm is used to predict road users' evasive action and assess their optimal policies. The findings demonstrate that the multiagent simulation model provides highly accurate predictions of road users' trajectories and collision avoidance strategies. Furthermore, the results demonstrate a strong correlation between the predicted traffic conflict indicator from the simulated trajectories and that from the actual trajectories. [ABSTRACT FROM AUTHOR]

This work explores the hyperbolic cosine and hyperbolic secant functions within the framework of the maximum entropy principle, deriving these probability distribution functions from first principles. The resulting maximum entropy solutions are applied to various physical systems, including the repulsive oscillator and solitary wave solutions of the advection equation, using the method of moments. Additionally, a different moment analysis using experimental and theoretical inputs is employed to address non-linear systems described by the non-linear Schrödinger equation, non-linear diffusion equation, and Korteweg–de Vries equation, demonstrating the versatility of this approach. These findings demonstrate the broad applicability of maximum entropy methods in solving different differential equations, with potential implications for future research in non-linear dynamics and transport physics. [ABSTRACT FROM AUTHOR]

This paper presents an improved entropy-based freight tour synthesis (FTS) using fuzzy logic (FL). One approach used in formulating FTS models is entropy maximization, which aims to obtain the most probable freight (trucks) tour flow distribution in a network based on traffic counts. These models consider fixed parameters and constraints. However, the variations in costs, traffic counts, and truck demands depending on human behavior, are not always captured in detail in such models. FL can include such variabilities in its modeling. The flexibility FL provides to the model allows to obtain solutions where some or all the constraints do not entirely satisfy—but are close to—their expected values. Moreover, the modeling approach used based on FL theory is the membership function, specifically the triangular membership function, which is defined by three points corresponding to the vertices. This optimization problem was transformed into a bi-objective problem when the optimization variables are the membership and the entropy. The performance of the proposed formulation was assessed in the Sioux Falls network. To solve the problem, the model was run in General Algebraic Modeling System (GAMS), applying the ε approach, where ε value (ε ∈ [0, 1] with steps of 0.01) represents the level of accomplishment that at least one of the constraints (but can be more) gets. The results show that the entropy value decreased as the accomplishment level increased, and this behavior indicates a Pareto frontier, which proves that the optimization problem is bi-objective. [ABSTRACT FROM AUTHOR]

Hydrologic systems are characterized by processes which may occur on, below, and above the land surface. These processes are irreversible, and the irreversibility produces entropy. In thermodynamics, entropy is a measure of the loss of heat energy which is a reflection of disorder. The entropy of a system achieves its maximum at steady state, meaning that the maximum entropy production corresponds to the most probable state. In information theory, entropy is a measure of uncertainty or disorder or information imbued in the random variable describing the hydrologic system. The maximum uncertainty corresponds to maximum entropy or the most probable distribution of the variable which leads to the principle of maximum entropy or the principle of minimum cross entropy, subject to the given constraints. The most probable or maximum entropy-based distribution is confirmed by the theorem of concentration. The form of entropy (Shannon, Tsallis, Renyi, or Kapur), principle of maximum entropy, principle of minimum cross-entropy, and the concentration theorem constitute the theory of entropy. This paper presents a general framework based on the entropy theory, and demonstrates its application for modeling a number of surface and subsurface hydrologic and water quality processes, including hydrometric network evaluation, eco-index, surface runoff, infiltration, soil moisture, velocity distribution, sediment concentration, sediment discharge, sediment yield, channel cross section, rating curve, and debris flow. [ABSTRACT FROM AUTHOR]

Inferring a quantum system from incomplete information is a common problem in many aspects of quantum information science and applications, where the principle of maximum entropy (MaxEnt) plays an important role. The quantum state compatibility problem asks whether there exists a density matrix ρ$\rho$ compatible with some given measurement results. Such a compatibility problem can be naturally formulated as a semidefinite programming (SDP), which searches directly for the existence of a ρ$\rho$. However, for large system dimensions, it is hard to represent ρ$\rho$ directly, since it requires too many parameters. In this work, MaxEnt is applied to solve various quantum state compatibility problems, including the quantum marginal problem. An immediate advantage of the MaxEnt method is that it only needs to represent ρ$\rho$ via a relatively small number of parameters, which is exactly the number of the operators measured. Furthermore, in case of incompatible measurement results, the method will further return a witness that is a supporting hyperplane of the compatible set. The method has a clear geometric meaning and can be computed effectively with hybrid quantum‐classical algorithms. [ABSTRACT FROM AUTHOR]

The rapid expansion of the Internet of Things (IoT) necessitates advanced routing schemes capable of meeting the stringent demands for low latency and high accuracy, which are critical for applications such as autonomous vehicles and telemedicine. Traditional edge computing methods often struggle with elevated latency, rendering them unsuitable for time-sensitive applications. Additionally, many reinforcement learning (RL) algorithms require action space discretization, which can introduce biases and dimensionality challenges. This paper introduces a novel Soft Actor-Critic (SAC)-based distributed routing scheme for edge computing, specifically designed to address these limitations. By integrating RL with Maximum Entropy principles and employing a decentralized approach, the proposed model enhances network performance, reduces delays, and effectively manages multi-optimality criteria. The distributed routing scheme operates independently of a centralized controller, allowing routers to make autonomous decisions and adapt seamlessly to changes in the network. This is accomplished through a Markov Decision Process (MDP) that optimizes routing paths based on various factors, including node depth, energy consumption, and transmission probability. The methodology encompasses local training phases for individual nodes, followed by federated training to refine the model across the network. Experimental results conducted on topologies of varying scales demonstrate the model’s efficacy in achieving high accuracy and efficient convergence, particularly in dynamic IoT environments. These findings underscore the potential of the proposed SAC-based distributed routing scheme as a robust solution for enhancing routing efficiency and reliability in the evolving landscape of IoT applications.IMPACT STATEMENTThe rapid expansion of Internet of Things (IoT) applications demands advanced routing solutions to ensure low latency and high accuracy, crucial for sectors like autonomous vehicles and telemedicine. Traditional edge computing methods struggle with elevated latency, while many reinforcement learning (RL) algorithms face challenges with action space discretization, leading to biases and dimensionality issues. This study introduces a novel Soft Actor-Critic (SAC)-based distributed routing scheme to address these limitations. Integrating Maximum Entropy principles with RL enhances exploration and decision-making stability. The decentralized approach allows routers to make autonomous, real-time decisions based on local network conditions, optimizing routing paths through a Markov Decision Process (MDP). Experimental results from various simulated IoT network topologies show the model’s superior performance in reducing delays and maintaining bandwidth. This research paves the way for more reliable, low-latency IoT applications, significantly enhancing routing efficiency and network adaptability in dynamic IoT environments. [ABSTRACT FROM AUTHOR]

