Maximum Entropy Research Articles

Large Margin Weighted k-Nearest Neighbors Label Distribution Learning for Classification.

Label distribution learning (LDL) helps solve label ambiguity and has found wide applications. However, it may suffer from the challenge of objective inconsistency when adopted to classification problems because the learning objective of LDL is inconsistent with that of classification. Some LDL algorithms have been proposed to solve this issue, but they presume that label distribution can be represented by the maximum entropy model, which may not hold in many real-world problems. In this article, we design two novel LDL methods based on the k -nearest neighbors ( k NNs) approach without assuming any form of label distribution. First, we propose the large margin weighted k NN LDL (LW- k NNLDL). It learns a weight vector for the k NN algorithm to learn label distribution and implement a large margin to address the objective inconsistency. Second, we put forward the large margin distance-weighted k NN LDL (LD k NN-LDL) that learns distance-dependent weight vectors to consider the difference in the neighborhoods of different instances. Theoretical results show that our methods can learn any general-form label distribution. Moreover, extensive experimental studies validate that our methods significantly outperform state-of-the-art LDL approaches.

INS/UWB Fusion Localization Algorithm in Indoor Environment Based on Variational Bayesian and Error Compensation

Localization technology is crucial for indoor robot navigation. However, because of the intricacies inherent in the indoor setting, the signal transmission is vulnerable to the interference of obstacles, which leads to the decline of positioning accuracy. Ultra-Wideband (UWB) has the characteristics of channel insensitivity and high localization accuracy. Inertial navigation system (INS) functions independently as a navigation system, and its positioning results will not be affected due to non-line-of-sight (NLOS) interference. When using UWB t locate the mobile node, the Variational Bayesian Gaussian Mixture Model (VBGMM) clustering algorithm based on Gaussian Mixture Model (GMM) is applied to lessen the influence of NLOS propagation. This paper proposed a loose coupling of the INS and UWB, which combines the advantages of the two subsystems and improves the performance of the positioning system. On the basis of INS autonomous positioning, the maximum entropy fuzzy generalized probability data association filter (MEF-GPDAF) is used to modify the INS positioning results, and then the virtual inertia points are further built to compensate the error of the corrected coordinates. Finally, Unscented Kalman Filter is applied to the compensated coordinates for enhanced positioning. Simulation indicates that the proposed approach in this paper exhibits superior location accuracy. The real experimental results show that the proposed algorithm achieves an average improvement of 61.12% in positioning accuracy.

Eliciting patient preferences and predicting behaviour using Inverse Reinforcement Learning for telehealth use in outpatient clinics

IntroductionNon-attendance (NA) causes additional burden on the outpatient services due to clinician time and other resources being wasted, and it lengthens wait lists for patients. Telehealth, the delivery of health services remotely using digital technologies, is one promising approach to accommodate patient needs while offering more flexibility in outpatient services. However, there is limited evidence about whether offering telehealth consults as an option can change NA rates, or about the preferences of hospital outpatients for telehealth compared to in-person consults. We model patient preferences with a Maximum Entropy Inverse Reinforcement Learning (IRL) behaviour model, allowing for the calculation of general population- and demographic-specific relative preferences for consult modality. The aim of this research is to use real-world data to model patient preferences for consult modality using Maximum Entropy IRL behaviour model.MethodsRetrospective data were collected from an immunology outpatient clinic associated with a large metropolitan hospital in Brisbane, Australia. We used IRL with the Maximum Entropy behaviour model to learn outpatient preferences for appointment modality (telehealth or in-person) and to derive demographic predictors of attendance or NA. IRL models patients as decision making agents interacting sequentially over multiple time-steps, allowing for present actions to impact future outcomes, unlike previous models applied in this domain.ResultsWe found statistically significant (α = 0.05) within-group preferences for telehealth consult modality in privately paying patients, patients who both identify as First Nations individuals and those who do not, patients aged 50–60, who did not require an interpreter, for the general population, and for the female population. We also found significant within-group preferences for in-person consult modality for patients who require an interpreter and for patients younger than 30.DiscussionUsing the Maximum Entropy IRL sequential behaviour model, our results agree with previous evidence that non-attendance can be reduced when telehealth is offered in outpatient clinics. Our results complement previous studies using non-sequential modelling methodologies. Our preference and NA prediction results may be useful to outpatient clinic administrators to tailor services to specific patient groups, such as scheduling text message consult reminders if a given patient is predicted to be more likely to NA.

