Entropy Algorithm Research Articles

Cardiac arrhythmia detection using cross-sample entropy measure based on short and long RR interval series.

Accurate arrhythmia (atrial fibrillation (AF) and congestive heart failure (CHF)) detection is still a challenge in the biomedical signal-processing field. Different linear and nonlinear measures of the electrocardiogram (ECG) signal analysis are used to fix this problem. Sample entropy (SampEn) is used as a nonlinear measure based on single series to detect healthy and arrhythmia subjects. To follow this measure, the proposed work presents a nonlinear technique, namely, the cross-sample entropy (CrossSampEn) based on two series to quantify healthy and arrhythmia subjects. The research work consists of 10 records of normal sinus rhythm, 20 records of Fantasia (old group), 10 records of AF, and 10 records of CHF. The method of CrossSampEn has been proposed to obtain the irregularity between two same and different R-R (R peak to peak) interval series of different data lengths. Unlike the SampEn technique, the CrossSampEn technique never awards a 'not defined' value for very short data lengths and was found to be more consistent than SampEn. One-way ANOVA test has validated the proposed algorithm by providing a large F value and p < .0001. The proposed algorithm is also verified by simulated data. It is concluded that different RR interval series of approximate 1500 data points and same RR interval series of approximate 1000 data points are required for health-status detection with embedded dimensions, M=2 and threshold, r=.2. Also, CrossSampEn has been found more consistent than Sample entropy algorithm.

Species Distribution Modelling under Climate Change Scenarios for Maritime Pine (Pinus pinaster Aiton) in Portugal

To date, a variety of species potential distribution mapping approaches have been used, and the agreement in maps produced with different methodological approaches should be assessed. The aims of this study were: (1) to model Maritime pine potential distributions for the present and for the future under two climate change scenarios using the machine learning Maximum Entropy algorithm (MaxEnt); (2) to update the species ecological envelope maps using the same environmental data set and climate change scenarios; and (3) to perform an agreement analysis for the species distribution maps produced with both methodological approaches. The species distribution maps produced by each of the methodological approaches under study were reclassified into presence–absence binary maps of species to perform the agreement analysis. The results showed that the MaxEnt-predicted map for the present matched well the species’ current distribution, but the species ecological envelope map, also for the present, was closer to the species’ empiric potential distribution. Climate change impacts on the species’ future distributions maps using the MaxEnt were moderate, but areas were relocated. The 47.3% suitability area (regular-medium-high), in the present, increased in future climate change scenarios to 48.7%–48.3%. Conversely, the impacts in species ecological envelopes maps were higher and with greater future losses than the latter. The 76.5% suitability area (regular-favourable-optimum), in the present, decreased in future climate change scenarios to 58.2%–51.6%. The two approaches combination resulted in a 44% concordance for the species occupancy in the present, decreasing around 30%–35% in the future under the climate change scenarios. Both methodologies proved to be complementary to set species’ best suitability areas, which are key as support decision tools for planning afforestation and forest management to attain fire-resilient landscapes, enhanced forest ecosystems biodiversity, functionality and productivity.

Fault diagnosis of real-scenario battery systems based on modified entropy algorithms in electric vehicles

Entropy algorithm is an efficient fault diagnosis technology gradually gaining popularity, which is highly promising in terms of battery safety protection in electric vehicles. This paper compares two entropy algorithms commonly used for battery fault diagnosis and their ability to diagnose abnormal fluctuations implied in the pre-fault phase. By exploring the influence of key calculation factors in the entropy algorithm on the fault diagnosis effect, this study pioneered the discovery of a significant normal distribution pattern and the logarithmic relationship between the diagnostic effect and the size of the Shannon entropy's calculation window and multi-scale sample entropy's scale factor, respectively. The relationship between computation time and computation factor was found to provide a theoretical basis for improving diagnosis efficiency through the rational selection of computation factors. Furthermore, based on actual vehicle fault data verification, a multi-level diagnosis strategy with strong robustness and generality is proposed using statistical methods. More importantly, this study continues to explore the importance of deeper mining of different entropy features, and the proposed control strategies are of high practical significance for battery fault diagnosis/prognosis in real-world vehicle applications.