Federated Learning (FL), as a distributed machine learning framework, can effectively learn symmetric and asymmetric patterns from large-scale participants. However, FL is susceptible to malicious backdoor attacks through attackers injecting triggers into the backdoored model, resulting in backdoor samples being misclassified as target classes. Due to the stealthy nature of backdoor attacks in FL, it is difficult for users to discover the symmetric and asymmetric backdoor properties. Currently, backdoor defense methods in FL cause model performance degradation while reducing backdoors. In addition, some methods will assume the existence of clean samples, which does not match the realistic scenarios. To address such issues, we propose FLSAD, an effective backdoor defense method in FL via self-attention distillation. FLSAD can recover the triggers using an entropy maximization estimator. Based on the recovered triggers, we leverage the self-attention distillation to eliminate the backdoor. Compared with the baseline backdoor defense methods, FLSAD can reduce the success rates of different state-of-the-art backdoor attacks to 2% on four real-world datasets through extensive evaluation. [ABSTRACT FROM AUTHOR]

On-demand meal delivery has become a feature of most cities around the world as a result of platforms and apps that facilitate it as well as the pandemic, which for a period, closed restaurants. Meals are delivered by couriers, typically on bikes, e-bikes, or scooters, who circulate collecting meals from kitchens and delivering them to customers, who usually order online. A Markov model for circulating couriers with n + 1 parameters, where n is the number of kitchens plus customers, is derived by entropy maximization. There is one parameter for each kitchen and customer representing the demand for a courier, and there is one parameter representing the urgency of delivery. It is shown how the mean and variance of delivery time can be calculated once the parameters are known. The Markov model is irreducible. Two procedures are presented for calibrating model parameters on a data set of orders. Both procedures match known order frequencies with fitted visit probabilities; the first inputs an urgency parameter value and outputs mean delivery time, whereas the second inputs mean delivery time and outputs the corresponding urgency parameter value. Model calibration is demonstrated on a publicly available data set of meal orders from Grubhub. Grubhub data are also used to validate the calibrated model using a likelihood ratio. By changing the location of one kitchen, it is shown how the calibrated model can estimate the resulting change in demand for its meals and the corresponding mean delivery time. The Markov model could also be used for the assignment of courier trips to a street network. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT Conference. [ABSTRACT FROM AUTHOR]

AbstractA map becomes readable and translatable only after the use of labels. High-quality label placement (i.e., labelling) is a combinatorial optimization problem, where one or more objective functions are required. However, such objective functions are still not well achieved in existing methods with commonly used labelling rules (e.g., “avoidance of overlapping labels” and “placement at priority positions”). In this study, we define a new objective function using the maximum entropy principle and employ a modified genetic algorithm in the optimization process. Comparative experiments have been conducted with 29 existing labelling methods in a dataset of 1000 points. Experimental results show that the average percentage of non-overlapping labels by this new method reaches 94.73% (i.e., being ranked second in all 30 methods) and has no statistically significant difference from the best one (i.e., 94.87%). By further analysis, it is found that this new method not only can place more labels in priority positions than the method being ranked first but also has the potential to place labels for line and area features. Such results indicate that the maximum entropy principle may pave the way for further research in cartography. [ABSTRACT FROM AUTHOR]

This paper presents a localized treatment of quantum Rényi entropy. Specifically, based on the localized characteristics of Local Quantum Bernoulli Noises (LQBNs), a new definition of quantum Rényi entropy, that is, quantum Rényi entropy with localization characteristics, is given through the local density operator constructed by local conservative operators l k ∘ . We also verify that this new definition possesses properties such as unitary invariance, additivity, monotonicity, and weak subadditivity. Furthermore, through the monotonicity of the local quantum Rényi entropy, we derive the local quantum Rényi minimum entropy and the local quantum Rényi maximum entropy. The local quantum Rényi entropy can be used to study quantum entanglement and coherence. For instance, in the contexts of quantum phase transitions and quantum state transmission, the local quantum Rényi entropy can provide important insights into the flow of information and interactions within quantum systems. [ABSTRACT FROM AUTHOR]

