Maximum Entropy Research Articles

Risk Assessment of Global Animal Melioidosis Under Current and Future Climate Scenarios

Melioidosis is a zoonotic disease that is caused by Burkholderia pseudomallei, which is a serious public health and safety risk. In order to explore the global animal melioidosis risk distribution and its dynamic response to future climate scenarios, we collected global data about reported animal incidence sites. Data regarding the density of Burkholderia pseudomallei in the environment were created by collecting and sorting information about the Burkholderia pseudomallei occurrence sites in contaminated air, soil, and water. Combined with bioclimatic variables, the maximum entropy (MaxEnt) niche was modeled for global animal melioidosis. The findings indicate that under current bioclimatic conditions, global animal melioidosis risk regions are concentrated between 30° S and 30° N, with high-risk areas being mainly in Central America, the northern part of South America, and eastern and southern India, among others. Most countries will expand their risk regions under future climatic scenarios. Melioidosis risk expanding towards higher northern latitudes has led to new epidemic areas. In addition, the melioidosis risk area will contract in some areas. Therefore, we have provided a basis for global melioidosis surveillance and propose feasible strategies for prevention and control in high-risk regions, which will help countries to carry out targeted surveillance and prevention to reduce risks and losses.

Evidence of Magnetocaloric Effect in (Fe63Ni37)89B11 Nanostructured Magnetic Alloys

Abstract The magnetocaloric effect associated with magnetic entropy changes (ΔS M ) and phase transitions in (Fe63Ni37)89B11 (FeNiB) powder alloys was investigated. For this purpose, the particle size of the samples was reduced under milling times of 0 (FNB) and 36 hours (FNB36). X-ray diffraction and Mössbauer spectroscopy results showed the formation of nanostructured magnetic alloys and the coexistence of γ-FCC, α-BCC, and oP-(Fe, Ni)B phases in agreement with the INVAR region. M(H) measurements revealed that both alloys are ferromagnetic soft at room temperature, with coercive field values below ~ 48 Oe. A detailed analysis of the magnetic phase transition using the modified Arrott plot and critical isotherm plots yields critical exponents (β = 0.27, γ = 0.92, and α = 4.4) close to the theoretical exponents obtained from the Tricritical Mean Field model. Moreover, a maximum magnetic entropy change (ΔS M ) was evidenced around the phase transition (T C ) at ~ 330 K (− ΔS M of 2.58 J kg−1 K−1) for FNB and ~ 415 K (− ΔS M of 0.4 J kg−1 K−1) for FNB36 with an applied field of 1.3 T. The relative cooling power and the temperature-averaged entropy change values were determined, and they exhibited a linear dependency as function of the external field. These findings give a good insight towards the advancements of FNB-based alloys for potential room-temperature magnetic refrigeration technology.

Unveiling A-site substituent’s influence on magnetocaloric response in La2-xNaxNiMnO6 (0.0 ≤ x ≤ 1.0) double perovskite manganite materials

Abstract In this study, the compounds of La2-xNaxNiMnO6 (x = 0.0, 0.1, 0.2, 0.3, 0.5, and 1.0) double perovskite manganites have been produced by employing the sol-gel method to investigate their magnetocaloric effect. From X-Ray Diffraction analysis, all compounds crystallized in rhombohedral structure form with the $${\rm{R}}\bar{3}{\rm{c}}$$ R 3 ¯ c space group confirmed using the Rietveld refinement technique. The compounds show a magnetic phase transition from ferromagnetic to paramagnetic according to the temperature-dependent magnetization measurement. The phase transition temperatures sharply increased with Na doping (x = 0.1) to 277.8 K which can be considered as under room temperature level and then slowly decreased with further Na concentration to 262.3 K. Using an external magnetic field-dependent magnetization measurements, the isothermal magnetization curves were obtained, and these curves used to find the magnetic entropy change values, which give info about compounds’ characteristics of magnetic cooling performance. Under 5 T magnetic field change, the maximum magnetic entropy change value increased 10 times with a small amount of Na doping (x = 0.1), and compounds’ values changed from 0.21 to 1.29 Jkg−1K−1 for x = 0.0 to 1.0 compounds, respectively. Arrott plots were graphed by using isothermal magnetization curves to demonstrate all the compounds have a 2nd order magnetic phase transition which supports that these compounds can be candidates as magnetic refrigerants. Graphical Abstract

