Maximum Entropy Research Articles

3D-Ising-type magnetic interactions stabilized by the extremely large uniaxial magnetocrystalline anisotropy in layered ferromagnetic Cr2Te3

We investigate the magnetocrystalline anisotropy, critical behavior, and magnetocaloric effect in ferromagnetic-layered Cr2Te3. We have studied the critical behavior around the Curie temperature (TC) using various techniques, including the modified Arrott plot (MAP), the Kouvel-Fisher method (KF), and critical isothermal analysis (CI). The derived critical exponents β = 0.353(4) and γ = 1.213(5) fall in between the three-dimensional (3D) Ising and 3D Heisenberg type models, suggesting complex magnetic interactions by not falling into any single universality class. On the other hand, the renormalization group theory, employing the experimentally obtained critical exponents, suggests 3D-Ising-type magnetic interactions decaying with distance as J(r) = r−4.89. We also observe an extremely large uniaxial magnetocrystalline anisotropy energy (MAE) of Ku = 2065 kJ/m3, the highest ever found in any CrxTey based systems, originating from the noncollinear ferromagnetic ground state as predicted from the first-principles calculations. The self-consistent renormalization theory (SCR) suggests Cr2Te3 to be an out-of-plane itinerant ferromagnet. Further, a maximum entropy change of -ΔSMmax≈ 2.08 J/kg − K is estimated around TC for the fields applied parallel to the c-axis.

Predicting the Current and Future Distribution of Monolepta signata (Coleoptera: Chrysomelidae) Based on the Maximum Entropy Model.

Monolepta signata is a polyphagous and highly destructive agricultural pest, currently only distributed in Asia. In its place of origin, it poses a serious threat to important economic crops, for instance, maize (Zea mays L.) and cotton (Gossypium hirsutum L.). Based on morphological and molecular data research, it has been found that M. quadriguttata (Motschulsky), M. hieroglyphica (Motschulsky), and M. signata are actually the same species. This discovery means that the range of this pest will expand, and it also increases the risk of it spreading to non-native areas worldwide. It is crucial for global agricultural production to understand which countries and regions are susceptible to invasion by M. signata and to formulate corresponding prevention, control, and monitoring strategies. This study uses the maximum entropy model, combined with bioclimatic variables and elevation, to predict the potentially suitable areas and diffusion patterns of M. signata worldwide. The results indicate that in its suitable area, M. signata is mainly affected by three key climatic factors: Precipitation of Wettest Month (bio13), Mean Temperature of Warmest Quarter (bio10), and Temperature Seasonality (bio4). Under the current status, the total suitable region of M. signata is 252,276.71 × 104 km2. In addition to its native Asia, this pest has potentially suitable areas in Oceania, South America, North America, and Africa. In the future, with climate change, the suitable area of M. signata will expand to high-latitude areas and inland areas. This study found that by the 2070s, under the SSP5-8.5 climate scenario, the change in the potentially suitable area of this insect is the largest. By identifying the potentially suitable areas and key climatic factors of M. signata, we can provide theoretical and technical support to the government, enabling them to more effectively formulate strategies to deal with the spread, outbreak, and invasion of M. signata.

Effect of Annealing Temperature on Microstructure and Magnetocaloric Properties of Gd-Based Metallic Microfibers

In this paper, vacuum annealing has been adopted to introduce atomic cluster micro-regions inside Gd-based metallic microfibers to further explore the effect of the structural changes on the magnetocaloric properties and the mechanism which is systematically expressed. The experimental results indicate that the as-prepared Gd-based metallic microfibers have favorable amorphous formation ability and thermal stability. After annealing @ 380 °C, the maximum magnetic entropy change −ΔSmmax, refrigerating capacity (RC), and relative cooling power (RCP) values of the Gd-based metallic microfibers are 7.20 J/kg·K, 459.4 J/kg, and 588.7 J/kg, respectively. Combined with the transmission electron microscopy analysis results, the internal organizational order of the annealed microfibers is significantly altered, and the atomic clusters formed in localized regions, which reduce the magnetocrystalline anisotropy of the microfibers. While under the uni-action of an external magnetic field, the magnetic moment rotation state and magnetic domain structure distribution of the micro-region atoms will be changed obviously, thereby changing the general magnetic properties and magnetocaloric properties of the metallic microfibers. The above research results can promote the engineering application of Gd-based metallic microfibers in the field of magnetic refrigeration.