Seismic fragility analysis and resilience assessment of large‐span cable‐stayed bridge structures are critical for evaluating their seismic performance. However, there is a scarcity of research on the effects of multi‐support ground motions and their non‐Gaussian characteristics on seismic fragility and resilience. This paper aims to addresses this issue. Initially, random ground motions with spatial variability and non‐Gaussian characteristics are simulated using the Spectral Representation Method (SRM) and the Unified Hermite Polynomial Model (UHPM). Subsequently, the Fractional Exponential Moments‐based Maximum Entropy Method (FEM‐MEM) and the Adaptive Gaussian Mixture Model (AGMM) are employed for seismic reliability‐based fragility analysis, overcoming the shortcomings of conventional lognormal assumption. Component‐ and system‐level fragility analyses are conducted sequentially, followed by seismic resilience assessment of bridge structures based on the results of system‐level fragility analysis. A numerical example is presented to validate the proposed method. Computational results indicate that: (1) The proposed method offers higher accuracy and broader applicability for seismic fragility analysis of large‐span cable‐stayed bridge structures compared to traditional assumptions. (2) The non‐Gaussian characteristics of ground motions may significantly impact the seismic fragility analysis and resilience assessment of large‐span bridge structures. [ABSTRACT FROM AUTHOR]

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This study was designed to reveal the possible effects of climate change on the landscape structure of the Bartın Stream Basin. Remote sensing (RS) and Geographic Information Systems (GIS) tools and statistical methods were employed throughout the study. Landsat satellite images, which are 30 m × 30 m resolution images produced by Landsat 4–5, Landsat 7, and Landsat 8-Oli satellites, were used. In addition, 42 variables were produced, including 19 bioclimatic variables, plant index data from satellite images, and environmental variables. The effect of the produced variables on land use-land cover (LULC) was investigated. Then, the expected situation in 2050 according to the RCP climate change scenarios was estimated using the R Studio software with time series analysis. The data for 2050 were modeled and mapped using the Maximum Entropy method. As a result, it was revealed that LULC changes within the basin would be in the form of artificialization and increased fragmentation, that bare lands and residential areas would increase, and that agricultural areas and forest areas would decrease by approximately 50%. Planning should be made in order to reduce the breakdown of landscape resistance by predicting the adverse events to be experienced due to climate change in the future. It was concluded that agriculture, which was determined as the development strategy of the region in the current Environmental Plan (EP) of the basin, would not be possible due to the approximately 50% loss in agricultural areas. This study revealed that the effects of climate change, which is the biggest threat of the age, could be revealed with statistical models. [ABSTRACT FROM AUTHOR]

Empirical force fields employed in molecular dynamics simulations of complex systems are often optimized to reproduce experimentally determined structural and thermodynamic properties. In contrast, experimental knowledge about the interconversion rates between metastable states in such systems is hardly ever incorporated in a force field due to a lack of an efficient approach. Here, we introduce such a framework based on the relationship between dynamical observables, such as rate constants, and the underlying molecular model parameters using the statistical mechanics of trajectories. Given a prior ensemble of molecular dynamics trajectories produced with imperfect force field parameters, the approach allows for the optimal adaption of these parameters such that the imposed constraint of equally predicted and experimental rate constant is obeyed. To do so, the method combines the continuum path ensemble maximum caliber approach with path reweighting methods for stochastic dynamics. When multiple solutions are found, the method selects automatically the combination that corresponds to the smallest perturbation of the entire path ensemble, as required by the maximum entropy principle. To show the validity of the approach, we illustrate the method on simple test systems undergoing rare event dynamics. Next to simple 2D potentials, we explore particle models representing molecular isomerization reactions and protein–ligand unbinding. Besides optimal interaction parameters, the methodology gives physical insights into what parts of the model are most sensitive to the kinetics. We discuss the generality and broad implications of the methodology. [ABSTRACT FROM AUTHOR]

A novel method combining the maximum entropy principle, the Bayesian-inference of ensembles approach, and the optimization of empirical forward models is presented. Here, we focus on the Karplus parameters for RNA systems, which relate the dihedral angles of γ, β, and the dihedrals in the sugar ring to the corresponding 3J-coupling signal between coupling protons. Extensive molecular simulations are performed on a set of RNA tetramers and hexamers and combined with available nucleic-magnetic-resonance data. Within the new framework, the sampled structural dynamics can be reweighted to match experimental data while the error arising from inaccuracies in the forward models can be corrected simultaneously and consequently does not leak into the reweighted ensemble. Carefully crafted cross-validation procedure and regularization terms enable obtaining transferable Karplus parameters. Our approach identifies the optimal regularization strength and new sets of Karplus parameters balancing good agreement between simulations and experiments with minimal changes to the original ensemble. [ABSTRACT FROM AUTHOR]

Abstract The aim of this paper is to prepare, describe and discuss the models of the current and future distribution of Phthiracarus longulus (Koch, 1841) (Acari: Oribatida: Euptyctima), the oribatid mite species widely distributed within the Palearctic. We used the maximum entropy (MAXENT) method to predict its current and future (until the year 2100) distribution based on macroclimatic bio-variables. To our best knowledge, this is the first-ever prediction of distribution in mite species using environmental niche modelling. The main thermal variables that shape the current distribution of P. longulus are the temperature annual range, mean temperature of the coldest quarter and the annual mean temperature, while for precipitation variables the most important is precipitation of the driest quarter. Regardless of the climatic change scenario (SSP1-2.6, SSP2-4.5, SSP5-8.5) our models show generally the northward shift of species range, and in Southern Europe the loss of most habitats with parallel upslope shift. According to our current model, the most of suitable habitats for P. longulus are located in the European part of Palearctic. In general, the species range is mostly affected in Europe. The most stable areas of P. longulus distribution were the Jutland with surrounding southern coasts of Scandinavia, islands of the Danish Straits and the region of Trondheim Fjord.

