Path Distance Research Articles

Evaluation of Sit-to-Stand Movement in Older Adults with Locomotive Syndrome Using the Nintendo Wii Balance Board.

Standing up from a chair is a mechanically demanding daily motion, and its biomechanics represent motor performance. In older adults with locomotive syndrome (LS), sit-to-stand (STS) movement with adequate postural control is essential to prevent falls. This study evaluated the characteristics of dynamic balance during STS movement on older adults with LS. A total of 116 participants aged ≥65 years were divided into Non-LS, LS stage 1, and LS stage 2 groups using the LS risk test. The participants were instructed to stand on the Nintendo Wii Balance Board as quickly as possible, and the STS movement was quantified using the vertical ground reaction force (VGRF) and center of pressure (CoP). The STS score, which represented dynamic balance, was significantly different among the groups (p < 0.001). The rate of VGRF development was significantly lower in the LS stages 1 and 2 than in the Non-LS group (p < 0.001). On the other hand, the total distance of the CoP path did not differ among the groups (p = 0.211). These findings indicated a reduction of postural control in older adults with LS. The STS score emphasized the importance of balance training to prevent falls in older adults with LS.

Adaptive Weighted K-Nearest Neighbor Trilateration Algorithm for Visible Light Positioning

An adaptive weighted K-nearest neighbor (AWKNN) trilateration positioning algorithm fused with the channel state information (CSI) is proposed to optimize the accuracy of the visible light positioning. The core concept behind this algorithm is to combine the WKNN algorithm with ranging based on the CSI. The direct path distance estimated by the CSI is utilized to construct a position set consisting of multiple positions and a corresponding distance database containing multiple distance vectors. The error parameters of the weighted combinations of different distance vectors are calculated iteratively to evaluate the impact of different K-values and weights on the positioning accuracy. The proposed algorithm can achieve high-precision trilateration positioning by adaptively selecting the K-value and weight. A typical 4 m × 4 m × 3 m indoor multipath scene with four LEDs is established to simulate the positioning performance. The simulation results reveal that the mean error of the CSI-based AWKNN algorithm achieves 1.84 cm, with a root mean square error (RMSE) of 2.13 cm. Compared with the CSI-based least squares (LS) method, the CSI-based nonlinear LS method, and the CSI-based WKNN method, the average error of this method is decreased by 29%, 16%, and 17%, whereas the RMSE is reduced by 35%, 14%, and 19%.

Convergent thinking and insight problem solving relate to semantic memory network structure

The associative theory of creativity has long held that creative thinking involves connecting remote concepts in semantic memory. Network science tools have recently been applied to map the organization of concepts in semantic memory, and to study the link between semantic memory and creativity. Yet such work has largely overlooked the domain of convergent thinking, despite the theoretical importance of semantic memory networks for facilitating associative processes relevant for convergent problem solving (e.g., spreading activation). Convergent thinking problems, such as the Compound Remote Associates (CRA) test, can be solved with insight (the sudden “aha” experience) or analysis (deliberately and incrementally working towards the solution). In a sample of 477 participants, we adopted network science methods to compare semantic memory structure across two grouping variables: 1) convergent thinking ability (i.e., CRA accuracy), and 2) the self-reported tendency to solve problems with insight or analysis. Semantic memory networks were constructed from a semantic fluency task, and problem solving style (insight or analysis) was determined from judgments provided during solving of CRAs. We found that, compared to the low-convergent thinking group, the high-convergent thinking group exhibited a more flexible and interconnected semantic network—with short paths and many connections between concepts. Moreover, participants who primarily solved problems with insight (compared to analysis) showed shorter average path distances between concepts, even after controlling for accuracy. Our results extend the literature on semantic memory and creativity, and suggest that the organization of semantic memory plays a key role in convergent thinking, including insight problem solving.

The Effects of Deep Brain Stimulation on Speech Motor Control in People With Parkinson's Disease.