Quantized kernel recursive minimum error entropy algorithm

In this paper, we propose a online vector quantization (VQ) method based on the kernel recursive minimum error entropy (KRMEE) algorithm. According to information theoretic learning (ITL), the minimum error entropy criterion (MEE) is robust and can effective resistance to non-Gaussian noise. By combining the kernel recursive least squares (KRLS) algorithm with MEE criterion, KRMEE algorithm has been generated, which has excellent performance in non-Gaussian environments. However, with the size of data increases, the computational complexity will raise. We propose a quantized to solve this problem, the input space of the algorithm is quantized to suppress the linear growth radial basis function (RBF) network in kernel adaptive filtering (KAF). The VQ method is different from novelty criterion (NC), approximate linear dependency (ALD) criterion, and other sparsity methods, the online VQ method need to construct the dictionary, and calculate the distance by Euclidean norm. We propose a novel quantized kernel recursive minimum error entropy (QKRMEE) algorithm by combining VQ method with KRMEE algorithm, and update the solution with a recursive algorithm. In Mackey-Glass time series and a real-world datasets, Monte Carlo simulation experiments show that the proposed algorithm achieves better predictive performance in non-Gaussian noise environment. Meanwhile, the algorithm can restrain the growth of RBF network well, thus reducing the computational complexity and memory consumption effectively.

Inverse synthetic aperture radar autofocus imaging of block structure targets with sparse aperture

Inverse synthetic aperture radar (ISAR) autofocus imaging performance is challenged by the residual phase errors that arise from the traditional motion compensation methods with sparse aperture. Moreover, sparse reconstruction algorithms usually ignore the block sparsity in the ISAR image, which cannot recover structured information of the block structure target completely. An imaging method based on joint minimum Tsallis entropy and pattern-coupled sparse Bayesian learning algorithm is proposed to achieve ISAR autofocus imaging of block structure targets with sparse aperture. A pattern-coupled hierarchical complex Gaussian prior is utilized to characterize the correlation among ISAR image pixels in the complex domain. Such a prior model has the potential to encourage block-sparse patterns and achieve ISAR sparse aperture imaging of block structure targets without knowing the block partition prior information. The residual phase errors are estimated based on the minimum Tsallis entropy criterion during the sparse reconstruction of ISAR image to achieve ISAR autofocus imaging, which has the advantage of improving the computational efficiency compared with minimum Shannon entropy criterion. The superiority of the proposed algorithm is verified by the experimental results based on both simulated and measured data.

Predicting the Impact of Climate Change on the Distribution of Rhipicephalus sanguineus in the Americas

Climate change may influence the incidence of infectious diseases including those transmitted by ticks. Rhipicephalus sanguineus complex has a worldwide distribution and transmits Rickettsial infections that could cause high mortality rates if untreated. We assessed the potential effects of climate change on the distribution of R. sanguineus in the Americas in 2050 and 2070 using the general circulation model CanESM5 and two shared socioeconomic pathways (SSPs), SSP2-4.5 (moderate emissions) and SSP2-8.5 (high emissions). A total of 355 occurrence points of R. sanguineus and eight uncorrelated bioclimatic variables were entered into a maximum entropy algorithm (MaxEnt) to produce 50 replicates per scenario. The area under the curve (AUC) value for the consensus model (&gt;0.90) and the partial ROC value (&gt;1.28) indicated a high predictive capacity. The models showed that the geographic regions currently suitable for R. sanguineus will remain stable in the future, but also predicted increases in habitat suitability in the Western U.S., Venezuela, Brazil and Bolivia. Scenario 4.5 showed an increase in habitat suitability for R. sanguineus in tropical and subtropical regions in both 2050 and 2070. Habitat suitability is predicted to remain constant in moist broadleaf forests and deserts but is predicted to decrease in flooded grasslands and savannas. Using the high emissions SSP5-8.5 scenario, habitat suitability in tropical and subtropical coniferous forests and temperate grasslands, savannas, and shrublands was predicted to be constant in 2050. In 2070, however, habitat suitability was predicted to decrease in tropical and subtropical moist broadleaf forests and increase in tropical and subtropical dry broadleaf forests. Our findings suggest that the current and potential future geographic distributions can be used in evidence-based strategies in the design of control plans aimed at reducing the risk of exposure to zoonotic diseases transmitted by R. sanguineus.

Not only climate: The importance of biotic interactions in shaping species distributions at macro scales.