Baroreflex is critical to maintain blood pressure homeostasis, and the quantification of baroreflex regulation function (BRF) can provide guidance for disease diagnosis, treatment, and healthcare. Current quantification of BRF such as baroreflex sensitivity cannot represent BRF systematically. From the perspective of complex systems, we regard that BRF is the emergence result of fluctuate states and interactions in physiological mechanisms. Therefore, the three‐layer emergence is studied in this work, which is from physiological mechanisms to physiological indexes and then to BRF. On this basis, since the entropy in statistical physics macroscopically measures the fluctuations of system's states, in this work, the principle of maximum entropy is adopted, and a new index called PhysioEnt is proposed to quantify the fluctuations of four physiological indexes, i.e., baroreflex sensitivity, heart rate, heart rate variability, and systolic blood pressure, which aims to represent BRF in the resting condition. Further, two datasets with different subjects are analyzed, and some new findings can be obtained, such as the contributions of the physiological interactions among organs/tissues. With measurable indexes, the proposed method is expected to support individualized medicine. [ABSTRACT FROM AUTHOR]

Understanding current fire dynamics in the Amazon is vital for designing effective fire management strategies and setting a baseline for climate change projections. This study aimed to analyze recent fire probabilities and project future "fire niches" under global warming scenarios across the Legal Amazon, a scale chosen for its relevance in social and economic planning. Utilizing the maximum entropy method, this study combined a complex set of predictors with fire occurrences detected during 1985–2022. It allowed for the estimation of current fire patterns and projecting changes for the near future (2020–2040) under two contrasting socioeconomic pathways. The results showed strong model performance, with AUC values consistently above 0.85. Key predictors included "Distance to Farming" (53.4%), "Distance to Non-Vegetated Areas" (11.2%), and "Temperature Seasonality" (9.3%), revealing significant influences from human activities alongside climatic predictors. The baseline model indicated that 26.5% of the Amazon has "moderate" to "very high" fire propensity, especially in the southern and southeastern regions, notably the "Arc of Deforestation". Future projections suggest that fire-prone areas may expand, particularly in the southern border regions and near the Amazon riverbanks. The findings underscore the importance of incorporating both ecological and human factors into fire management strategies to effectively address future risks. [ABSTRACT FROM AUTHOR]

The simplest evolutionary model for catching prey by an agent (predator) is considered. The simulation is performed on the basis of a software-emulated Intel i8080 processor. Maximizing the number of catches is chosen as the objective function. This function is associated with energy dissipation and developmental time. It is shown that during Darwinian evolution, agents with an initially a random set of processor commands subsequently acquire a successful catching skill. It is found that in the process of evolution, a logarithmic relationship between astronomical and developmental times arises in agents. This result is important for the ideas available in the literature about the close connection of such concepts as time, Darwinian selection, and the maximization of entropy production. [ABSTRACT FROM AUTHOR]

We present here a unit-log-symmetric model based on the bivariate log-symmetric distribution discussed in recent literature. It is a flexible family of distributions over the interval (0, 1). We then discuss its mathematical properties such as stochastic representation, identifiability, symmetry, modality, moments, quantile function, entropy and maximum likelihood estimation, paying particular attention to the special cases of unit-log-normal, unit-log-Student-t and unit-log-Laplace distributions. Finally, some empirical results and a real-life data analysis involving internet acess data are presented for illustrative purpose. [ABSTRACT FROM AUTHOR]

Today, climate adaptation strategies are at the forefront in wildlife management and protection studies. This study aimed to model and map the effects of global climate change on the Andean bear, which is in the vulnerable category and distributed in South America. For this purpose, 20 environmental variables and 19 high-resolution Chelsa climate maps that could be effective on Andean bear modeling were created. Moreover, the Maximum Entropy method, which is frequently preferred in species distribution modeling, was preferred. The current habitat suitability model of the Andean bear was in the "very good" model category with the training data set ROC value of 0.973 and the test data set ROC value of 0.972. The variables contributing to the current model are roughness index (41.1%), isothermality (38%), elevation (14%), and annual mean temperature (6.9%), respectively. Variables contributing to the current Andean bear model have been simulated in different scenarios (SSP126/SSP370/SSP585) for the year 2100. However, it has been determined that Andean bear habitats will shrink according to the SSP126 Chelsa climate scenario of the year 2100, these habitats will fragment according to the SSP370 scenario, and brown bear habitats will disappear in some regions in the SSP585 scenario. In conclusion, this study raises alarms that the possible decrease in Andean bear habitats will be approximately 67.3% by the year 2100 due to global climate change. [ABSTRACT FROM AUTHOR]

It is well established that a diverse range of entropic measures, while remarkably adaptable, must inevitably be complemented by innovative approaches to enhance their effectiveness across various domains. These measures play a crucial role in fields like communication and coding theory, driving researchers to develop numerous new information measures that can be applied in a wide array of disciplines. This paper introduces a pioneering isolated entropic measure and its solicitations to queueing theory the study of dissimilarities of uncertainty. By creating the newly developed discrete entropy, we have articulated an optimization principle where the space capacity is predetermined and solitary evidence accessible is around the mean size. Additionally, we have conveyed the solicitations of "maximum entropy principle" to maximize the entropy probability distributions. [ABSTRACT FROM AUTHOR]

This article explores the estimation of Shannon entropy and Rényi entropy based on the generalized inverse exponential distribution under the condition of stepwise Type II truncated samples. Firstly, we analyze the maximum likelihood estimation and interval estimation of Shannon entropy and Rényi entropy for the generalized inverse exponential distribution. In this process, we use the bootstrap method to construct confidence intervals for Shannon entropy and Rényi entropy. Next, we select the gamma distribution as the prior distribution and apply the Lindley approximation algorithm to calculate 'estimates of Shannon entropy and Rényi entropy under different loss functions including Linex loss function, entropy loss function, and DeGroot loss function respectively. Afterwards, simulation is used to calculate estimates and corresponding mean square errors of Shannon entropy and Rényi entropy in GIED model. The research results show that under DeGroot loss function, estimation accuracy of Shannon entropy and Rényi entropy for generalized inverse exponential distribution is relatively high, overall Bayesian estimation performs better than maximum likelihood estimation. Finally, we demonstrate effectiveness of our estimation method in practical applications using a set of real data. [ABSTRACT FROM AUTHOR]