The most likely common cause

The common cause principle for two random variables A and B is examined in the case of causal insufficiency, when their common cause C is known to exist, but only the joint probability of A and B is observed. As a result, C cannot be uniquely identified (the latent confounder problem). We show that the generalized maximum likelihood method can be applied to this situation and allows identification of C that is consistent with the common cause principle. It closely relates to the maximum entropy principle. Investigation of the two binary symmetric variables reveals a non-analytic behavior of conditional probabilities reminiscent of a second-order phase transition. This occurs during the transition from correlation to anti-correlation in the observed probability distribution. The relation between the generalized likelihood approach and alternative methods, such as predictive likelihood and minimum common entropy, is discussed. The consideration of the common cause for three observed variables (and one hidden cause) uncovers causal structures that defy representation through directed acyclic graphs with the Markov condition.

The importance of geography in forecasting future fire patterns under climate change

An increasing amount of California's landscape has burned in wildfires in recent decades, in conjunction with increasing temperatures and vapor pressure deficit due to climate change. As the wildland-urban interface expands, more people are exposed to and harmed by these extensive wildfires, which are also eroding the resilience of terrestrial ecosystems. With future wildfire activity expected to increase, there is an urgent demand for solutions that sustain healthy ecosystems and wildfire-resilient human communities. Those who manage disaster response, landscapes, and biodiversity rely on mapped projections of how fire activity may respond to climate change and other human factors. California wildfire is complex, however, and climate-fire relationships vary across the state. Given known geographical variability in drivers of fire activity, we asked whether the geographical extent of fire models used to create these projections may alter the interpretation of predictions. We compared models of fire occurrence spanning the entire state of California to models developed for individual ecoregions and then projected end-of-century future fire patterns under climate change scenarios. We trained a Maximum Entropy model with fire records and hydroclimatological variables from recent decades (1981 to 2010) as well as topographic and human infrastructure predictors. Results showed substantial variation in predictors of fire probability and mapped future projections of fire depending upon geographical extents of model boundaries. Only the ecoregion models, accounting for the unique patterns of vegetation, climate, and human infrastructure, projected an increase in fire in most forested regions of the state, congruent with predictions from other studies.

An Efficient Maximum Entropy Approach with Consensus Constraints for Robust Geometric Fitting

An Efficient Maximum Entropy Approach with Consensus Constraints for Robust Geometric Fitting

Congestion Transition on Random Walks on Graphs.

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.

A point mapping strategy-based sparse grid integration method for statistical moments estimation and structural reliability analysis

The high-order moment-based method is an attractive non-intrusive approach for structural reliability analysis, while it remains a challenge to strike a balance between accuracy, efficiency, and versatility in estimating statistical moments of performance functions. In this paper, a point mapping strategy-based sparse grid integration (SGI) method is proposed for statistical moment estimation and structural reliability analysis, with improved efficiency while ensuring the accuracy. Firstly, a point mapping strategy (PMS) is developed based on the adaptive high dimensional model representation (HDMR), by which the traditional SGI is reconstructed as a sum of multiple integrations over the sparse grid point sets with different effective dimensions. With this new form of SGI, the estimation of statistical moments only requires the evaluation of the performance function at a small number of necessary reference points, once the effects of some interaction terms are negligible. The validity and feasibility of the proposed PMS-based SGI method for statistical moments estimation are verified by several numerical examples. Then, the proposed method is applied to structural reliability analysis with the aid of an improved maximum entropy method. Two engineering examples are investigated, including the strength failure problem of tubular tower K-shape connection joints and the fatigue failure problem of a marine riser resulting from the vortex-induced vibration (VIV). The results indicate that the proposed method provides a better trade-off between accuracy and efficiency in evaluating the first four order statistical moments and structural failure probability compared to conventional methods.