Abiotic factors are usually considered key drivers of species distribution at macro scales, while biotic interactions are mostly used at local scales. A few studies have explored the role of biotic interactions at macro scales, but all considered a limited number of species and obligate interactions. We examine the role of biotic interactions in large-scale SDMs by testing two main hypotheses: (1) biotic factors in SDMs can have an important role at continental scale; (2) the inclusion of biotic factors in large-scale SDMs is important also for generalist species. We used a maximum entropy algorithm to model the distribution of 177 bat species in Africa calibrating two SDMs for each species: one considering only abiotic variables (noBIO-SDMs) and the other (BIO-SDMs) including also biotic variables (trophic resource richness). We focused the interpretation of our results on variable importance and response curves. For each species, we also compared the potential distribution measuring the percentage of change between the two models in each pixel of the study area. All models gave AUC >0.7, with values on average higher in BIO-SDMs compared to noBIO-SDMs. Trophic resources showed an importance overall higher level than all abiotic predictors in most of the species (~68%), including generalist species. Response curves were highly interpretable in all models, confirming the ecological reliability of our models. Model comparison between the two models showed a change in potential distribution for more than 80% of the species, particularly in tropical forests and shrublands. Our results highlight the importance of considering biotic interactions in SDMs at macro scales. We demonstrated that a generic biotic proxy can be important for modeling species distribution when species-specific data are not available, but we envision that a multi-scale analysis combined with a better knowledge of the species might provide a better understanding of the role of biotic interactions.

Quantized Information in Spectral Cyberspace.

The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time-frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling method will depend on the signature of interest, the desired information, and the acceptable levels of uncertainty of signal and noise features. Selected Lamb wave signatures and information spectra from the 2022 Tonga eruption are presented as representative case studies. Resilient transformations from physical to information metrics are provided for sensor-agnostic signal processing, pattern recognition, and machine learning applications.

ANALISIS SENTIMEN PADA ULASAN APLIKASI INVESTASI ONLINE AJAIB PADA GOOGLE PLAY MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN MAXIMUM ENTROPY

Investment is money or asset to earn profits in the future. Online investment applications are already available, one of which is Ajaib. A review of Ajaib’s application is needed to find out reviews given are positive or negative. Sentiment analysis in Ajaib is used to see the user's response to Ajaib’s performance which is divided into positive and negative classes. Sentiment analysis of the Ajaib’s reviews classification can be used with the Support Vector Machine and Maximum Entropy methods. Support Vector Machine on non-linear problems inserts the kernel into a high-dimensional space, to find a hyperplane that can maximize the distance between classes. The kernel used in SVM is the Radial Basis Function (RBF) kernel with gamma parameters of 0.002 and Cost (C) of 0.1; 1; 10. Maximum Entropy is a classification technique that uses the entropy value to classify data with the evaluation model used, namely 5-fold cross-validation. The algorithm which has the highest accuracy and kappa statistics is the best algorithm for classifying the sentiments of Ajaib users. The results using the Support Vector Machine algorithm show the overall accuracy is 85.75% and the kappa accuracy is 58.07%. The results using the Maximum Entropy algorithm show an overall accuracy of 83% and kappa accuracy of 50.5%. This shows that sentiment using the Support Vector Machine has a better performance than Maximum Entropy.

Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment?

So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale.

Fungus under a Changing Climate: Modeling the Current and Future Global Distribution of Fusarium oxysporum Using Geographical Information System Data.

The impact of climate change on biodiversity has been the subject of numerous research in recent years. The multiple elements of climate change are expected to affect all levels of biodiversity, including microorganisms. The common worldwide fungus Fusarium oxysporum colonizes plant roots as well as soil and several other substrates. It causes predominant vascular wilt disease in different strategic crops such as banana, tomato, palm, and even cotton, thereby leading to severe losses. So, a robust maximum entropy algorithm was implemented in the well-known modeling program Maxent to forecast the current and future global distribution of F. oxysporum under two representative concentration pathways (RCPs 2.6 and 8.5) for 2050 and 2070. The Maxent model was calibrated using 1885 occurrence points. The resulting models were fit with AUC and TSS values equal to 0.9 (±0.001) and 0.7, respectively. Increasing temperatures due to global warming caused differences in habitat suitability between the current and future distributions of F. oxysporum, especially in Europe. The most effective parameter of this fungus distribution was the annual mean temperature (Bio 1); the two-dimensional niche analysis indicated that the fungus has a wide precipitation range because it can live in both dry and rainy habitats as well as a range of temperatures in which it can live to certain limits. The predicted shifts should act as an alarm sign for decision makers, particularly in countries that depend on such staple crops harmed by the fungus.