Enhancing insights in sexually transmitted infection mapping: Syphilis in Forsyth County, North Carolina, a case study.

In 2008-2011 Forsyth County, North Carolina experienced a four-fold increase in syphilis rising to over 35 cases per 100,000 mirroring the 2021 state syphilis rate. Our methodology extends current models with: 1) donut geomasking to enhance resolution while protecting patient privacy; 2) a moving window uniform grid to control the modifiable area unit problem, edge effect and remove kriging islands; and 3) mitigating the "small number problem" with Uniform Model Bayesian Maximum Entropy. Data is 2008-2011 early syphilis cases reported to the NC Department of Health and Human Services for Forsyth County. Results were assessed using latent rate theory cross validation. We show combining a moving window and a UMBME analysis with geomasked data effectively predicted the true or latent syphilis rate 5% to 26% more accurate than the traditional, geopolitical boundary method. It removed kriging islands, reduced background incidence rate to 0, relocated nine outbreak hotspots to more realistic locations, and elucidated hotspot connectivity producing more realistic geographical patterns for targeted insights. Using the Forsyth outbreak as a case study showed how the outbreak emerged from endemic areas spreading through sexual core transmitters and contextualizing the outbreak to current and past outbreaks. As the dynamics of sexually transmitted infections spread have changed to online partnership selection and demographically to include more women, partnership selection continues to remain highly localized. Furthermore, it is important to present methods to increase interpretability and accuracy of visual representations of data.

Human-like Decision Making for autonomous lane change driving: a hybrid inverse reinforcement learning with game-theoretical vehicle interaction model

The development of automated driving vehicles aims to provide safer, comfortable, and more efficient mobility options. However, the decision-making control of autonomous vehicles still embraces limitations on human performance mimicry. These limitations become particularly evident in complex and unfamiliar driving scenarios, where weak decisionmaking abilities and poor adaptation of vehicle behaviour are prominent issues. This paper proposes a game-theoretic decisionmaking algorithm for human-like driving in the vehicle lane change scenario. Firstly, an inverse reinforcement learning (IRL) model is used to quantitatively analyse the lane change trajectories of the natural driving dataset, establishing the human-like human cost function. Subsequently, joint safety, comfort to build the comprehensive decision cost function. Use the combined decision cost function to conduct a non-cooperative game of vehicle lane changing decision to solve the optimal decision of host vehicle lane changing. The host vehicle lane-changing decision problem is formulated as a Stackelberg game optimization problem. To verify the feasibility and effectiveness of the algorithm proposed in this study, a lane change test scenario has been established. Firstly, we analyse the human-like decision-making model derived by the maximum entropy inverse reinforcement learning algorithm to verify the effectiveness and robustness of the IRL algorithm. Secondly, the human-like game decisionmaking algorithm in this paper is validated by conducting an interactive lane-changing experiment with obstacle vehicles of different driving styles. The experimental results prove that the human-like driving decision-making model proposed in this study can make lane-changing behaviours in line with human driving patterns in lane-changing scenarios of expressway.

Structure, Optical Properties and Magnetocaloric Effects in Gd3Fe5O12 Garnet Synthesized by Solid State Method

In this work, gadolinium iron garnet (Gd3Fe5O12) was synthesized using the solid-state ceramic method. The structural analysis, confirmed through Rietveld refinement, indicated the formation of a single-phase garnet structure. Scanning electron microscopy studies revealed uniform grain size and homogeneity in the sintered material, while UV-vis absorption studies showed a peak at 300 nm, indicating strong ultraviolet interaction and an optical band gap of 3.64 eV, suggesting optoelectronic potential. The fluorescence emission spectrum shows a band at 435 nm, while the excitation spectrum exhibits bands at 275 and 360 nm. The magnetic measurements demonstrated Gd ion ordering at low temperatures, with a compensation temperature around 290 K. Arrott plots confirmed the second-order magnetic phase transition. Importantly, Gd3Fe5O12 exhibited both normal and inverse magnetocaloric effects, with a maximum magnetic entropy change of 1.05 J kg–1 K–1 at 230 K under 9 T magnetic field. These properties indicate Gd3Fe5O12 material as a promising candidate for magnetic refrigeration and optoelectronic applications.

