Maximum Entropy Research Articles

The Potential Habitat Response of Cyclobalanopsis gilva to Climate Change.

Cyclobalanopsis gilva, a valuable timber species in China, holds significant importance for understanding the constraints imposed by climate change on the dynamic geographic distribution of tree species. This study utilized the MaxEnt maximum entropy model to reconstruct the migratory dynamics of C. gilva geographical distribution since the Last Glacial Maximum. The objective was to comprehend the restrictive mechanisms of environmental factors on its potential geographical distribution, aiming to provide insights for mid-to-long-term afforestation planning of C. gilva. The optimized MaxEnt model exhibited a significantly high predictive accuracy, with an average AUC value of 0.949 ± 0.004 for the modern suitable habitat model of C. gilva. The total suitable habitat area for C. gilva in contemporary times was 143.05 × 104 km2, with a highly suitable habitat area of 3.14 × 104 km2. The contemporary suitable habitat was primarily located in the southeastern regions of China, while the highly suitable habitat was concentrated in eastern Fujian and central-eastern Taiwan. Bioclimatic variables such as mean diurnal range (Bio2), min temperature of coldest month (Bio6), precipitation of driest quarter (Bio17), and precipitation of driest month (Bio14) predominantly influenced the modern geographic distribution pattern of C. gilva, with temperature factors playing a leading role. With global climate warming, there is a risk of fragmentation or even loss of suitable habitat for C. gilva by 2050 and 2090. Therefore, the findings of this study can significantly contribute to initiating a habitat conservation campaign for this species.

Unveiling the overlooked: Current and future distribution dynamics of kissing bugs and palm species linked to oral Chagas disease transmission

Chagas disease, a neglected global health concern primarily transmitted through the bite and feces of kissing bugs, has garnered increasing attention due to recent outbreaks in northern Brazil, highlighting the role of oral transmission facilitated by the kissing bugs species Rhodnius robustus and Rhodnius pictipes. These vectors are associated with palm trees with large crowns, such as the maripa palm (Attalea maripa) and moriche palm (Mauritia flexuosa). In this study, we employ maximum entropy (MaxEnt) ecological niche models to analyze the spatial distribution of these vectors and palm species, predicting current and future climate suitability. Our models indicate broader potential habitats than documented occurrences, with high suitability in northern South America, southern Central America, central Africa, and southeast Asia. Projections suggest increased climate suitability by 2040, followed by a reduction by 2080. This study identifies present and future areas suitable for kissing bugs and palm tree species due to climate change, aiding in the design of prevention and management strategies.

Habitat for Coilia nasus in southern Zhejiang Province, China, based on a maximum entropy model

As an important fishery resource and endangered species, studying the habitat of Coilia nasus (C. nasus) is highly significant. This study used fishery survey data from southern Zhejiang coastal waters from 2016 to 2020, employing a maximum entropy model (MaxEnt) to map the habitat distribution of C. nasus. Model performance was evaluated using two metrics: the area under the curve (AUC) of the receiver operating characteristic curve for the training and test sets and true skill statistics (TSS). This study aimed to predict the habitat distribution of C. nasus and explore how environmental variables influence habitat suitability. The results indicated that the models for each season had strong predictive performance, with AUC values above 0.8 and TSS values exceeding 0.6, indicating that they could accurately predict the presence of C. nasus. In the study area, C. nasus was primarily found in brackish or marine waters near bays and coastal islands. Among all environmental factors, salinity (S) and bottom temperature (BOT) had the highest correlations with habitat distribution, although these correlations varied across seasons. The findings of this study provide empirical evidence and a reference for the conservation and management of C. nasus and for the designation of its protected areas.

Methods of medical image segmentation analysis to improve the effectiveness of diagnosing lung diseases

Methods of medical image segmentation analysis to improve the effectiveness of diagnosing lung diseases. The article aims to review selected methods for segmenting lung medical images based on computed tomography (CT) image data analysis. Included in the analysis are 11 different approaches, including edge algorithms such as Canny, Prewitt, Roberts, Sobel and Log, clustering-based algorithms such as Fuzzy C-means, genetic K-means, k-nearest neighbours, and neural networks such as perceptron, neural and fuzzy sets, and edge binarization techniques, among others. Each method was evaluated for its ability to segment lung structures on CT images accurately. Finally, the contour of the heart from the CT image was determined using the maximum entropy thresholding method. The juxtaposition of different approaches to medical image segmentation is an important contribution to developing medical diagnostic techniques, which can help improve the efficiency of diagnosing lung diseases.