Modelling Distributions of Asian and African Rice Based on MaxEnt

Rice landraces, including Asian rice (Oryza sativa L.) and African rice (Oryza glaberrima Steud.), provide important genetic resources for rice breeding to address challenges related to food security. Due to climate change and farm destruction, rice landraces require urgent conservation action. Recognition of the geographical distributions of rice landraces will promote further collecting efforts. Here we modelled the potential distributions of eight rice landrace subgroups using 8351 occurrence records combined with environmental predictors with Maximum Entropy (MaxEnt) algorithm. The results showed they were predicted in eight sub-regions, including the Indus, Ganges, Meghna, Mekong, Yangtze, Pearl, Niger, and Senegal river basins. We then further revealed the changes in suitable areas of rice landraces under future climate change. Suitable areas showed an upward trend in most of study areas, while sub-regions of North and Central China and West Coast of West Africa displayed an unsuitable trend indicating rice landraces are more likely to disappear from fields in these areas. The above changes were mainly determined by changing global temperature and precipitation. Those increasingly unsuitable areas should receive high priority in further collections. Overall, these results provide valuable references for further collecting efforts of rice landraces, while shedding light on global biodiversity conservation.

Modeling current and future distribution patterns of Uvaria chamae in Benin (West Africa): Challenges and opportunities for its sustainable management

Uvaria chamae is a wild shrub species widely used as a source for traditional medicine, food and fuel in West Africa. The species is threatened by uncontrolled harvesting of its roots for pharmaceutical applications and by the extension of agricultural land. This study assessed the role of environmental variables for the current distribution and the potential impact of climate change on the future spatial distribution of U. chamae in Benin. We used data related to climate, soil, topography and land cover to model the distribution of the species. Occurrence data were combined with six least correlated bioclimatic variables derived from the WorldClim database, data on soil layers (texture and pH) and topography (slope) obtained from the FAO world database and land cover from the DIVA-GIS site. Random Forest (RF), Generalized Additive Models (GAM), Generalized Linear Models (GLM) and the Maximum Entropy (MaxEnt) algorithm were used to predict the current and future (2050–2070) distribution of the species. Two climate change scenarios (SSP245 and SSP585) were considered for the future predictions. The results showed that climate (i.e., water availability) and soil type are the key predictors of the distribution of the species. Based on future climate projections, RF, GLM and GAM models predict that the Guinean-Congolian and Sudano-Guinean zones of Benin will remain suitable for U. chamae, while it will decline in these zones according to the MaxEnt model. These results call for a timely management effort for the species in Benin through its introduction into agroforestry systems to ensure the continuity of its ecosystem services.

Species distribution modelling of Monotheca buxifolia (Falc.) A. DC.: Present distribution and impacts of potential climate change

Species distribution modelling (SDM) is an important tool to examine the possible change in the population range and/or niche-shift under current environment and predicted climate change. Monotheca buxifolia is an economically and ecologically important tree species inhabiting Pakistan and Afghanistan in dense patches, and species range is contracting rapidly. This study hypothesize that predicted climate change might remarkably influence the existing distribution pattern of M. buxifolia in the study area. A total of 75 occurrence locations were identified comprising M. buxifolia as a dominant tree species. The Maximum Entropy (MaxEnt) algorithm was utilized to perform the SDM under current (the 1970s–2000s) and two future climate change scenarios (shared socioeconomic pathways: SSPs 245 and 585) of two time periods (the 2050s and 2070s). The optimal model settings were assessed, and simulation precision was assessed by examining the partial area under the receiver operating characteristic curve (pAUC-ROC). The results showed that out of 39 considered bio-climatic, topographic, edaphic, and remote sensing variables which were utilized in the preliminary model, 6 variables including precipitation of warmest quarter, topographic diversity, global human modification of terrestrial land, normalized difference vegetation index, isothermality, and elevation (in order) were the most influential drivers, and utilized in all reduced SDMs. A high predictive performance (pAUC-ROC; >0.9) of all the considered SDMs was recorded. A total of about 67,684 km2 of geographical area was predicted as suitable habitat (p > 0.8) for M. buxifolia, and Pakistan is the leading country (with about 54,975 km2 of suitable land area) under the current climate scenario. Overall, the existing distribution of the tree species in the study area might face considerable loss (i.e. rate of change %; −27 to −107) in future, and simultaneously a northward (high elevation) niche shift is predicted for all the considered future climate change scenarios. Hence, development and implementation of a coordinated conservation program is required on priority basis to save the tree species in its native geographic range.