Tensor product random matrix theory

The evolution of complex correlated quantum systems such as random circuit networks is governed by the dynamical buildup of both entanglement and entropy. We here introduce a real-time field theory approach—essentially a fusion of the GΣ functional of the SYK model and the field theory of disordered systems—engineered to microscopically describe the full range of such crossover dynamics: from initial product states to a maximum entropy ergodic state. To showcase this approach in the simplest nontrivial setting, we consider a tensor product of coupled random matrices, and compare to exact diagonalization. Published by the American Physical Society 2024

Presence-only fisheries bycatch data produce biased species distribution predictions for Alcyonacean corals on British Columbia’s continental shelf and slope

Sedentary benthic species such as Alcyonacea corals form critical habitat for fishes and invertebrates. Assessing anthropogenic risks to these organisms requires unbiased, species distribution models (SDMs) that attempt to map probabilities of coral presence in relation to bio-physical ocean characteristics; however, in deep-water settings, the accuracy of SDMs is highly variable and dependent on spatial and taxonomic resolution. Here we investigated how data and model types affect SDM predictions of Alcyonacea probability of presence. We compared predictions from generalized additive models (GAMs) fitted to presence-absence observations over a stratified-random survey design with predictions from maximum entropy models (Maxent) fitted to presence-only bycatch records from commercial fisheries. We use a simulation analysis to show that both model types (i.e., GAM or Maxent) using presence-only bycatch data produced biased estimates of species distribution. Additionally, the maxent model fit using presence-only bycatch data produced biased estimates of performance metrics (AUC, TSS) that were overly optimistic. This study demonstrates a need for presence-absence data collected using a robust survey sampling design to fit SDMs that will better inform marine protected area placement while minimizing unnecessary economic losses, such as forgone fishing yield, due to sub-optimal placement.

Habitat suitability and relative abundance of wild boars in the east‐central Tianshan Mountains, China

AbstractAs wild boar populations and their distribution ranges increase, human–wild boar conflicts have become increasingly prevalent in numerous regions across the globe. These conflicts have a profound impact on human livelihoods, resulting in significant economic losses. Understanding the habitat requirements and relative abundance of wild boars is crucial prior to implementing any conservation measures. However, studies on wild boar habitat and population in the central and eastern regions of the Tianshan Mountains in China are lacking. We assessed the activity patterns and relative abundance of wild boars in these areas and evaluated habitat suitability using a combination of camera trapping, line transects, species distribution modeling (maximum entropy model), and hierarchical abundance modeling (Bayesian N‐mixture model). We used 311 infrared cameras and 280 field‐based line transects to cover approximately 31,000 km² from September 2022 to May 2023 in the east‐central Tianshan Mountains. We used 240 wild boar distribution locations and 13 environmental predictors in the development of species distribution models. We also used species counts and associated environmental predictors in the N‐mixture model to estimate the relative abundance of wild boar. Wild boars were most active during crepuscular hours (1800), and relatively active in the diurnal period compared to the nocturnal period. The probability of wild boar occurrence increased with higher normalized difference vegetation index (NDVI), the minimum temperature of the coldest month, and annual temperatures below 39°C. Boars were most likely to be found in closed deciduous‐coniferous forests. The relative abundance of wild boars was positively affected by NDVI and negatively affected by the minimum temperature of the coldest month and temperature annual range. Based on our results, we suggest areas of management priority. In particular, extensive and intact habitat with substantial wild boar populations, such as the Banfanggou, the South Mountain of Urumqi, and the Hutubi, should be prioritized for long‐term wild boar population monitoring and management so the adverse impacts of increasing wild boar populations in the study region can be minimized.