Despite the overall benefits of deep brain stimulation (DBS) in Parkinson's disease (PD), its effects on speech production have been mixed when examined using auditory-perceptual and acoustic measures. This study investigated the effects of DBS on the lip and jaw kinematics during sentence production in individuals with dysarthria secondary to PD. Twenty-seven participants from three groups were included in the study: (a) individuals with PD and without DBS (PD group), (b) individuals with PD and with DBS (PD-DBS group), and (c) neurologically healthy control speakers (HC group). Lip and jaw movements during speech were recorded using optical motion capture and analyzed for path distance, speed, duration, articulatory stability, and interarticulator coordination. The PD-DBS group showed (a) increased path distance compared with the PD and HC groups and (b) increased speed compared with the PD group but not the HC group. Both PD and PD-DBS groups exhibited lengthened sentence duration compared with the HC group. Articulatory stability was greater for the two PD groups, PD and PD-DBS, compared with the HC group. Spatial, but not temporal, coordination was lower for the PD group than for the other two groups. The only kinematic changes between the DBS on and off conditions within the PD-DBS group were increases in spatial coordination. These data suggest that DBS primarily affects the amplitude scaling of articulatory movements, but not the temporal scaling, in individuals with PD. The findings are discussed with respect to the DBS-induced neural changes and their effects on speech motor control in PD.

EEDLABA: Energy-Efficient Distance- and Link-Aware Body Area Routing Protocol Based on Clustering Mechanism for Wireless Body Sensor Network

In medical environments, a wireless body sensor network (WBSN) is used to operate remotely, and sensor nodes are employed. It consists of sensor nodes installed on a human body to monitor a patient’s condition, such as heartbeat, temperature, and blood sugar level, and are functionalized and controlled by remote devices. A WBSN consists of nodes that are actually sensors in nature and are operated with a short range of communication. These sensor nodes are fixed with limited computation power and the main concern is energy consumption and path loss. In this paper, we propose a new protocol named energy-efficient distance- and link-aware body area (EEDLABA) with a clustering mechanism and compare it with the current link-aware and energy-efficient body area (LAEEBA) and distance-aware relaying energy-efficient (DARE) routing protocols in a WBSN. The proposed protocol is an extended type of LAEEBA and DARE in which the positive features have been deployed. The clustering mechanism has been presented and deployed in EEDLABA for better performance. To solve these issues in LAEEBA and DARE, the EEDLABA protocol has been proposed to overcome these. Path loss and energy consumption are the major concerns in this network. For that purpose, the path loss and distance models are proposed in which the cluster head (CH) node, coordinator (C) node, and other nodes, for a total of nine nodes, are deployed on a human body. The results have been derived from MATLAB simulations in which the performance of the suggested EEDLABA has been observed in assessment with the LAEEBA and DARE. From the results, it has been concluded that the proposed protocol can perform well in the considered situations for WBSNs.

Indoor Location Fingerprinting Algorithm Based on Path Loss Parameter Estimation and Bayesian Inference

For indoor positioning methods based on Wi-Fi fingerprint, a high-density location fingerprint database usually needs to be constructed to ensure high-precision positioning requirements. At the same time, when the indoor area is huge or the number of reference points (RPs) is large and densely distributed, the computational burden of the online phase will be increased. To solve the above two problems, we propose an indoor location fingerprinting algorithm based on path loss parameter estimation and Bayesian inference (FA-PEBI). In the off-line phase, first, we collect location fingerprints from a few RPs in different rooms; second, the location fingerprints collected are filtered; third, the path loss parameters (PLPs) in different rooms are trained by using a logarithmic distance path loss (LDPL) model; and finally, predict the fingerprint information of the other locations in that room based on the trained parameters. In the online phase, the real-time fingerprint information of the user is first filtered, and then, the room where the user is located is predetermined by using the Bayesian inference method. Subsequently, the corresponding fingerprint database is selected to match based on the regional adaptive selection (RAS) algorithm. Eventually, the ultimate location of the user is obtained based on the Manhattan distance. The experimental results show that the proposed algorithm improves the localization accuracy by at least 16% compared with other existing methods, both in the Wireless Insite simulation data and MAN dataset, and still outperforms the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -nearest neighbor (KNN) algorithm when the fingerprint collection effort in the off-line phase is reduced by half.