Effects of environment and human activities on rice planting suitability based on MaxEnt model.

Rice is one of the major food crops, and the study of suitable planting areas for rice plays an important role in improving rice yield and optimizing the production layout. This study used Maximum Entropy (MaxEnt) model to simulate and predict the distribution of suitable rice planting areas in China from 1981 to 2020 by combining the climate, soil, and human activities, analyzed the spatial and temporal changes of suitable rice planting areas in China, and determined the main factors affecting rice planting suitability. The results indicated that the main factors influencing the distribution of suitable planting areas for rice in China were gross domestic product (GDP), population density (Pop), and annual sunshine duration (Sun), with human activities playing a dominant role. The high suitable planting areas of rice were mainly distributed in Hubei, Hunan, Jiangxi, Anhui, Guangdong, southeastern Sichuan and western Guizhou. The total suitable planting areas for rice were 346.00 × 104 km2, 345.66 × 104 km2, 347.01 × 104 km2, and 355.57 × 104 km2 from 1981 to 1990, 1991 to 2000, 2001 to 2010 and 2011 to 2020, respectively. With the passage of time, the area of unsuitable areas for rice gradually decreased, and the area of medium suitable areas increased, with large changes in the area of high- and low-suitable areas. Moreover, due to the transfer of a large number of rural laborers to the cities in recent years, the tension between people and land caused by the population explosion has led to the increasing impact of Pop on rice suitable areas and the relatively weakening of the impact of GDP on rice production interventions. The results can be used to provide scientific evidence for the management of rice cultivation and food production safety, with a view to reducing the impacts of climate change on agricultural production in the context of global climate change.

Predicting the climatic suitability of non-native drought resistant trees in the face of desertification – A case study with Argania spinosa in the Iberian Peninsula

Climate change is driving a poleward shift in the optimal distributions of terrestrial organisms, including trees. Consequently, trees are expected to decrease in climatically unfavourable areas as their optimal climatic conditions shift poleward. This is the case of Mediterranean High Nature Value agroforestry systems in Iberian Peninsula, dominated by holm oaks (Quercus rotundifolia) and cork oaks (Quercus suber), which are expected to shift their future distribution northward. Failure to keep pace with climate warming poses a heightened risk of extinction for these tree species, as well as the loss of crucial ecosystem services they provide. A similar situation is found for argan trees (Argania spinosa) in the southwestern region of Morocco, where it also supports an agroforestry system. The optimum climate for these trees is also shifting northward, toward the Iberian Peninsula. As the argan tree thrives in more arid conditions with irregular precipitation, the future climatic conditions in the Iberian Peninsula may become favourable for the persistence of this tree. In light of this situation, the assisted colonization of the argan tree could potentially contribute to protecting and preserving the tree-associated ecosystem services in Iberian Peninsula. We conducted climate suitability modelling using maximum entropy approach, for both the current and future timeframes, for North of Morocco and Algeria (native range) and the whole Iberian Peninsula (non-native range). Accordingly, there appears to be a significant area with highly suitable climatic conditions (>80 %) for argan tree occurrence, covering approximately 6.7 % of the territory, over 38,000 km2. This area is projected to increase significantly in the future, covering 9–22 % of the territory, or about 55,000 and 127,000 km2. With this study, we aim to contribute to the discussion on the benefits of assisted colonization of argan trees into Iberian Peninsula, as well as potential drawbacks such as the risk of this species becoming invasive.