Structure and chemical bonding in high-pressure potassium silver alloys

The high-pressure structures of K-Ag alloys were examples of pressure-induced electron transfer from the electropositive potassium to the electronegative silver. We re-examined the crystal and electronic structures of KAg2, K2Ag, and K3Ag using powder X-ray diffraction and theoretical calculations. Our findings establish a connection between the morphologies of these three phases and the precursor face-centered cubic Ag. For K2Ag, we discovered a disordered structure that better matches the X-ray pattern. Valence electron density distributions obtained from the maximum entropy method, along with charge density calculations, provide a comprehensive understanding of the evolution of chemical bonding in these systems. It was found that K atoms share their valence electrons during alloy formation, contributing to K-Ag and Ag-Ag bonds in K2Ag and KAg2, while no Ag-Ag bonds are present in K3Ag. These results indicate the Zintl-Klemm model may be too simplistic to describe the structure and bonding in high-pressure binary intermetallic compounds.

Research on Estimating Potato Fraction Vegetation Coverage (FVC) Based on the Vegetation Index Intersection Method

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.

Evaluation of the environmental factors influencing the quality of Astragalus membranaceus var. mongholicus based on HPLC and the Maxent model

BackgroundIn recent years, global climate change in tandem with increased human activity has resulted in habitat degradation or the migration of rare medicinal plants, potentially impacting the quality of medicinal herbs. Astragalus membranaceus var. mongholicus is a valuable bulk medicinal material in Northwest China. As the demand for this medicinal herb continues to increase in both domestic and international markets, ensuring the sustainable development of high-quality Astragali Radix is important. In this study, the maximum entropy (Maxent) model was applied, thereby incorporating 136 distribution records, along with 39 environmental factors of A. membranaceus var. mongholicus, to assess the quality zonation and potential distribution of this species in China under climate change.ResultsThe results showed that the elevation, annual mean temperature, precipitation of wettest month, solar radiation in June, and mean temperature of warmest quarter were the critical environmental factors influencing the accumulation of astragaloside IV and Astragalus polysaccharide in A. membranaceus var. mongholicus. Among the twelve main environmental variables, annual mean temperature, elevation, precipitation of the wettest month, and solar radiation in November were the four most important factors influencing the distribution of A. membranaceus var. mongholicus. In addition, ecological niche modelling revealed that highly suitable habitats were mainly located in central and western Gansu, eastern Qinghai, northern Shaanxi, southern Ningxia, central Inner Mongolia, central Shanxi, and northern Hebei. However, the future projections under climate change suggested a contraction of these suitable areas, shifting towards northeastern high-latitude and high-elevation mountains.ConclusionsThe findings provide essential insights for developing adaptive strategies for A. membranaceus var. mongholicus cultivation in response to climate change and can inform future research on this species. By considering the identified environmental factors and the potential impacts of the predicted climate changes, we can visualize the regional distribution of high-quality Radix Astragali and develop conservation strategies to protect and restore its suitable habitats.

Regression‐based network‐flow and inner‐matrix reconstruction

AbstractNetwork or matrix reconstruction is a general problem that occurs if the row‐ and column sums of a matrix are given, and the matrix entries need to be predicted conditional on the aggregated information. In this paper, we show that the predictions obtained from the iterative proportional fitting procedure (IPFP) or equivalently maximum entropy (ME) can be obtained by restricted maximum likelihood estimation relying on augmented Lagrangian optimization. Based on this equivalence, we extend the framework of network reconstruction, conditional on row and column sums, toward regression, which allows the inclusion of exogenous covariates and bootstrap‐based uncertainty quantification. More specifically, the mean of the regression model leads to the observed row and column margins. To exemplify the approach, we provide a simulation study and investigate interbank lending data, provided by the Bank for International Settlement. This dataset provides full knowledge of the real network and is, therefore, suitable to evaluate the predictions of our approach. It is shown that the inclusion of exogenous information leads to superior predictions in terms of and errors.