Distribution of the Culex pipiens mosquito in mainland Portugal: a geospatial modelling study

Abstract Background The mosquito Culex pipiens is a vector for several pathogens, such as the West Nile virus. As the most abundant mosquito in Portugal, understanding its spatial distribution can support vector-borne diseases control and robust public health planning, given the mainland’s favourable climate for this vector. Information on its spatial distribution at a national level is limited, especially its association with climate variables. Thus we aimed to predict the suitability of Culex pipiens distribution in mainland Portugal, considering climate factors. Methods We used presence records for adult and larvae of Culex pipiens mosquitoes sampled across mainland Portugal from January 2017 to October 2023, as part of the Portuguese Vector Surveillance Network program (REVIVE). Between May-October, in random locations and throughout the year in point-of-entry sites, adult mosquito collection used CDC light traps and BG-Sentinel traps, and larvae collection used dippers in breeding sites. Sampling bias was corrected by filtering presence sites, retaining one record per each cell grid of 1 km2. Climatic data (including temperature, precipitation and elevation) were used as predictors in a maximum entropy (MaxEnt) model of the Culex pipiens geographical distribution. Results From the dataset of 6859 records, 1416 unique sites were obtained after filtering. Suitable habitats seem to be mainly distributed on the coast, particularly in Minho, Porto and Lisbon metropolitan areas, and Tagus Valley, with precipitation as the most important predictor. Conclusions Most West Nile Virus case reports come from the south of Portugal, but the potential distribution of Culex pipiens covers the whole mainland territory, with seemingly higher distribution on the north coast. However, bias due to convenience sampling could be present. West Nile vector surveillance should be a priority across all regions to accurately assess transmission risk and implement effective control measures. Key messages • The suitability of Culex pipiens distribution covers the entire mainland of Portugal, with higher presence in the north. • The distribution of Culex pipiens in the country points towards a need for developing region-specific strategies regarding West Nile Virus prevention and control.

Optimizing Room‐Temperature Thermoelectric and Magnetocaloric Performance via Constructing Multi‐Scale Interfacial Phases in LaFeSi/BiSbTe Thermo‐Electro‐Magnetic Refrigeration Materials

AbstractFabricating a thermo‐electro‐magnetic material that exhibits simultaneously excellent magnetocaloric (MC) and thermoelectric (TE) performance is challenging since the interfacial reaction causes severe deterioration of MC and TE performance. In this work, a construction of multi‐scale interfaces in LaFe10.4Co0.8Si1.8/Bi0.5Sb1.5Te3 (LFS/BST) composites is realized by adopting a low‐temperature high‐pressure sintering strategy. It is revealed in the atomic‐scale that the interfacial reaction between LFS and BST leads to the formation of (Fe,Co)(Sb,Te)2 micro‐grains and LaTe2 nano‐grains, and the latter form low‐mismatch phase boundaries with LFS matrix. Benefiting from the multi‐scale interfacial phases, excellent MC performance of LFS is preserved alongside a minor impact on TE properties, e.g., a peak zT of 1.04 and a small decrease of 3.0% in relative cooling power are achieved in the 2%LFS/BST composite. Compared with other thermo‐electro‐magnetic materials, a good trade‐off between MC and TE performance is realized in LFS/BST composites with simultaneously high MC and TE performance. The 20%LFS/BST composite exhibits a room‐temperature zT of 0.46 with large maximum magnetic entropy change and relative cooling power of 0.81 J kg−1 K−1 and 44.83 J kg−1, respectively. This work provides an effective material design for developing the all‐solid‐state MC/TE hybrid refrigeration technique.

Assessing Climate Change Impact on Habitat Suitability and Ecological Connectivity of Wych Elm (Ulmus glabra Huds.) in Türkiye

Understanding how climate change influences the geographical distribution of species within an ecological niche is essential for predicting habitat shifts and informing conservation efforts. This study evaluates the impact of climate change on habitat suitability and ecological connectivity of wych elm (Ulmus glabra Huds.) in Türkiye. The study explores the future distribution of U. glabra and how its connectivity is affected by habitat fragmentation arising from changing climatic conditions. Contextually, this paper aims to achieve two primary objectives: estimating the potential geographical ranges of U. glabra under different climate scenarios and assessing alterations in ecological connections between current and future habitats. The maximum entropy (MaxEnt) model was used along with Morphological Spatial Pattern Analysis (MSPA), and the Probability of Connectivity (PC) index was applied to show possible transformations in distribution patterns of U. glabra over time. The findings suggest that there will be a reduction in the suitability of locations for the species. Moreover, it is expected that under future climate scenarios, ecological connectivity will decline, especially from 2061 to 2100 in the SSP585 scenario. Notably, significant alterations are anticipated during the latter half of the twenty-first century, mainly outside the coastal areas of the Black Sea, where extensive regions would become unsuitable. Additionally, the species is projected to shift its range, decreasing its presence in inland regions while expanding along the coasts. The results show the vulnerability of this species against climate change, thereby demanding adaptive conservation measures to preserve it within the forest ecosystems of Türkiye.