Data-driven computational mechanics: comparison of model-free and model-based methods in constitutive modeling

In computational homogenization approaches, data-driven methods entail advantages due to their ability to capture complex behavior without assuming a specific material model. Within this domain, constitutive model-based and model-free data-driven methods are distinguished. The former employ artificial neural networks as models to approximate a constitutive relation, whereas the latter directly incorporate stress–strain data in the analysis. Neural network-based constitutive descriptions are one of the most widely used data-driven approaches in computational mechanics. In contrast, distance-minimizing data-driven computational mechanics enables substituting the material modeling step entirely by iteratively obtaining a physically consistent solution close to the material behavior represented by the data. The maximum entropy data-driven solver is a generalization of this method, providing increased robustness concerning outliers in the underlying data set. Additionally, a tensor voting enhancement based on incorporating locally linear tangent spaces enables interpolating in regions of sparse sampling. In this contribution, a comparison of neural network-based constitutive models and data-driven computational mechanics is made. General differences between machine learning, distance minimizing, and entropy maximizing data-driven methods are explored. These include the pre-processing of data and the required computational effort for optimization as well as evaluation. Numerical examples with synthetically generated datasets obtained by numerical material tests are employed to demonstrate the capabilities of the investigated methods. An anisotropic nonlinear elastic constitutive law is chosen for the investigation. The resulting constitutive representations are then applied in structural simulations. Thereby, differences in the solution procedure as well as use-case accuracy of the methods are investigated.

Invasive alien plants in the Qinghai-Tibetan Plateau (China): Current state and future predictions

Invasive alien plants have received increasing attention due to their worldwide environmental impacts, ecological concerns, and significant economic consequences. The Qinghai-Tibetan Plateau is an important global biodiversity hotspot hosting rich biodiversity and serving as a crucial component of the southwest ecological security barrier, with global conservation value. However, in recent years, invasive plants have been detrimental to the conservation of biodiversity and native ecosystems on the Qinghai-Tibetan Plateau. Here, 197 invasive plant species were recognized from the Qinghai-Tibetan Plateau, encompassing 133 genera across 47 families. We used the maximum entropy model (MaxEnt) method to predict the potential distribution of invasive alien plants on the Qinghai-Tibetan Plateau. Furthermore, potential geographic distribution regions suitable for 43 invasive alien plant species were found to be primarily concentrated in central and eastern Qinghai, southern and eastern Tibet, southwest Xinjiang, and the Qinghai-Tibetan Plateau part of Sichuan, Yunnan, and Gansu in China. Notably, the current potential distribution area for all 43 invasive alien plant species accounts for 29.92 % of the total area, with an increase of 32.72–59.39 % projected by 2050. Moreover, risk screening results confirm the high risk of invasion of most alien plants on the Qinghai-Tibetan Plateau. This study provides a foundation for assessing the risks associated with introducing and managing invasive alien plants in the region. It also offers scientific support for conserving the Qinghai-Tibetan Plateau’s unique biodiversity, ecological originality, and sustainable development during economic growth.

Detecting Measurement-Induced Entanglement Transitions with Unitary Mirror Circuits.

Monitored random circuits, consisting of alternating layers of entangling two-qubit gates and projective single-qubit measurements applied to some fraction p of the qubits, have been a topic of recent interest. In particular, the resulting steady state exhibits a phase transition from highly correlated states with "volume-law" entanglement at p<p_{c} to localized states with "area-law" entanglement at p>p_{c}. It is hard to access this transition experimentally, as it cannot be seen at the ensemble level. Naively, to observe it one must repeat the experiment until the set of measurement results repeats itself, with likelihood that is exponentially small in the number of measurements. To overcome this issue, we present a hybrid quantum-classical algorithm which creates a matrix product state (MPS) based "unitary mirror" of the projected circuit. Polynomial-sized tensor networks can represent quantum states with area-law entanglement, and so the unitary mirror can well approximate the experimental state above p_{c} but fails exponentially below it. The breaking of this mirror can thus pinpoint the critical point. We outline the algorithm and how such results would be obtained. We present a bound on the maximum entanglement entropy of any given state that is well represented by an MPS, and from the bound suggest how the volume-law phase can be bounded. We consider whether the entanglement could similarly be bounded from below where the MPS fails. Finally, we present numerical results for small qubit numbers and for monitored circuits with random Clifford gates.

Efficient First-Order Algorithms for Large-Scale, Non-Smooth Maximum Entropy Models with Application to Wildfire Science.