SPATIALLY EXPLICIT ASSESSMENT OF THE SUITABLE AREAS FOR THE THREATENED IROKOS (MILICIA EXCELSA [WELW.] C.C. BERG) CONSERVATION IN BENIN: A COMBINATION OF GEOSTATISTICS AND ECOLOGICAL NICHE MODELING

Although Iroko (Milicia excelsa) is listed endangered and has a great socioeconomic and cultural importance in Benin, there have been few attempts to definition of sustainable conservation strategies for the species. This study explored the spatial patterns of the species and tested if the species distribution may be affected under future climates forecasts. Moran index was used to measure the spatial autocorrelation of the abundance of Iroko. For the niche modeling, records of the species were added to bioclimatic variables (current and future conditions) and soil layers in the maximum entropy algorithm. Results showed overall a spatial dependency between Iroko population according to the density (P &lt; 0.001). The population density seems similar between 0 and 3 km but differ from 3 to 150 km. A slight increase was noted from the present-day distribution to the future forecasts (4.71 % and 6.95 % respectively following the scenarios RCP4.5 and RCP8.5). Urgent conservation actions are needed to safeguard the remnant populations of Iroko in Benin.

The identification and conservation of climate refugia for two Colombian endemic titi (Plecturocebus) monkeys

Natural resource managers face the challenge of developing conservation plans for key species and given that anthropogenic climate change (CC) effects on biodiversity are becoming increasingly evident, the new challenge is to properly incorporate CC adaptation strategies into such plans. Thus, the objective of this study is to evaluate the potential CC effects on the climatically suitable areas for two Colombian endemic titi monkeys Plecturocebus ornatus and P. caquetensis and to identify the prospective climate refugia as macro-ecological adaptation strategies for each species. A detailed ecological niche modeling (ENM) approach was applied with the maximum entropy algorithm, using presence records and different sets of bioclimatic variables describing baseline (1960–1990) and future climates (∼2070). Models of future climatic suitability were generated using projections of variables under a stabilization (RCP4.5) and business as usual (RCP8.5) scenarios with data from two general circulation models (GCMs) describing storylines of increasing (CESM1_CAM5) and decreasing (CSIRO_ACCESS1_3) rainfall patterns. The results for both species indicate that in a warmer future, opposite rainfall patterns and choice of the bioclimatic variables may lead to divergent responses on the extent and geographic distribution of their climatic niche, which varied from regions gaining, losing, and retaining suitability in potential climate refugia. Moreover, CC represents a serious threat for P. caquetensis and P. ornatus since their ranges may be largely exposed to novel climates. Their baseline climatic suitability area is projected to shrink and shift to higher elevations in the Andes mountains, and the climate refugia identified for both species are poorly covered by protected areas. Therefore, the climate refugia identified in this work and the management recommendations offered should be considered by species conservation plans to contribute to the selection of priority regions for conservation actions. The modeling approach reveals the uncertainties arising from the selection of bioclimatic variables and GCMs in ENM, which can be replicated to identify climate refugia targeting different species of conservation concern.