A novel approach to seismic fragility evaluation of underground structures considering hybrid epistemic uncertainties of both seismic demand and capacity

Precise seismic fragility analysis holds significant importance in the evaluation of seismic resilience for underground structures. However, the conventional seismic fragility analysis of underground structures usually ignores the hybrid epistemic uncertainties caused by the limited samples of both seismic demand and thresholds and deterministic boundaries between different limit states, which can inevitably cause errors in the seismic resilience evaluation of underground structures. Thus, focusing on the quantification of epistemic uncertainties, this paper aims to propose an approach to seismic fragility evaluation of underground structures considering hybrid epistemic uncertainties of both seismic demand and capacity. In this approach, the analytical formulation of seismic fragility considering the fuzziness of limit states is firstly derived via adopting the entropy equivalence method. Then, the non-parametric Bootstrap method and maximum entropy principle, as well as the Copula theory are combined to establish a probability model characterizing the statistical uncertainties of both seismic demand and capacity. On this basis, the variability of failure probability of underground structures is quantified, and the envelope fuzzy seismic fragility is obtained. Moreover, the influences of the coupling effect of statistical uncertainty and fuzziness on the seismic fragility of underground structures are also analyzed in this paper. The results show that the seismic fragility of underground structures based on limited samples of both seismic demand and thresholds and deterministic boundaries between different limit states has significant variability, and the variability degree of fragility is various under different ground motion intensities. Besides, the envelope fuzzy seismic fragility curves can effectively reflect the coupling effect of statistical uncertainty and fuzziness and adequately characterize the variability of estimated seismic fragility, which can provide a more accurate basis for seismic resilience evaluation of underground structures.

Spatial prediction of forest fires in India: a machine learning approach for improved risk assessment and early warning systems.

Forest fires pose a significant ecological and environmental threat globally, and India has seen a marked increase in both the frequency and severity of these events in recent years. This has led to extensive damage to natural resources, including forests and wildlife habitats. Effective management strategies are essential to mitigate these impacts, and this study aims to contribute to that effort through spatial prediction of forest fires in India using machine learning techniques. The research begins by analyzing spatial patterns and trends of forest fires and identifying key factors contributing to their occurrence. It then explores the relationships between forest fires and these factors. Using data from 2001 to 2020, the study develops a probability map of forest fire occurrences in India, leveraging data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on fire points and burned areas and the European Space Agency Climate Change Initiative (ESA-CCI) land use land cover. The analysis incorporates various fire occurrence conditioning factors, including climate variables (precipitation, air temperature, specific humidity, near-surface wind speed, net longwave radiation, and land surface temperature for both daytime and nighttime), biophysical factors (aspect, slope, elevation, soil moisture, and Normalized Difference Vegetation Index), and other factors such as proximity to railways, roads, and waterways. The maximum entropy model (MaxEnt) was employed to identify these factors and generate the probability map. The results indicate that forest fires are predominantly concentrated in three major regions: Northeast India, Uttarakhand-Himachal, and the Deccan Plateau. The study also highlights the link between forest fires and Sustainable Development Goals (SDGs 1, 2, 3, 5, 6, 9, 12, 13, and 15). The findings can inform the identification of vulnerable areas, enabling the implementation of preventative measures. This research is crucial as it not only pinpoints regions at risk of forest fires but also elucidates their impacts on the environment and local livelihoods, thereby enhancing risk assessment and early warning systems.

Response to music on the nonlinear dynamics of human fetal heart rate fluctuations: A recurrence plot analysis.