The present article proposes a new Legendre wavelet (LW) filter-based image super-resolution technique. The LW basis vector is used in compact form using the unit step function. It is acquired using LW parameters and is discretized via collocation points. The LW basis is used to design a new filter that transforms the low-resolution image into a high-resolution image. This filter matrix is zoomed into a higher dimension using a discretized wavelet basis. The zero-padded low-resolution original image and the zoomed filter matrix are then multiplied by a constant parameter to enhance the information of the reconstructed image. The value of this constant parameter is optimized using the maximization of the entropy of the reconstructed image to further enhance the quality of the image. After the multiplication operation, the zoomed filter matrix and the zero-padded low-resolution original image are added to reconstruct the high-resolution image. Four existing techniques are compared with the proposed technique visually and through spatial frequency, standard deviation, and entropy. The proposed method shows better visual and quantitative feature performance as compared to other schemes. [ABSTRACT FROM AUTHOR]

Locusta migratoria (Linnaeus, 1758) is one of the locusts known as important pests of food crops. Outbreaks of this species can cause catastrophic damage to maize, paddy, and many other crops. A species distribution model was used to identify the probability of the locust's current and future potential distribution in the Indonesian archipelago. The study relied on the machine learning method Maximum Entropy (Maxent) Model to forecast the future spread of the species in the Indonesian archipelago and to find the climate variable that influenced the distribution of Locusta migratoria. The results showed an Area Under Curve (AUC) value of 0.956 for the Locusta migratoria model, indicating a highly reliable model. The important variable for the distribution of this species was precipitation, especially during the dry season. A low amount of rainfall increases the possibility of the species existing and being distributed. Maxent prediction models showed the potential distribution in the southern part of the Indonesian archipelago under both middle and worst-case scenarios for 2070. This model can become one of the baselines for early warning systems, targeted monitoring and surveillance, and the use of specific pesticides or biological control agents to prevent or minimize the harm of Locusta migratoria outbreak to agricultural lands in the future. [ABSTRACT FROM AUTHOR]

Yager (2014) proposed a transformation for negating the happening of an uncertain event which opposes the occurrence of any uncertain event by redistributing its probability equally among the other outcomes. Yager's negation applied repeatedly on any probability distribution converges to the uniform distribution (all events having identical probability of occurrence). In most cases, uniform distribution is the maximum entropy probability distribution (MEPD) as it has maximum uncertainty inherent in it. However, if some information is available regarding the occurrence of events associated with a random experiment, then MEPD may or may not be a uniform distribution. In this work, we have first explored some new properties of Yager's negation and then investigated Yager's negation when we have some additional information about the probability distribution other than the natural constraints. The MEPD in the presence of this additional information is determined using the negation of probabilities. It is shown that the existence of MEPD (and as a result the maximum entropy) largely depends on the parameters of the additional constraints. Some numerical examples have been considered for comparing the MEPD with and without additional constraints. [ABSTRACT FROM AUTHOR]

The main aim of this numerical analysis is to demonstrate the effect of Lorentz force and viscous dissipation on the couple stress-squeezed hybrid-nanofluid flow between two parallel plates under the influence of external squeezing. A novel entropy generation effect is also included to describe the temperature distribution in the couple stress hybrid nanofluid regime. However, the couple stress hybrid nanofluid finds its abundant applications in the various fields of medicine and bio-engineering, particularly, in the removal of obstacles in the arteries, cancer treatment, etc. Inspired by these applications of couple stress hybrid nanofluids, the present problem is devised based on the squeezed parallel plate geometry. The unsteady nonlinear, coupled, two-dimensional, partial differential equations are constructed to disclose the flow and heat transport features. A robust Matlab-based Runge–Kutta fourth-order scheme with shooting technique is used to produce the similarity solutions of the governing equations through the deployment of suitable scaling transformations. Accordingly, it is noted that, enhancing Hartmann and Brinkman numbers increase the entropy generation. Increasing couple stress fluid parameter increases the thermal distribution. Velocity profile shows dual behavior for the raising couple stress fluid parameter. Bejan number increases with increasing Brinkman number. Entropy generation increases with enhancing nanofluid volume fraction. Conclusively, the uniqueness and novelty of the present investigation is the addition of magnetic Ohmic heating and viscous dissipation effects which generalizes the former studies and gives a more redefined numerical simulation of flow and heat transport features of couple stress hybrid nanofluid in the squeezing regime. [ABSTRACT FROM AUTHOR]

Türkiye is one of the first regions where olives were domesticated, and olives reflect the country's millennia-old agricultural and cultural heritage. Moreover, Türkiye is one of the leading nations in olive and olive oil production in terms of quality and diversity. This study aims to determine the current and future distribution areas of olives, which is important for Türkiye's socio-economic structure. For this purpose, 19 different bioclimatic variables, such as annual mean temperature (Bio1), temperature seasonality (Bio4), and annual precipitation (Bio12), have been used. The RCP4.5 and RCP8.5 emission scenarios of the CCSM4 model were used for future projections (2050 and 2070). MaxEnt software, which uses the principle of maximum entropy, was employed to determine the current and future habitat areas of the olives. Currently and in the future, it is understood that the Mediterranean, Aegean, Marmara, and Black Sea coastlines have areas with potential suitability for olives. However, the model projections indicate that the species may shift from south to north and to higher elevations in the future. Analyses indicate that the Aegean Region is the most sensitive area and that a significant portion of habitats in the Marmara Region will remain unaffected by climate change. [ABSTRACT FROM AUTHOR]