Magnetocaloric and Magnetostrictive Properties of the Tb(Co,In)<sub>2</sub> Laves Phases

Multicomponent polycrystalline TbInxCo2–x (with х = 0–0.2) solid solutions are prepared for the first time, and their crystal structure and magnetic, magnetocaloric, and magnetostrictive properties are studied. X-ray diffraction patterns taken at room temperature demonstrate mainly the presence of the cubic C15 Laves phase in all samples. As the indium content increases to x = 0.1, the lattice parameter is found to increase; the further increase in the indium content to х = 0.2 leads to a decrease in the lattice parameter. In this case, the Curie temperature TC monotonically increases to 245 K. The isotheral magnetic entropy change ΔSmag is calculated in accordance with magnetic measurements using the thermodynamic Maxwell’s relation. At a magnetic field change from 0 to 1.8 T, the maximum entropy change monotonically decreases and, for composition with x = 0.2, is 1.8 J/(kg∙К). As the indium content increases to x = 0.05, the volume magnetostriction increases. The further increase in the indium concentration leads to the decrease in the peak values and their shift to high temperatures.

Excellent Magnetocaloric Properties near 285 K of Amorphous Fe88Pr6Ce4B2 Ribbon

A novel amorphous Fe88Pr6Ce4B2 ribbon with better magnetocaloric properties near 285 K is reported in the present work. The Fe88Pr6Ce4B2 ribbon exhibits a typical second-order ferromagnetic–paramagnetic transition near its Curie temperature (Tc, ~284 K), with a maximum magnetic entropy change (−ΔSmpeak) of ~4.15 J/(kg × K) under 5 T and a maximum adiabatic temperature rise (ΔTad) of ~2.57 K under 5 T, both of which are almost the largest amongst the iron-based metallic glasses with Tc = 285 ± 10 K. The high −ΔSmpeak enables several amorphous hybrids with table-like −ΔSm–T curves to be synthesized by appropriately proportioning the Fe88Pr6Ce4B2 ribbon and other amorphous ribbons with different Tc. The larger average −ΔSm and effective refrigeration capacity, as well as the appropriate temperature range, make the two amorphous hybrids potential candidates for use as refrigerants in household magnetic air conditioners.

Potential Global Distribution and Habitat Shift of Prunus subg. Amygdalus Under Current and Future Climate Change

The genus of Prunus subg. Amygdalus are endangered Tertiary-relict plants that are an essential source of woody plant oil. In order to provide a theoretical basis for better protection and utilization of species in the Prunus subg. Amygdalus. This study collected global distribution information for six species within the Prunus subg. Amygdalus, along with data on 29 environmental and climatic factors. The Maximum Entropy (MaxEnt) model was used to simulate the globally suitable distribution areas for these species within the subgenus. The suitable results showed that the area under the test curve (AUC) values of the simulation results were more than 0.8, indicating that the simulation results have high accuracy. Temperature, precipitation, UV-B, and altitude were critical environmental factors affecting the distribution of each species in Prunus subg. Amygdalus. Currently, the distribution area of six species in this genus, from largest to smallest, is Prunus triloba (Lindl.) Ricker, Prunus tenella Batsch, Prunus amygdalus Batsch, Prunus pedunculata Maxim, Prunus mongolica Maxim and Prunus tangutica (Batal.) Korsh. The simulation results of distribution areas showed that under the ssp2.45 and ssp5.85 scenarios, the potential distribution areas of P. amygdalus, P. tangutica, and P. pedunculata all show a decreasing trend, while the distribution areas of P. mongolica and P. tenella, and P. triloba exhibit an increasing trend. The general distribution of P. amygdalus, P. mongolica, and P. tenella will trend to transfer in a northwest direction. P. tangutica and P. pedunculata were affected by other environmental factors (such as slope, altitude, and soil pH), and the distribution area has a tendency to move northeastward. The P. triloba moved to the southwest. The spatiotemporal distribution patterns of Prunus subg. Amygdalus can be used as a reference for forest management and to formulate species conservation strategies.