Magnetocaloric effect and phase transformation of Zn-doped Ni–Mn–Ti all-d metal Heusler alloys

Ni–Mn–Ti Heusler alloys have gained a great consideration recently due to their important magnetoresistance and magnetocaloric properties. Herein, the structure, martensitic transition (MT), and magnetocaloric effect (MCE) of Ni50-xZnxMn35Ti15 (x = 0, 2, 4, and 6) Heusler alloys have been studied. XRD results show that the studied alloy with x = 0 display a monoclinic (5 M) martensite phase. Besides, the examined alloy with x = 2 presents a combination of monoclinic (5 M) martensite and cubic (B2) austenite phases, whereas the alloys with x = 4 and x = 6 exhibit only a cubic (B2) austenite phase. By increasing Zn content x up to 6, the Curie temperature of austenite decreases from 283 K to 132 K, and the MT temperatures decrease from ferromagnetic austenite to weak-magnetic martensite, which is apparently due to the exchange interaction of Mn–Mn and the low power of d-d hybridization between the A/C and B(D) sites. The best maximum magnetic entropy change (ΔSM, max) and refrigeration capacity (RC) are for the studied alloy with x = 4 (5.22 J kg−1K−1 and 217 J kg−1 at 120 kOe, respectively). The Zn addition not only facilitates the formation of B2-type Heusler phases but also offer the possibility to tune the MT and establish strong ferromagnetic coupling.

Associated Probabilities’ Aggregations in Fuzzy Collaborative Filtering Recommender System Prediction

AbstractIn this article, the expert evaluations transformed in the multi-attribute decision-making (MADM) model are presented in the discrimination q-rung picture linguistic fuzzy numbers (q-RPLFNs). In the construction of a second-order additive fuzzy measure (TOAFM) the attributes’ interaction indexes and Shapley values are taken into account. The Shapley entropy maximum principle for identification of associated probabilities class (APC) of a TOAFM is constructed. Based on the APC of the TOAFM, a new aggregation operators’ class is constructed which represents some hybrid extensions of ordered weighted averaging (OWA), geometric (OWG), the Choquet integral averaging (CA) and geometric (CG) operators under discrimination q-rung orthopair fuzzy (q-ROF) and q-rung picture linguistic fuzzy (q-RPLF) information. These operators, constructed for the q-RPLF and q-ROF environments, take into account the overall pair interactions among attributes. Main properties on the correctness of extensions are proved: for the lower and upper capacities of order 2, all constructed operators consequently coincide with q-ROF and q-RPLF Choquet averaging and geometric operators, respectively. Constructed operators in the evaluation of prediction of fuzzy Collaborative Filtering Recommender Systems (CFRS) are used. New symmetric discrimination measures as some extensions of discrimination measures for the fuzzy CFRS are proposed. Users’ profile data by the constructed operators in the new similarity measure under q-rung picture linguistic environment are aggregated. The developed new approach is schematically described in such a way that it can be “embedded” in any existing CFRS model. An example is given to illustrate the results, for which the software designed to aggregate profile data for similarity comparison provides the use of new and well-known classical aggregation operators.

Modeling Proboscis monkey conservation sites on Borneo using ensemble machine learning

This study aimed to analyze the habitat suitability of the endangered Proboscis monkey (Nasalis larvatus) on Borneo using a multi-machine-learning approach. This study integrated physical, vegetational, meteorological, and human activity data to develop a comprehensive habitat suitability model. Four machine-learning algorithms, namely, maximum entropy (MaxEnt), random forest (RF), support vector machine (SVM), gradient tree boosting (GTB), and classification and regression trees (CART), were employed to model the habitat suitability index. A total of 1943 sample points were divided into training (70 %) and validation (30 %) sets for the analysis. This study included three main stages: geospatial database creation, spatial habitat modeling using multi-machine-learning algorithms, and habitat suitability evaluation. In addition, the pressure from human development on the habitat suitability index model was analyzed. This study identified a high level of suitability for Proboscis monkey habitats in nearshore areas. The maximum habitat suitability for Proboscis monkeys was observed to be 11.54 %, as evidenced by the consensus of the MaxEnt value and four machine-learning algorithms. Conversely, the minimum habitat suitability was recorded at 13.27 %, as indicated by disagreement among all algorithms. The AUC values for the machine-learning models ranged from 74 % to 90 %, indicating moderate to high predictive performance. This study provides valuable insights for the formulation of well-planned development programs for Proboscis monkeys. The results of this study will contribute to the accurate identification of potential Proboscis monkey habitats, thereby providing support for conservation efforts aimed at safeguarding this endangered species.