Maximum entropy (MaxEnt) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, MaxEnt models need efficient optimization algorithms to scale well for big data applications. State-of-the-art algorithms for MaxEnt models, however, were not originally designed to handle big data sets; these algorithms either rely on technical devices that may yield unreliable numerical results, scale poorly, or require smoothness assumptions that many practical MaxEnt models lack. In this paper, we present novel optimization algorithms that overcome the shortcomings of state-of-the-art algorithms for training large-scale, non-smooth MaxEnt models. Our proposed first-order algorithms leverage the Kullback-Leibler divergence to train large-scale and non-smooth MaxEnt models efficiently. For MaxEnt models with discrete probability distribution of n elements built from samples, each containing m features, the stepsize parameter estimation and iterations in our algorithms scale on the order of O(mn) operations and can be trivially parallelized. Moreover, the strong ℓ1 convexity of the Kullback-Leibler divergence allows for larger stepsize parameters, thereby speeding up the convergence rate of our algorithms. To illustrate the efficiency of our novel algorithms, we consider the problem of estimating probabilities of fire occurrences as a function of ecological features in the Western US MTBS-Interagency wildfire data set. Our numerical results show that our algorithms outperform the state of the art by one order of magnitude and yield results that agree with physical models of wildfire occurrence and previous statistical analyses of wildfire drivers.

On the Occurrence of Domain Boundaries and Defects in Self-Organized Ordered Porous Anodic Alumina Systems.

The formation of domains and defects is a part of every self-organized structure without prepatterning which evolves from the electrochemical oxidation of valve metals like Ti, Al or Ta. Defects and domains, especially in highly ordered porous alumina films (PAOX), are one of the most studied phenomena in the current literature. The focus is on understanding their formation in order to find parameters to minimize their amount for applications that require the most highly organized structures. In this letter we give a solution to this problem by showing that the occurrence of defects and domains is an unavoidable consequence of the underlying self-organized process due to the simultaneous impact of the minimum (MINEP) and maximum (MAXEP) entropy production principle. As a consequence, experimental procedures to manufacture defect-free PAOX-films from a self-organization process face a fundamental limitation.

An assessment of the mechanically alloyed equiatomic FeCoNiMnSi high entropy amorphous alloy for non-isothermal crystallization kinetics and magnetocaloric refrigeration application

We studied an equiatomic FeCoNiMnSi high entropy amorphous alloy successfully processed via mechanical alloying technique. The structural, magnetic, and magnetocaloric characteristics of the resulting materials were examined using X-ray diffraction, field emission scanning electron microscopy, high-resolution transmission electron microscopy, and a vibrating sample magnetometer. The structural analysis showed a prominent amorphous phase formation as the milling time increases from t = 0–35h of mechanical alloying. The differential scanning calorimetry was employed to investigate the non-isothermal crystallization kinetics of the 35-h milled sample. The results revealed that the milled powder consists of a single exothermic peak with its apparent activation energy (Eg, Ex, Ep) being determined using the Kissinger, Ozawa, and Augis-Bennett equations. The findings exhibit Ex > Ep > Eg, representing that nucleation is more complicated than the growth mechanism during the crystallization process. Meanwhile, under non-isothermal conditions, the Kissinger-Akahira-Sunose, Friedman, and Flynn-Wall-Ozawa models were calculated using the local activation energies that agree with the apparent activation energy. Furthermore, the Johson-Mehl-Avrami-Kolmogorov exponent (n) and Avrami-Ozawa combined [F(T)] models exhibit high-dimensional nucleation and growth with an increasing nucleation rate enabled by lowering the local activation energies as a function of degree of conversion with respect to temperature. In magnetic measurements, a feasible mathematical model was proposed as a novel strategy for predicting the value of saturation magnetization as a milling time function. The model has an exceptional predictive capability, confirmed by fitting other research outcomes. Finally, the proposed system also delivers the best magnetocaloric properties with a maximum magnetic entropy change of 3.70 Jkg−1 K−1 at 150 K curie temperature and a refrigeration capacity of 252.86 J/kg with 1000 Oe applied magnetic field.