Analysis of current and future bioclimatic suitability for C. arabica production in Ethiopia.

The coffee sector in Ethiopia is the livelihood of more than 20% of the population and accounts more than 25% of the country's foreign exchange earnings. Climate change is expected to affect the climatic suitability of coffee in Ethiopia, and this would have implications for global coffee output, the national economy, and farmers' livelihoods in Ethiopia. The objective of this paper is to assess the current and future impacts of climate change on bioclimatic suitability to C.arbica production in Ethiopia. Based on the current distribution of coffee production areas and climate change predictions from HadGEM2 and CCSM2 models and using the Maximum Entropy (MaxEnt) bioclimatic modeling approach, future changes in climatic suitability for C. arabica were predicted. Coffee production sites in Ethiopia were geo-referenced and used as input in the MAXENT model. The findings indicated that climate change will increase the suitable growing area for coffee by about 44.2% and 30.37% under HadGEM2 and CCSM2 models, respectively, by 2080 in Ethiopia. The study also revealed a westward and northwestward shift in the climatic suitability to C. arabica production in Ethiopia. This indicates that the suitability of some areas will continue with some adaptation practice, whilst others currently suitable will be unsuitable, yet others that are unsuitable will be suitable for arabica coffee production. These findings are intended to support stakeholders in the coffee sector in developing strategies for reducing the vulnerability of coffee production to climate change. Site-specific strategies should be developed to build a more climate resilient coffee livelihood in the changing climate.

Spatiotemporal variations and driving factors of fine-scale habitat use by the Yangtze finless porpoise population in the Yangtze River.

The Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis) is critically endangered and has suffered from extensive habitat loss and fragmentation. Knowledge of its habitat preference could assist the conservation of the species and associated ecosystem. In the present study, spatiotemporal variations and driving factors of habitat use of a Yangtze finless porpoise population were studied in a 30-km section of the Yangtze mainstream. Seasonal variation in porpoise occurrences was observed based on visual surveys conducted from 2022 to 2023, with five surveys during the dry season and seven surveys during the wet season. Biological and environmental factors were synchronously sampled in both seasons. The maximum entropy model (MaxEnt) was applied to get the effects of biological and environmental variables on porpoise habitat preference. Within the study area, high-suitability habitats of the finless porpoises accounted for 19.0% and 15.4% of the area during the dry and wet seasons, respectively. The high-suitability habitat shifted from the mainstream in the dry season to the sandbar head and tributary area in the wet season. In the dry season, variables influencing the distribution were phytoplankton biomass, water velocity, and zooplankton biomass, which contributed 97.05% variation in the MaxEnt modeling. In the wet season, water depth, surface water temperature, and zooplankton biomass accounted for 97.69% variation of the distribution. Above results highlight that food availability plays an important role in porpoises distribution regardless of seasons, particularly in the dry season. This is because plankton is the primary food source for filter-feeding and omnivorous fish, especially those in the upper-middle layer which are frequently preyed on by finless porpoises. Suitable areas for finless porpoises, particularly those with high suitability, differed significantly across seasons at a fine-scale in the Yangtze mainstream. Seasonal variations in habitat were driven by different factors. But food availability plays an important role in porpoises distribution regardless of seasons, particularly in the dry season. These results suggest prioritizing conservation of the finless porpoise in the dry season when fish resources are relatively scarce. Measures including monitoring and evaluating prey resources should be considered. More attention should also be paid on management of shipping in the dry season given that the high-suitability habitat of the finless porpoises shifts to the mainstream.