Music has been shown to influence physiological functions in humans, but its effects on fetal heart rate variability (fHRV) are not well understood. This study aimed to assess the response of classical music exposure on the nonlinear behavior of fetal heart rate fluctuations in fetuses between 32 and 40 weeks of gestation using recurrence quantification analysis (RQA). We collected R-R time series from 37 fetuses in the third trimester following a study into four stages: PRE (baseline), STIM1 (first musical piece), STIM2 (second musical piece), and POST (post-exposure). The fetal R-R time series from each stage were evaluated using RQA indices such as determinism (DET), average diagonal line length (L), maximum line length (LMAX), entropy (ENTR), and trapping time (TT), as well as conventional linear indices like SDRR (standard deviation of R-R intervals). Results revealed three main points. First, there was an increase in DET, L, LMAX, and TT, with a decrease in ENTR in the POST stage compared to PRE, indicating more regular and predictable patterns. Second, the STIM2 stage enhanced the predictability and stability of cardiac dynamics compared to PRE, as indicated by L, LMAX, and TT. Third, no significant changes were observed in conventional indices, except for an increase in SDRR in the POST stage compared to STIM1. These findings suggest a reduction in complexity and nonlinear behavior of fHRV patterns after musical stimulus. The increase in SDRR during the POST stage appears to coincide with fetal movements, as indicated by the number of fetal accelerations found.

Reinforced fuzzy neural networks based on maximum entropy clustering and conjugate gradient method

Reinforced fuzzy neural networks based on maximum entropy clustering and conjugate gradient method

Robustness and limitations of maximum entropy in plant community assembly

Robustness and limitations of maximum entropy in plant community assembly

Determination of Diffraction Elastic Constants Using the Maximum Entropy Method

X-ray diffraction methods are an established technique to analyze residual stresses in polycrystalline materials. Using diffraction, lattice plane distances are measured, from which residual stresses can be calculated by using diffraction elastic constants which can be inferred from experimental measurements or calculated based on micromechanical model assumptions. We consider two different generalizations of existing micromechanical models for the case of texture-free, i.e. statistically isotropic, single-phase polycrystals. The first is based on the singular approximation method of classical micromechanics, from which existing Voigt, Reuss, Hashin-Shtrikman and self-consistent methods are recovered. The second approach, which is newly proposed in this work, is based on the micromechanical Maximum Entropy Method. Both approaches are applied to the problem of calculating diffraction elastic constants of texture-free cubic polycrystals and are found to be consistent with each other in that case. Full-field FFT simulations are used to validate the analytical models by simulating X-ray diffraction measurements of copper. In the simulative setting, many sources of experimental measurement error are not present, which results in a particularly accurate validation of theoretical bounds and approximations. The first core result of the paper is a formulation of diffraction elastic constants for texture-free polycrystals in terms of the macroscopically measurable effective shear modulus. These diffraction elastic constants can be adapted to the properties of a given material sample. The second core result is the validation of the Maximum Entropy Method for X-ray diffraction stress analysis of texture-free single-phase materials as a preliminary step before extending the method to textured and multi-phase materials.

Analysis of the Distribution Pattern of Asparagus in China Under Climate Change Based on a Parameter-Optimized MaxEnt Model

Asparagus (Asparagus officinalis L.) has high health and nutritional values, but the lack of scientific and rational cultivation planning has resulted in a decline in asparagus quality and yield. Important soil, climatic, anthropogenic, and topographic environmental factors influencing the distribution of asparagus cultivation were chosen for this study. The Kuenm package in the R language (v4.2.1) was employed to optimize the maximum entropy model (MaxEnt). Pearson’s correlation analysis, optimized MaxEnt, and geographic information spatial technology were then utilized to identify the main environmental factors that influence suitable habitats for asparagus in China. Potential distribution patterns, migration, and changes in trends concerning the suitability of asparagus in China under various historical and future climate scenarios were modeled and projected. Human activities and climate factors were found to be the primary environmental factors that influence the suitability distribution of asparagus cultivation in China, followed by soil and topographic factors. Historical suitable habitats covered 345.6 × 105 km2, accounting for 36% of China. These habitats are projected to expand considerably under future climatic conditions. This research offers a basis for the rational planning and sustainable development of asparagus cultivation.