Background: Situational awareness is an essential component of wildland firefighter safety. In the US, crew lookouts provide situational awareness by proxy from ground-level locations with visibility of both fire and crew members. Aims: To use machine learning to predict potential lookout locations based on incident data, mapped visibility, topography, vegetation, and roads. Methods: Lidar-derived topographic and fuel structural variables were used to generate maps of visibility across 30 study areas that possessed lookout location data. Visibility at multiple viewing distances, distance to roads, topographic position index, canopy height, and canopy cover served as predictors in presence-only maximum entropy modelling to predict lookout suitability based on 66 known lookout locations from recent fires. Key results and conclusions: The model yielded a receiver-operating characteristic area under the curve of 0.929 with 67% of lookouts correctly identified by the model using a 0.5 probability threshold. Spatially explicit model prediction resulted in a map of the probability a location would be suitable for a lookout; when combined with a map of dominant view direction these tools could provide meaningful support to fire crews. Implications: This approach could be applied to produce maps summarising potential lookout suitability and dominant view direction across wildland environments for use in pre-fire planning. We use machine learning to predict the probability an area is suitable for a wildland firefighter lookout based on incident data, roads, and lidar-derived visibility, terrain, and vegetation information. This approach may aid pre-fire planning and enhance situational awareness by providing maps of potential lookout locations. [ABSTRACT FROM AUTHOR]

In this research work, BaZr0.85Ho0.10Y0.025Nd0.025O3-δ (BZHYN) electrolyte ceramic was synthesized through a cost-effective flash pyrolysis route followed by conventional sintering for intermediate-temperature solid oxide fuel cells. The calcined powder and sintered pellet were characterized through various techniques like high-resolution X-ray diffraction (HRXRD), high-resolution transmission electron microscopy (HRTEM), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectra (EDS), and Raman spectroscopy. The HRXRD pattern of calcined and sintered pellet shows the pure cubic phase with P m 3 ¯ m space group symmetry through the Rietveld refinement. The study of the electron-density distribution of calcined powder and sintered pellet calculated by the maximum entropy method reveals the presence of oxygen vacancies at the octahedral site in the sintered sample. The microstructure of the fracture surface of the sintered sample indicates two types of grain with a relative density of 93.7% through FESEM. The Raman analysis confirms the distortion along the c-axis and oxygen vacancies in the octahedral site of BZHYN ceramic. Impedance spectroscopy measurement was conducted in the temperature range of 50 to 700 °C and frequency range of 1 Hz to 10 MHz. The Nyquist plots obtained in the temperature range of 350–700 °C reveal three distinct relaxation processes attributed to grain, grain boundary, and electrode effect. The temperature-dependent exponent (n) associated with grain and grain boundary decreases with the increase in temperature, indicating that large polaron hopping is involved in the electrical conduction mechanism. [ABSTRACT FROM AUTHOR]

1-Introduction Soil erosion is one of the most important environmental issues in the world; so that, soils that are affected by degradation processes such as compaction, loss of nutrients and water retention capacity lead to soil erosion and loss in fertile agricultural lands. Gully erosion is the most severe type of water erosion, which is one of the main factors of land degradation. Spatial forecasting of gully erosion using models that are based on land sensitivity to gully erosion and their output leads to the preparation of gully erosion risk maps in the form of gully erosion is the most suitable strategy for land management planning in watersheds to prevent gully erosion. If there is a zoning map of gully erosion in watersheds, economic and social damages could be controlled through appropriate measures and strategies in addition to preventing heavy costs to control gully erosion. According to the available models and relationships, it can be concluded that the most efforts in gully erosion modeling have been focused on the location of gullies as well as predicting the possibility or non-possibility of gully occurrence; So that, only a part of the effective factors have been investigated due to variability of factors affecting the gully. While the present research aims to determine the most important factors affecting the gully erosion and prioritize the factors and finally zoning the gully points and sensitivity to the gully in Maroon watershed. Maroon watershed is located in the southern and southwestern slopes of the Middle Zagros. The area of the watershed is 3837 km2, the maximum height from the sea level is 3420.3 m and the minimum is 372.8 m. The average annual rainfall is 815 mm, the long-term average annual temperature in te representative stations of the plains is and the stations located in the highlands are 19 and 16 ºC respectively. According to the Dumarten system, the climate type is Mediterranean to semi-arid. In summary, the present research was carried out as follows: 1) Selecting the area, preparation of the distribution map of gully occurrence (dependent variable) and its random division into two training or calibration (70 percent) and experimental or forecasting (30 percent) categories, 2) preparation of the maps of 23 effective factors (independent variables), 3) selecting the effective factors using alignment test between the effective factors and the occurrence of gullies, 4) running the model, 5) validation and evaluation of the model, and 6) preparation of a zoning map of susceptibility to gully erosion. In the present study, the location of gullies created in the Takht Daraz watershed was first recorded by field visit, and based on the recorded points, they were divided into two groups of training data (70%) and experimental data (30%) respectively in order to calibrate and validate the models. 23 factors affecting the occurrence of gully erosion were identified based on scientific references and watershed conditions, and their maps were prepared in ARCGIS environment. The weight of these factors was determined based on the frequency ratio, and then the correlation of the effective factors was investigated using the collinearity test. In the next step, the maximum entropy model was calibrated and validated using the weighted data of the effective factors and the spatial training and experimental data of the gully distribution. The Jackknife test and the receiver operating characteristic index were respectively used to determine the thresholds of effective factors in the occurrence of gully erosion as well as evaluating the efficiency of the studied models. 3-Results According to the collinearity analysis, the variance inflation rate of the investigated variables was less than 0.5, which indicates the non-collinearity between the investigated variables and all the variables were included in the modeling process. Based on the results of MaxEnt model (maximum entropy), a gully erosion sensitivity map was obtained, based on which the very high sensitivity class with 52 percent of gully occurrences has the highest density of gullies; So that the ratio of the frequency of gullies in this class was 6.73 and the Seed Cell Index area was 0.15. The very low sensitivity class has the lowest amount and frequency ratio of gully by 0.02 and nuclear cell surface index at the rate of 46.8. According to the information obtained from the map, the highest percentage of the area is related to very low sensitivity class (46%), which has eight pixels of gullies, and the lowest area of the watershed is related to the very high sensitivity class (8%) which has 420 pixels of gully. In general, based on the maximum entropy method, 34% of the watershed is in the medium to very high sensitivity class. Also, the validation results of the maximum entropy model based on the index of the area under the curve (AUC) showed that the area under the curve in this method was 0.85, which indicates the high accuracy of the maximum entropy model. 4-Discussion & Conclusions In the present study, the spatial distribution of gullies and its relationship with the effective environmental parameters of the Takht Daraz basin have been used in order to evaluate the sensitivity of gully erosion. For this purpose, different methods and models including witness weight, belief model and entropy method have been used to prepare a gully erosion sensitivity map. Google Earth image interpretation, past research and extensive field visits were used to prepare the gully distribution map, and 70% of the gullies were used for modeling and 30% for validation. In the field of effective parameters in gully erosion, 23 effective parameters were identified and prepared using research literature, access to data and environmental conditions of the study area. The results showed that the entropy model with the area under the curve (AUC) of 0.86 had high prediction accuracy. The results of the effective factors on gully erosion showed that according to the Jackknife test, the lithological factor had the greatest effect on the areas under the curve. The results of determining the parameters weight also showed that different parameters have different participation rates in the gully erosion occurrence; so that, the parameters of land-use and geology had the largest share of participation in the occurrence of gully erosion, with a contribution of 24% and 18% respectively. [ABSTRACT FROM AUTHOR]