Unveiling the lifeblood of cities: Identifying urban ecological networks from the perspective of biodiversity conservation

Urban biodiversity faces threats from habitat loss, landscape fragmentation, and human disturbances. Ecological networks (ENs) can enhance habitat connectivity and bolster population resilience to disruptions. To safeguard the biodiversity of Nanjing's urban area, this study selected understory insectivorous birds as indicator species for biodiversity, employing a comprehensive approach integrating the maximum entropy (MaxEnt) model and the area threshold method to delineate ecological source areas. Key indicators directly linked to the species distribution were extracted based on the habitat suitability assessment results, and an ecological resistance surface was crafted using spatial principal component analysis. Subsequently, the circuit theory model was applied to pinpoint ecological corridors, pinch points, and barrier points. The findings unveiled the following: (1) The normalized difference vegetation index (NDVI), human activities (HA), and canopy height (CH) were critical indicators influencing biodiversity. The response curves of the NDVI and CH were positively correlated with the probability distribution of indicator species, while the response curve of the HA showed an overall negative correlation with the same distribution. (2) The spatial distribution characteristics of the ecological network revealed a ‘one axis, two cores, intersecting’ pattern. Ecological sources were supported by mountainous forests and riverside green spaces, symmetrically distributed on both sides of the Yangtze River. Ecological corridors were forming continuous tree belts along highways and riverways, predominantly concentrated on the eastern side of the Yangtze River. Ecological pinch points and barriers were primarily located at the intersections of fragmented green spaces and developed areas in the northeastern and southeastern regions of the study area. (3) This study finally identified 30 ecological source areas, 65 ecological corridors, 8 pinch points, and 8 barrier points. This study presents a construction paradigm for urban ENs from the vantage point of biodiversity conservation, including both regional specificity and universal applicability, providing vital theoretical underpinnings and pragmatic insights for urban sustainability.

Effects of Eu3+ doping on the structural, magnetocaloric effect and critical behavior of Ruddlesden-Popper compounds La1.4-xEuxSr1.6Mn2O7 (0 ≤ x ≤ 0.1)

The effects of Eu3+ doping on the structural and magnetocaloric effect of Ruddlesden-Popper compounds La1.4-xEuxSr1.6Mn2O7 (0 ≤ x ≤ 0.1) were investigated. The X-ray diffraction and infrared spectroscopy confirmed that La1.4-xEuxSr1.6Mn2O7 compounds exhibited the tetragonal lattice symmetry (I4/mmm) of the Ruddlesden-Popper phase. The crystal structure of La1.4-xEuxSr1.6Mn2O7 compounds distorted and phase separated as the amount of Eu3+ doping varied. The magnetization-temperature curves demonstrated that La1.4-xEuxSr1.6Mn2O7 compounds display a notable transition temperature attributed to its anisotropic exchange coupling. The maximum magnetic entropy changes (−ΔSMmax) of La1.4-xEuxSr1.6Mn2O7(x = 0, 0.075, and 0.1) compounds are 2.82, 3.12 and 3.40 J/(kg·K) at the 5 T applied magnetic field. Meanwhile, its relative cooling power (RCP) was 186.37, 207.42 and 242.54 J/kg, respectively. The critical behavior of La1.4-xEuxSr1.6Mn2O7 (0≤x≤0.1) compounds was analyzed around Tc, and it was found that the critical index (β = 0.413, γ = 1.020, δ = 3.038) is essentially in line with the mean field theory.

MEGA: Maximum-Entropy Genetic Algorithm for Router Nodes Placement in Wireless Mesh Networks

Over the past decade, wireless mesh networks (WMNs) have seen significant advancements due to their simple deployment, cost-effectiveness, ease of implementation, and reliable service coverage. However, despite these advantages, the placement of nodes in WMNs presents a critical challenge that significantly impacts their performance. This issue is recognized as an NP-hard problem, underscoring the necessity of development optimization algorithms, such as heuristic and metaheuristic approaches. This motivated us to develop the Maximum Entropy Genetic Algorithm (MEGA) to address the issue of mesh router node placement in WMNs. To assess the proposed method, we conducted experiments across various scenarios with different settings, focusing on key metrics such as network connectivity and user coverage. The simulation results showed the comparative performance of MEGA in relation to other prominent algorithms, such as the Coyote Optimization Algorithm (COA), Firefly Algorithm (FA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), revealing MEGA’s effectiveness and usability in determining optimal locations for mesh routers.