Climate Change Threatens Barringtonia racemosa: Conservation Insights from a MaxEnt Model

Barringtonia racemosa (L.) Spreng. (Lecythidaceae), a crucial species in mangrove ecosystems, is facing endangerment primarily due to habitat loss. To address this issue, research is imperative to identify suitable conservation habitats for the endangered B. racemosa within mangrove ecosystems. The utilization of the optimized Maximum Entropy (MaxEnt) model has been instrumental in predicting potential suitable regions based on global distribution points and environmental variables under current and future climates conditions. The study revealed that the potential distribution area of B. racemosa closely aligns with its existing range with an Area Under the Curve (AUC) greater than 0.95. The Jackknife, AUC, percent contribution (PC), and permutation importance (PI) tests were employed alongside the optimized MaxEnt model to examine the influence of environmental variables on the distribution of B. racemosa. The primary factors identified as significant predictors of B. racemosa distribution included the average temperature of the ocean surface (Temperature), average salinity of the ocean surface (Salinity), precipitation of the warmest quarter (Bio18), precipitation of the driest month (Bio14), seasonal variation coefficient of temperature (Bio4), and isothermality (Bio3). Currently, the habitat range of B. racemosa is predominantly found in tropical and subtropical coastal regions near the equator. The total suitable habitat area measures 246.03 km2, with high, medium, low, and unsuitable areas covering 3.90 km2, 8.57 km2, 16.94 km2, and 216.63 km2, respectively. These areas represent 1.58%, 3.48%, 6.88%, and 88.05% of the total habitat area, respectively. The potential distribution area of B. racemosa demonstrated significant variations under three climate scenarios (SSP126, SSP245, and SSP585), particularly in Asia, Africa, and Oceania. Both low and high suitable areas experienced a slight increase in distribution. In summary, the research suggests that B. racemosa primarily flourishes in coastal regions of tropical and subtropical areas near the equator, with temperature and precipitation playing a significant role in determining its natural range. This study offers important implications for the preservation and control of B. racemosa amidst habitat degradation and climate change threats. Through a comprehensive understanding of the specific habitat needs of B. racemosa and the implementation of focused conservation measures, efforts can be made to stabilize and rejuvenate its populations in their natural environment.

The Effect of Hydrogen as a Coolant on the Characteristics of Humidification-Dehumidification Desalination Systems

The air humidification-dehumidification (HDH) technique for water desalination can be useful in many water production applications. Researchers from all around the world have examined various implementations of this technology to improve it. The present research investigates the effect of three dehumidifier coolants on the system. These coolants include water, helium, and hydrogen. The impact of these coolants on the parameters of the humidification-dehumidification desalination system will be discussed. The investigation’s parameters are tested at various mass ratios, air flow rates, and air outlet heaters. The results show that when hydrogen is employed as a dehumidifier coolant, the gained output ratio (GOR) achieves its peak of 6.37 in the considered mass ratio range of 2.1 to 3. On the other hand, when hydrogen is utilized as a dehumidifier coolant, the system produces the maximum entropy, with the dehumidifier contributing the most. When the mass ratio changes from 2 to 3, the average entropy generation for the system using hydrogen in the dehumidifier increases by 3.8 and 2.9 times, respectively, compared to the average entropy generation for the system using water and helium. However, when hydrogen is used as a dehumidifier coolant, safety concerns must be addressed, as well as the size and cost of heat exchangers in comparison to water.