Drowning overconfidence with uncertainty: mitigating deep learning overconfidence in flood depth super-resolution through maximum entropy regularization

Drowning overconfidence with uncertainty: mitigating deep learning overconfidence in flood depth super-resolution through maximum entropy regularization

Statistical Inference Based on Censored Data of Entropy for Lomax Distribution

The current research centers around entropy, which is officially defined as an indicator of the possible amount of information of the Lomax (Lo) distribution that quantifies the uncertainty of random variables. When the parameters are unknown under adaptive progressive Using type-II censored data, the entropy maximum likelihood estimate (MLE) is computed and the bootstrap confidence intervals of entropy is displayed. The Bayes estimator of entropy is demonstrated using both the symmetric and asymmetric loss functions. In the meantime, the posterior is also computed to assess how well the entropy estimators perform in relation to various loss functions. The Bayesian estimates were obtained in a numerical simulation applying the method of Markov Chain Monte Carlo (MCMC). Then, using Monte Carlo simulations, various approaches are compared to identify the believable intervals of the entropy's highest posterior density (HPD). Lastly, the recommended methods are illustrated using a numerical example.

Exploring Coding Sequence Length Distributions Across Taxonomic Kingdoms Based on Maximum Information Principle

Background: Genetic information about organisms' traits is stored and encoded in deoxyribonucleic acid (DNA) sequences. The fundamental inquiry into the storage mechanisms of this genetic information within genomes has long been of interest to geneticists and biophysicists. Objective: The objective of this study was to investigate the distribution of coding sequence (CDS) lengths in species genomes across different kingdoms. Methods: In this study, we used the maximum entropy principle and the gamma distribution model based on a comprehensive dataset including viruses, archaea, bacteria, and eukaryote species Results: Our study result revealed unique patterns in CDS length distributions among kingdoms and CDS lengths exhibit a right-skewed distribution, with varying preferences among kingdoms. Eukaryotes displayed bimodal distributions, with CDS sequences longer than those of prokaryotes. Fitting the gamma distribution model revealed differences in shape and scale parameters among kingdoms, with eukaryotes exhibiting larger scale parameters, indicating longer CDS sequences. Additionally, analysis of moments highlighted the complexity of eukaryotic genomes relative to prokaryotes. Conclusion: This study result deepens our understanding of genome evolution and provides valuable insights for biological research.Conclusion: This study result deepens our understanding of genome evolution and provides valuable insights for biological research.

Re-emergence of Aedes aegypti (Linnaeus) in Egypt: Predicting distribution shifts under climate changes.

Aedes aegypti, the primary vector of several medically significant arboviruses-including dengue fever, yellow fever, chikungunya and Zika-was successfully eradicated from Egypt in 1963. However, since 2011, there have been increasing reports of its re-emergence, alongside dengue outbreaks in southern Egyptian governorates, raising significant public health concerns. This study aimed to model the current and future distribution of Ae. aegypti in Egypt. Local occurrence data were integrated with bioclimatic, anthropogenic and biological environmental variables to identify key factors influencing the distribution of Ae. aegypti. Maximum entropy (MaxEnt) modelling demonstrated strong predictive performance (area under the receiver operating characteristic curve [AUC] mean = 0.975; true skill statistic [TSS] mean = 0.789). The key determinants of habitat suitability were identified as human population density, annual precipitation and the normalised difference vegetation index (NDVI). Current predictions indicate that suitable habitats for Ae. aegypti are concentrated in the Nile Valley, Nile Delta, Fayoum Basin, Red Sea coast and South Sinai. Projections under future climate change scenarios suggest an expansion of suitable habitats, particularly in the Nile Delta region. By 2050, the model predicts a 61%-68% increase in suitable habitat area, with a further 64%-69% increase by 2070, depending on the future climate scenarios. These findings are crucial for informing vector control and disease prevention strategies, particularly considering Egypt's status as one of the world's leading tourist destinations.