A soft actor–critic reinforcement learning approach for over the air active beamforming with reconfigurable intelligent surface

Combining reconfigurable intelligent surface (RIS) and unmanned aerial vehicle (UAV) provides ubiquitous connectivity for 6G air–ground communications, effectively enhancing coverage. However, due to the ”multiplicative fading” effect, passive RIS can only offer weak capacity gain. In addition, the mobility of UAVs may lead to imperfect channel state information (CSI), making it difficult to perform accurate beamforming. To address these issues, this paper adopts active RIS to actively amplify the reflected signals to overcome the high path loss caused by ”multiplicative fading”. To adapt to the randomness of channel changes, this paper employs the soft actor–critic (SAC) algorithm, which is based on the maximum entropy strategy. This approach jointly optimizes the precoding of the base station (BS) and the beamforming of the aerial RIS (ARIS), aiming to maximize the multi-user transmission rate. Simulation results show that when active ARIS is employed, the proposed algorithm achieves similar sum-rate results in imperfect CSI and perfect CSI scenarios and realizes 71% and 74% performance improvement compared to the traditional passive RIS, respectively. Moreover, the sum-rate remains stable within a certain range when the UAV hovers at any position between the BS and the user.

Causality-inspired multi-source domain generalization method for intelligent fault diagnosis under unknown operating conditions

Currently, fault diagnosis methods based on domain generalization have received widespread attention due to their advantages of not requiring target domain data. Therefore, in this paper, A multi-source domain generalization fault diagnosis method is proposed, consisting of feature diversity activation and non-causal feature suppression from a causal perspective. In the first part, a 3D-Dynamic convolution-based residual network is designed to adaptively learn task related features from different source domains, encouraging the model to focus more on causal feature learning. Furthermore, based on the maximum entropy idea, channel attention diversification is proposed to activate more potential causal features. In the second part, a feature suppression method based on domain discriminator guidance is proposed to explicitly discard non-causal features, specifically, the domain discriminator progressively locates and distinguishes between causal and non-causal features at the layer and channel level and creates binary mask matrices to suppress non-causal related features. Experiments are conducted on the PU and SDUST bearing datasets, and the proposed method can productively solve the cross-domain diagnosis problem under unknown operating conditions.

Highly stable rare earth metal-organic frameworks for proton conduction and magnetic refrigeration

Four isomorphic rare earth metal-organic frameworks designated as {[(CH3)2NH2]0.7[RE2(BTDBA)1.5(lac)0.7(H2O)2]·solvent}n (RE = Tb (JXUST-19), Eu (JXUST-29), Gd (JXUST-42), and Dy (JXUST-43), where JXUST denotes Jiangxi University of Science and Technology) have been synthesized derived from a benzothiadiazole tetracarboxylate ligand (4′,4'''-(benzo[c][1,2,5]thiadiazole-4,7-diyl)bis([1,1′-biphenyl]-3,5-dicarboxylic acid), H4BTDBA). They present chain-based three-dimensional structures with one-dimensional channels extending along the b-axis. Notably, the free [(CH3)2NH2]+ cations within the channels serve as proton carriers during proton conduction. All complexes demonstrate significant proton conductivity dependent on relative humidity and temperature. Among them, JXUST-43 displays the peak conductivity achieved 7. 38 × 10–5 S·cm-1. In terms of magnetic research, JXUST-42 exhibits potential as a magnetorefrigerant, reaching a maximum entropy change (-ΔSmmax) of 16.63 J kg-1 K-1 at 3.5 K with ΔH = 7 T. And the presence of field-induced slow magnetic relaxation behavior with antiferromagnetic coupling is observed in JXUST-43.

High-dimensional maximum-entropy phase space tomography using normalizing flows

Particle accelerators generate charged-particle beams with tailored distributions in six-dimensional position-momentum space (phase space). Knowledge of the phase space distribution enables model-based beam optimization and control. In the absence of direct measurements, the distribution must be tomographically reconstructed from its projections. In this paper, we highlight that such problems can be severely underdetermined and that entropy maximization is the most conservative solution strategy. We leverage —invertible generative models—to extend maximum-entropy tomography to six-dimensional phase space and perform numerical experiments to validate the model's performance. Our numerical experiments demonstrate consistency with exact two-dimensional maximum-entropy solutions and the ability to fit complicated six-dimensional distributions to large measurement sets in reasonable time. Published by the American Physical Society 2024

Magnetocaloric properties of Nd-doped Gd5Si4 microparticles and nanopowders

Abstract The preparation of materials with enhanced magnetocaloric properties is crucial for magnetic refrigeration. In this study, Nd-doped Gd5Si4 microparticles and nanomaterials were synthesized using the reduction–diffusion method. The impact of Nd doping with varying compositions on the structure and entropy change properties of the materials was investigated. The Curie temperatures of both the micron- and nano-sized materials ranged from 190 K to 210 K, which were lower than previously reported values. Micron-sized samples doped with 1% Nd exhibited superior magnetocaloric properties, demonstrating a maximum entropy change of 4.98 J⋅kg−1⋅K−1 at 5 T, with an entropy change exceeding 4 J⋅kg−1⋅K−1 over a wide temperature range of approximately 70 K. Conversely, the nanomaterials had broader entropy change peaks but lower values. All samples exhibited a second-order phase transition, as confirmed by the Arrott plots.