A transcranial multiple waves suppression method for plane wave imaging based on Radon transform

Transcranial ultrasound imaging presents a significant challenge due to the intricate interplay between ultrasound waves and the heterogeneous human skull. The skull’s presence induces distortion, refraction, multiple scattering, and reflection of ultrasound signals, thereby complicating the acquisition of high-quality images. Extracting reflections from the entire waveform is crucial yet exceedingly challenging, as intracranial reflections are often obscured by strong amplitude direct waves and multiple scattering. In this paper, a multiple wave suppression method for ultrasound plane wave imaging is proposed to mitigate the impact of skull interference. Drawing upon prior research, we developed an enhanced high-resolution linear Radon transform using the maximum entropy principle and Bayesian method, facilitating wavefield separation. We detailed the process of wave field separation in the Radon domain through simulation of a model with a high velocity layer. When plane waves emitted at any steering angles, both multiple waves and first arrival waves manifested as distinct energy points. In the brain simulation, we contrasted the characteristic differences between skull reflection and brain-internal signal in Radon domain, and demonstrated that multiples suppression method reduces side and grating lobe levels by approximately 30 dB. Finally, we executed in vitro experiments using a monkey skull to separate weak intracranial reflection signals from strong skull reflections, enhancing the contrast-to-noise ratio by 85 % compared to conventional method using full waveform. This study deeply explores the effect of multiples on effective signal separation, addresses the complexity of wavefield separation, and verifies its efficacy through imaging, thereby significantly advancing ultrasound transcranial imaging techniques.

Enhanced Cryogenic Magnetocaloric Effect from 4f‐3d Exchange Interaction in B‐Site Ordered Gd2CuTiO6 Double Perovskite Oxide

AbstractMagnetic refrigeration based on the principle of the magnetocaloric effect (MCE) in magnetic solids has been considered as a prospective cooling technology. Exploring suitable magnetocaloric materials (MCMs) is a vital prerequisite for practical applications. Herein, an excellent cryogenic MCM—the B‐site‐ordered Gd2CuTiO6 double perovskite (DP) oxide—which exhibits the largest MCE among known Gd‐based DP oxides, is identified. Such enhanced cryogenic MCE in the Gd2CuTiO6 DP oxide likely stems from the exchange interaction effect between Gd‐4f and Cu‐3d magnetic sublattices. Under a magnetic field change of 0–7 T, the maximum magnetic entropy change (−ΔSTmax) of the Gd2CuTiO6 DP oxide reaches 51.4 J kg−1 K−1 (378.2 mJ cm−3 K−1), which is much larger than that of the commercialized magnetic refrigerant Gd3Ga5O12, which is 38.3 J kg−1 K−1 (271.2 mJ cm−3 K−1), and it is also superior to most of the recently reported benchmarked cryogenic MCMs, indicating the possibility for practical applications. This work also provides a productive route for future cryogenic MCM design by harnessing 4f–3d exchange interactions.

Ecological niche modeling of the Leopardus tigrinus complex sheds light on its elusive evolutionary history

Abstract The evolutionary history and taxonomy of the Leopardus tigrinus species complex have been studied based on several approaches, mostly employing genetic and morphological data, leading to distinct classification schemes. We approached this problem from an ecological perspective, with 2 main goals: (i) to evaluate ecological niche differences among regional L. tigrinus populations to determine the extent of ecological divergence among them; and (ii) to identify environmental barriers to historical dispersal that could have driven differentiation among the proposed groups. We modeled the ecological niche of all taxonomic/geographic groups proposed so far to comprise the L. tigrinus complex using the Maximum Entropy algorithm, and evaluated geographic and ecological niche differences among them. Furthermore, we investigated possible environmental barriers to historical dispersal that could have driven differentiation among regional groups. We evaluated 4 hypothetical barriers across 3 time periods to assess their potential historical effect. We found high ecological divergence between northeastern tigrina populations and the northern Andean and Central American tigrinas. Other groups within the L. tigrinus complex are less divergent. In addition, the Guiana Shield tigrina, where the type locality of the species is located, seems to be ecologically similar to populations from northeastern Brazil while also showing some overlap with Andean populations. The Panama center, the Llanos of Colombia and Venezuela, and the Amazon region were identified as historical barriers for tigrina dispersal across all time periods. The inferred historical barriers and ecological divergence observed in this study contribute to the inference of evolutionary differentiation among geographic groups comprising the L. tigrinus complex, revealing areas of consistently low habitat suitability that have likely contributed to divergence among regional populations.