A multimodal distribution based uncertainty analysis method for cross-domain aircraft morphing wing mechanisms is proposed to address the engineering issue of the reliability of morphing mechanisms. This method is based on Gaussian mixture model, isotropic sparse mesh method combined with maximum entropy method analysis. In the working environment of the morphing wings, the external load exhibits a multimodal distribution with changes in flight altitude and geographical location. Traditional uncertainty methods are difficult to accurately determine the reliability of aircraft under the influence of multiple variable influencing factors. Therefore, the proposed method is proposed to evaluate the reliability of morphing wing mechanisms. Firstly, a Gaussian mixture model is used to establish the mixture density function of the pressure and the leading edge size of the variant aircraft. Secondly, the integral points and weights of the multimodal random variables are calculated by the sparse grid method. Finally, an adaptive convergence mechanism is used to improve the uncertainty propagation accuracy. After a mathematical example and two engineering examples, it can be considered that the proposed method has a certain reference value in analyzing the uncertainty propagation under the multimodal distribution state of multiple factors. [ABSTRACT FROM AUTHOR]

Fluorinated gases, e.g., CF3I, C3F8, C4F8, C4F7N, and C5F10O, show potential to replace SF6 in power industry due to their high dielectric strength and low global warming potential. However, particle condensation from arc plasmas of these compounds may reduce dielectric performance. We perform a systematic investigation of particle condensation in two-temperature (2T) arc plasmas of various SF6 replacements mixed with CO2, N2, and O2, by the Gibbs free energy minimization and entropy maximization methods. The influences of buffer gases, non-equilibrium degree, and gas pressure on particle condensation are discussed in various cases. The results indicate that O2 is necessary to prevent graphite formation in carbon–fluorine gaseous arcs, and specific mixing ratios of CO2 and N2 are required to avoid graphite and iodine crystals in CF3I arc plasmas. The relationship between condensation temperature and non-equilibrium degree is complex, with peaks and valleys observed for graphite and iodine crystal condensation temperatures. Moreover, different calculation methods (Gibbs free energy minimization versus entropy maximization) show varying sensitivity of condensation temperatures to pressure changes. All the above findings highlight the importance of considering non-equilibrium effects and multiple condensed species in evaluating arc plasma compositions of SF6 replacements. [ABSTRACT FROM AUTHOR]

In this study, PROP adsorption was investigated using activated carbon derived from Bactris Guineensis residues and physical statistical modeling. The characterization results indicate high specific surface areas (624.72 and 1125.43 m2 g−1) and pore diameters (2.703 and 2.321 nm) for the peel and stone-activated carbon, respectively. Adsorption equilibrium was investigated at different temperatures (298 to 328 K), and it was found that the adsorption capacity increased with temperature, reaching maximum values of 168.7 and 112.94 mg g−1 for the peel and stone-activated carbon, respectively. The application of physical statistical modeling indicates that a monolayer model with one energy site is adequate for describing both systems, with an R2 above 0.986 and a low BIC of 20.021. According to the steric parameters, the density of molecules per site tends to increase by 116.9% for the stone and 61.6% for the peel. In addition, the model indicates that the number of molecules decreases with increasing temperature from 1.36 to 0.81 and from 1.03 to 0.82. These results indicate that temperature controls the number of receptor sites and the orientation in which propranolol is adsorbed at the surface. The adsorption energies were similar for both systems (approximately 10 kJ mol−1), which indicates that the adsorption occurred due to physical interactions. Finally, the application of thermodynamic potential functions indicates that the maximum entropy is reached at concentrations of half-saturation (Ce 3.85 and 4.6 mg L−1), which corresponds to 1.60 × 10–18 and 1.86 × 10–18 kJ mol−1 K−1 for the stone and peel, respectively. After this point, the number of available sites tends to decrease, which indicates the stabilization of the system. The Gibbs energy tended to decrease with increasing concentration at equilibrium, reaching minimum values of − 1.73 × 10–19 and − 1.99 × 10–19 kJ mol−1, respectively. Overall, the results obtained here further elucidate how the adsorption of propranolol occurs for different activated carbons from the same source. [ABSTRACT FROM AUTHOR]