Measuring Double Layer Charging Via Droplet Approach Technique

The potential of zero-charge (PZC) is an important property of the electrochemical double layer (EDL) that has been shown to correlate with changes in the activities of several important reactions such as the oxygen reduction reaction (ORR) and the hydrogen evolution reaction (HER)1. This property was historically measured on droplet mercury electrodes (DME) where surface tension was measured vs potential, an experiment difficult to perform with other metal electrodes. Electrochemical impedance spectroscopy, cyclic voltammetry and galvanic discharge have been widely used to elucidate double layer capacitance but make several assumptions about the ideality of the double layer, measurement system, physical model employed, and have suffered from irreproducibility. The potential of maximum entropy (PME), where the solvent molecules are most disordered, has been measured via the laser induced temperature jump (LITJ) technique which works well when there is no specific adsorption2. A multiple approach mode of scanning electrochemical cell microscopy (SECCM) has also been used to measure local PZCs of polycrystalline3 and high-entropy alloy electrodes4. The SECCM approach is similar to an older immersion method that is not often used5. In this work we create a simple droplet type cell with a pneumatic sample stage to enable a simplified and less expensive version of the SECCM approach/immersion experiments and compare the results to those obtained via the LITJ method.

Temperature Jump Methods for Measuring the Pme of RuO2

The potential of maximum entropy (PME) is used to understand the structure and charging of the electrochemical double layer. Closely related to the potential of zero charge (PZC), the PME occurs when disorder of the solvent molecules at the surface reaches a maximum. Using a laser induced temperature jump (LITJ) to measure the PME has been shown to be a convenient method for metal electrodes. We present experiments using LITJ to investigate a metal oxide thin film and film thickness. The PME of these thin films appears to change with thickness, correlating with surface strain where a thinner film shifts the PME closer to OER potentials in turn reducing the overpotentials required. Our experiments are shown to be reproducible and independent of sweep rate or the vertex potentials selected. Interestingly we observe a hysteresis with scan direction, of 100-250 mV which we nominally attribute to surface adsorbates. Our results highlight the link between excess surface charge and catalytic activity and how this may be tuned via surface strain. Figure 1

Investigation of the effect of martensitic phase transition temperature and Curie temperature difference on magnetic and magnetocaloric properties under low magnetic field on Si-doped Ni-Mn-In Heusler alloys

This study examines the impact of substituting Si for Mn on the structural, magnetic, and magnetocaloric properties of Ni43Mn46−x Si x In11 (x = 0.3 and 0.6) alloys. To this end, a range of analytical techniques are employed, including scanning electron microscopy (SEM), room temperature x-ray powder diffraction (XRD), and magnetization measurements. Above the martensitic transition temperature, the Ni43Mn46−x Si x In11 alloys exhibit cubic L2 1 (space group FM-3M). Below this temperature they adopt a tetragonal L1 0 (space group I4/mmm). The martensitic transition temperature decreased when Si is substituted for Mn. The magnetic field-induced entropy change is calculated from magnetic field-dependent magnetization measurements using Maxwell’s equations. The maximum magnetic field-induced entropy changes for Ni43.16Mn45.56Si0.29In11 and Ni43.51Mn44.82Si0.59In11 alloys are calculated 8.20 J kg−1K−1 and 3.15 J kg−1 K−1, respectively, in the vicinity of the magnetostructural phase transition for a magnetic field change of 18 kOe. It is demonstrated that the temperature differential between the high-temperature austenite phase's Curie point (T C ) and the mean martensitic transformation temperature (T M ), namely (T M -T C ), influences the martensitic transition temperatures and, consequently, on the magnetic field-induced entropy change (ΔS M ).