Modelling Mediterranean oak palaeolandscapes using the MaxEnt model algorithm: The case of the NE Iberia under the Middle Holocene climatic scenario

The Mediterranean Basin is a global biodiversity hotspot, and oak tree species play an important role in it. Since the beginning of the Holocene (∼11.4 kyr BP), the distribution of forests has not occurred uniformly, resulting in diverse types of vegetation landscapes. In this study, we used a maximum entropy algorithm (MaxEnt) to obtain the ecological niche model (ENM) of two sub-Mediterranean oak species, Quercus pubescens Willd. (pubescent oak) and Quercus ilex subsp. ilex (holm oak), both in the present day in the Iberian Peninsula and within a Middle Holocene (8.2–4.2 kyr BP) climatic scenario in the NE Iberian Peninsula. Moreover, we used the locations of Neolithic archaeological sites containing anthracological data to analyze the relationship between human occupations and oak habitats. Our results suggest that the two oaks have responded differently to the climatic conditions that have occurred, and show changes in both potential distributions. The palaeolandscape vegetation map shows a denser vegetation cover in the lowlands and a more open landscape in the highlands, with a higher dominance of Quercus pubescens in the septentrional areas, while Quercus ilex was more restricted to certain coastal areas. Temperature and precipitation factors, mainly seasonal climatic conditions, have had a greater impact on the distribution of vegetation than other factors. We found a good overlap between the ENM of the two oaks and the locations of the Neolithic sites analysed, and determined that the distribution of Neolithic archaeological sites is not random. The Neolithic populations in the study area depended heavily on the resources of the deciduous and evergreen sub- Mediterranean forest, although they also exploited the resources of the mountain pine forest. Neolithic sites distribution suggests that Neolithic human groups were aware of the potential of forests and probably gathered woody resources in their surroundings.

Hierarchical entropy analysis for flow pattern of steam-liquid two phase flow in a rod bundle

Flow pattern is very key parameter in the study of flow characteristics and heat transfer characteristics of two-phase flow. In this Paper, two-phase boiling experiment in a 3×3 rod bundle was conducted within the following parameter range: 0≤G≤600kg/(m2⋅s),0.1≤p≤0.12MPa,10≤ΔTsub≤30℃. We first observed the four typical flow patterns, including bubble flow, bubble-churn flow, churn flow, annular flow, and the physical characteristics were studied and the corresponding differential pressure fluctuation signals measured for the four typical flow patterns. The dynamic characteristic of typical flow pattern was investigated by the of hierarchical entropy algorithm. Then the hierarchical entropy value of different flow patterns was calculated. The results indicate that the hierarchical entropy has a better ability to resist noise, and can reveal dynamics complexity of different flow pattern in rod bundle.

Occurrence Prediction of Western Conifer Seed Bug (Leptoglossus occidentalis: Coreidae) and Evaluation of the Effects of Climate Change on Its Distribution in South Korea Using Machine Learning Methods

The western conifer seed bug (WCSB; Leptoglossus occidentalis) causes huge ecological and economic problems as an alien invasive species in forests. In this study, a species distribution model (SDM) was developed to evaluate the potential occurrence of the WCSBs and the effects of climate on WCSB distribution in South Korea. Based on WCSB occurrence and environmental data, including geographical and meteorological variables, SDMs were developed with maximum entropy (MaxEnt) and random forest (RF) algorithms, which are machine learning methods, and they showed good performance in predicting WCSB occurrence. On the potential distribution map of WCSBs developed by the model ensemble with integrated MaxEnt and RF models, the WCSB occurrence areas were mostly located at low altitudes, near roads, and in urban areas. Additionally, environmental factors associated with anthropogenic activities, such as roads and night lights, strongly influenced the occurrence and dispersal of WCSBs. Metropolitan cities and their vicinities in South Korea showed a high probability of WCSB occurrence. Furthermore, the occurrence of WCSBs in South Korea is predicted to intensify in the future owing to climate change.

Diffusion Mixture Minimum Total Error Entropy Adaptive Filtering Algorithm and Its Performance Analysis

The distributed adaptive filtering algorithms applied in the error-in-variables (EIV) model consider that both input and output signals suffer from noise disturbance for practical applications. However, the traditional distributed adaptive filtering algorithms applied in the EIV model cannot combat non-Gaussian noises well. To address this issue, this paper develops a mixture minimum total error entropy (MMTE) method and its diffusion mixture minimum total error entropy (DMMTE) algorithm. The proposed DMMTE has the significant performance improvement in non-Gaussian noises, and is more general than the diffusion minimum total error entropy (DMTE) algorithm. To select mixture coefficient effectively, a variable mixture coefficient method is further proposed to generate a variable DMMTE (VDMMTE) algorithm. Moreover, the local mean stability analysis and steady-state performance analysis of DMMTE are presented. Simulations verify the correctness of the theoretical analysis and illustrate the performance superiority of the proposed DMMTE.