Morphology-Transport Coupling and Dissipative Structures in PEO-PS+LiTFSI Electrolytes In-Operando Conditions.

A Single-Chain-in-Mean-Field (SCMF) algorithm was introduced to study block copolymer electrolytes in nonequilibrium conditions. This method self-consistently combines a particle-based description of the polymer with a generalized diffusion equation for the ionic fluxes, thus exploiting the time scale separation between fast ion motion and the slow polymer relaxation and self-assembly. We apply this computational method to study ion fluxes in electrochemical cells containing poly(ethylene oxide)-polystyrene (PEO-PS) block copolymers with added lithium salt. Blocking of the anion fluxes by the electrodes in-operando conditions polarizes the cells and results in an inhomogeneous salt-concentration profile. This gradient of salt concentration triggers lamellae-to-disorder and disorder-to-lamellae transitions near the electrodes, in good agreement with previous experimental observations. The effects of the selectivity of the electrode surface, the salt concentration and the voltage applied to the cell are systematically studied. For PEO-selective surfaces, the lamellae parallel to the electrode that forms at low applied potentials transition to a bicontinuous morphology at high applied potentials in order to allow ion transport through the insulating PS layers. The formation of this dissipative structure, which is unexpected considering the equilibrium behavior of the material, is in line with the principle of maximum entropy production. In summary, the transport and morphology in PEO-PS electrolytes are strongly coupled: ionic currents influence self-assembly, which in turn modulates the ionic fluxes in the cell.

Impact of Oceanographic Variability on Chlorophyll-a Concentration and Sea Surface Temperature in North Maluku Waters and its Influence on Fish Abundance

The waters of North Maluku are rich in fisheries resources, which are important for the local economy and marine biodiversity. The variability of oceanographic conditions, such as chlorophyll-a and sea surface temperature, plays a significant role in determining fish abundance in this region. This study analyzes the seasonal variations of chlorophyll-a and sea surface temperature during 2021, as well as their impact on the productivity of the waters. Satellite imagery data of chlorophyll-a and sea surface temperature were analyzed using spatial interpolation. The results show seasonal fluctuations in chlorophyll-a concentration and sea surface temperature, where higher chlorophyll-a concentrations support primary productivity and fish abundance during mid-year. The highest chlorophyll-a concentration occurred in June (0.212 mg/m³) and the lowest in February (0.103 mg/m³). The negative phase of the Indian Ocean Dipole (IOD) reduces upwelling intensity, decreases nutrient availability, and impacts the decline in primary productivity. The highest sea surface temperature was recorded in November (30.99°C) and the lowest in August (29.87°C). These oceanographic variations affect the availability of phytoplankton, which is crucial in the marine food chain, and have implications for sustainable fisheries management in North Maluku. The results of this study can also serve as a reference for fish distribution or abundance modeling, such as with the maximum entropy method, to better understand the factors influencing fish distribution patterns in the region.

Influencia de factores determinantes en la distribución y nicho ecológico de abejas melíferas (Hymenoptera: Apidae) en Ibarra, Ecuador

Apis mellifera L. is a species of vital importance as pollinator, whose presence is fundamental for the proper development of ecosystems. However, various anthropogenic factors cause alterations in its ecological niche. This research analyzes the presence of Apis mellifera along with bioclimatic, environmental, and agricultural variables using the maximum entropy model (MaxEnt) to determine the niche and potential distribution of the species within the study area. As a result, it was determined that the ecological niche of honeybees is mainly determined by factors such as the type of vegetation cover, honey production, and the annual range of temperature (Bio7), identifying areas with high suitability covering a total of 645.99 hectares corresponding to grasslands and crops, especially in the San Antonio de Ibarra parish. The model was validated using the AUC (0.904) and TSS (0.618) values. Certainly, it is evident that Apis mellifera finds its ideal habitat in small and specific areas where it would be feasible to promote the development of beekeeping.