This study proposes the foremost application of the entropy concept to investigate the streamwise S-type mean velocity distribution with infection-phenomenon in turbulent open-channel flows and turbulent Couette flows. Probability based fractional entropy is used for this study. Using the concept of entropy maximization the velocity distribution models are derived. Proposed models are fully analytical and applicable to the whole flow depth in both for open-channel flows and the turbulent Couette flows. All the proposed models are validated with different experimental data sets, field data and DNS data available in the literature. The validation results show good performance for both of the velocity distribution models. Apart form validation, the present velocity distribution models are compared with deterministic (non-entropy based) models since there exists no entropy based velocity distributions to describe the infection-phenomenon in turbulent open-channel flows and turbulent Couette flows. To understand the performance of these models, the relative error and the coefficient of determination ( R 2 ) are computed. Results of the error analysis show the good efficiency of the proposed models for several experimental and field data sets. [ABSTRACT FROM AUTHOR]

The acquisition of vegetation coverage information is crucial for crop field management, and utilizing visible light spectrum vegetation indices to extract vegetation coverage information is a commonly used method. However, most visible light spectrum vegetation indices do not fully consider the relationships between the red, green, and blue bands during their construction, making it difficult to ensure the accurate extraction of coverage information throughout the crop's entire growth cycle. To rapidly and accurately obtain potato vegetation coverage information, drones were used in this study to obtain high-resolution digital orthoimages of potato growth stages. Based on the differences in the grayscale values of potato plants, soil, shadows, and drip irrigation belts, this study presents a combination index of blue and green bands (BGCI) and a combination index of red and green bands (RGCI). The vegetation index intersection method was used with 10 vegetation information indices to extract vegetation coverage, and the differences in extraction accuracy were compared with those of the maximum entropy method and bimodal histogram method. Based on the high-precision fraction vegetation coverage (FVC) extraction results, the Pearson correlation coefficient method and random forest feature selection were used to screen 10 vegetation and 24 texture features, and the top six vegetation indices most strongly correlated with the FVC were selected for potato growth stage FVC estimation and accuracy verification. A high-precision potato vegetation coverage estimation model was successfully established. This study revealed that during the potato tuber formation and expansion stages, the BGCI combined with the vegetation index intersection method achieved the highest vegetation coverage extraction accuracy, with overall accuracies of 99.61% and 98.84%, respectively. The RGCI combined with the vegetation index intersection method achieved the highest accuracy, 98.63%, during the maturation stage. For the potato vegetation coverage estimation models, the model based on the BGCI achieved the highest estimation accuracy (R2 = 0.9116, RMSE = 5.7903), and the RGCI also achieved good accuracy in terms of vegetation coverage estimation (R2 = 0.8987, RMSE = 5.8633). In the generality verification of the models, the R2 values of the FVC estimation models based on the BGCI and RGCI were both greater than 0.94. A potato vegetation coverage estimation model was constructed based on two new vegetation information indices, demonstrating good accuracy and universality. [ABSTRACT FROM AUTHOR]

Aim s : Haemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease transmitted by rodents. The distribution of HFRS in the European part of Russia has been studied quite well; however, much less is known about the endemic area in the Russian Far East. The mutual influence of the epidemic situation in the border regions and the possibility of cross‐border transmission of infection remain poorly understood. This study aims to identify the spatiotemporal hot spots of the incidence and the impact of environmental drivers on the HFRS distribution in the Russian Far East. Methods and Results: A two‐scale study design was performed. Kulldorf's spatial scan statistic was used to conduct spatiotemporal analysis at a regional scale from 2000 to 2020. In addition, an ecological niche model based on maximum entropy was applied to analyse the contribution of various factors and identify spatial favourability at the local scale. One spatiotemporal cluster that existed from 2002 to 2011 and located in the border area and one pure temporal cluster from 2004 to 2007 were revealed. The best suitability for orthohantavirus persistence was found along rivers, including those at the Chinese–Russian border, and was mainly explained by land cover, NDVI (as an indicator of vegetation density and greenness) and elevation. Conclusions: Despite the stable incidence in recent years in, targeted prevention strategies are still needed due to the high potential for HRFS distribution in the southeast of the Russian Far East. [ABSTRACT FROM AUTHOR]

The formation of congestion on an urban road network is a key issue for the development of sustainable mobility in future smart cities. In this work, we propose a reductionist approach by studying the stationary states of a simple transport model using a random process on a graph, where each node represents a location and the link weights give the transition rates to move from one node to another, representing the mobility demand. Each node has a maximum flow rate and a maximum load capacity, and we assume that the average incoming flow equals the outgoing flow. In the approximation of the single-step process, we are able to analytically characterize the traffic load distribution on the single nodes using a local maximum entropy principle. Our results explain how congested nodes emerge as the total traffic load increases, analogous to a percolation transition where the appearance of a congested node is an independent random event. However, using numerical simulations, we show that in the more realistic case of synchronous dynamics for the nodes, entropic forces introduce correlations among the node states and favor the clustering of empty and congested nodes. Our aim is to highlight the universal properties of congestion formation and, in particular, to understand the role of traffic load fluctuations as a possible precursor of congestion in a transport network. [ABSTRACT FROM AUTHOR]