Black hole from entropy maximization

One quantum characterization of a black hole motivated by (local) holography and thermodynamics is that it maximizes thermodynamic entropy for a given surface area. In the context of quantum gravity, this could be more fundamental than the classical characterization by a horizon. As a step, we explore this possibility by solving the 4D semiclassical Einstein equation with many matter fields. For highly excited spherically symmetric static configurations, we apply local typicality and estimate the entropy including self-gravity to derive its upper bound. The saturation condition uniquely determines the entropy-maximized configuration: self-gravitating quanta condensate into a radially uniform dense configuration with no horizon, where the self-gravity and a large quantum pressure induced by the curvatures are balanced and no singularity appears. The interior metric is a self-consistent and nonperturbative solution in Planck’s constant. The maximum entropy, given by the volume integral of the entropy density, agrees with the Bekenstein-Hawking formula through self-gravity, deriving the Bousso bound for thermodynamic entropy. Finally, 10 future prospects are discussed, leading to a speculative view that the configuration represents a quantum-gravitational condensate in a semiclassical manner. Published by the American Physical Society 2025

Room temperature magnetocaloric effects in monoclinic Cr3Te4

We report a systematic investigation of anisotropic magnetocaloric effects in a crystalline, monoclinic Cr3Te4 sample grown by the chemical vapor transport (CVT) method. The maximum magnetic entropy change −ΔSmaxM is 3.31 J kg−1 K−1 for the c axis (3.16 J kg−1 K−1 for the ab-plane) and the relative cooling power (RCP) is 340 J kg−1 for the c axis (350 J kg−1 for the ab-plane) near the Curie temperature with a magnetic field (μ0H) change of 9 T. With the scaling analysis of ΔSM, all rescaled ΔSM(T, H) curves collapse onto a single universal curve, indicating a second-order magnetic phase transition in Cr3Te4. Furthermore, −ΔSmaxM follows the power law of Hn with n = 0.656 ± 0.005. The RCP and δTFWHM have Hc and Hb dependence on field, with c = 1.179 ± 0.011 and b = 0.498 ± 0.005, respectively, which led us to estimate the critical exponents of β = 0.359 ± 0.013, γ = 1.646 ± 0.057, and δ = 5.578 ± 0.190.

Numerical Analysis of Magnetohydrodynamics Mixed Convection and Entropy Generation in a Double Lid‐Driven Cavity Using Ternary Hybrid Nanofluids

AbstractThe present study numerically investigates the effects of a magnetic field on mixed convection flow and entropy generation within a double lid‐driven square cavity filled with a hybrid nanofluid. The flow is induced by two isothermally heated semi‐circles located on the bottom and left walls of the cavity. The cavity is filled with a ternary composition of hybrid nanofluid (aluminum oxide/silver/copper oxide‐water) and is exposed to a uniform magnetic field. The velocity ratio of the moving lids and the radius ratio of the semi‐circles are key parameters in the analysis. The study employs the finite volume method and full multigrid acceleration to solve the coupled continuity, momentum, energy, and entropy generation equations, along with the relevant boundary conditions. Key dimensionless parameters considered include the Hartmann number (0 ≤ Ha ≤ 100), Richardson number (0.01 ≤ Ri ≤ 1), hybrid nanofluid volume fraction (3% ≤ ϕ ≤ 12%), internal semi‐circle radius ratio (β = 0.5 and 1), and velocity ratio (−2 ≤ λ ≤ 2). Results revealed that the optimal heat transfer is achieved for Ri = 0.04, Ha = 100, ϕ = 0%, β = 1, and λ = 0.5 with 63% enhancement. Moreover, the maximum entropy generation rates are obtained for the same parameters with a rate of 47%, reflecting the complex balance of enhanced heat transfer and associated irreversibility's. Results reveal also that heat transfer and entropy generation are a decreasing function of Hartmann number implying a suppress of fluid motion due to the Lorentz force. This study provides a valuable resource and parametric analysis for researchers and engineers, aiding in the design and optimization of thermal management systems for various industrial applications, including heat exchangers, nuclear reactors, and energy systems.