Research on the Siting of Rural Public Cultural Space Based on the Path-Clustering Algorithm: A Case Study of Yumin Township, Yushu City, Jilin Province, China

Cultural revitalization is the foundation of rural revitalization. Rural public cultural spaces are important places for rural residents to participate in cultural activities and important carriers for the occurrence, inheritance and development of rural culture. In this study, we propose a siting analysis method for public cultural spaces based on a path-clustering algorithm. Then, Yumin Township in Jilin Province is used as a case study to test the method. Within the township area, public cultural spaces are divided into three types based on the difference in service scope: “single-village decentralized”, “single-village concentration” and “multi-village concentration”. This paper focuses on the analysis of the siting of the “multi-village concentration” type of public cultural spaces using a path-clustering algorithm. First, the path-clustering algorithm is written, and then its parameters, initial clustering centers and number of clusters are analyzed for their effect on the clustering results. Two parameters, within-cluster sum of path distance (WCSPD) and maximum value of the path distance within-cluster (MPDWC), are proposed to evaluate the results of the algorithm. Then, taking Yumin Township of Jilin Province as an example, the villages in the township are clustered with travel distance as the main influencing factor. It is ensured that the shortest path distance from the central village in each cluster to other villages is no greater than the set travel distance. Finally, the public cultural space is constructed in the central village to serve all villages in the cluster. This siting analysis method can output a variety of results that can be used as a reference for decision making in rural public cultural space planning.

The Influence of Built-Environment Factors on Connectivity of Road Networks in Residential Areas: A Study Based on 204 Samples in Nanjing, China

Over the past decades, China has built a large number of superblocks and gated residential areas, which have contributed to increased congestion on urban road traffic. Although some studies have found a correlation between residential environments and travel convenience, there is little quantitative evidence to explain which factors influence the connectivity of road networks. This paper aims to clarify the indicators of built environments in Chinese residential areas that affect travel accessibility and their correlation proportions through a more quantitative method and to provide a basis for planning and design decisions. To this end, this study took 204 residential areas in the three districts of Nanjing, China as a sample and calculated 16 built-environment indicators. The path distance (D) and pedestrian route directness (PRD) from the center of these residential areas to the nearest urban intersection were measured by GIS as outcome variables. First, correlation analysis was used to screen for significant variables, and multiple linear regression models were used to examine the significant influencing factors of D and PRD. Then, a binary logistic regression analysis was performed to provide a model that could determine whether the built environment of the residential area met the requirements for convenient travel. The results revealed that the length of the long side, the area size, and the total road length of the residential area were significantly related to D, and the number of entrances and exits, the intersection density, and the X ratio were significantly related to PRD. The indicators that were positively correlated with D were the size, network complexity, and the boundary shape of the residential area according to the degree of correlation. Meanwhile, other indicators such as the density and connectivity of the residential road network were negatively correlated with D.

Research on 3D Path Planning of Quadrotor Based on Improved A* Algorithm

Considering the complexity of the three-dimensional environment and the flexibility of the quadrotor aircraft, using the traditional A* algorithm for global path planning has the disadvantages of less search direction, more expanded nodes, and a longer planning path. Therefore, an improved A* algorithm is proposed, which is improved from two aspects. Firstly, a two-layer extended neighborhood strategy is proposed, which can increase the search direction and make better use of the flexibility of the aircraft. Secondly, the heuristic function is improved to make the heuristic function value closer to the actual planning path distance, which can reduce the expansion nodes and optimize the planning path. Finally, the path planning simulation of the improved A* algorithm is carried out and the results show that the path planned by the improved algorithm is shorter and the expanded nodes are fewer, which can guide the quadrotor to reach the destination better.

Effects of temperature gradient and beam path height from heat source on phase structure and mutual coherence functions of a light beam propagating through convective air turbulence

In this paper, we investigate the effects of different values of temperature gradient (TG) and the distance of a plane wave path from a flat heat source generating an anisotropic and non-Kolmogorov convective air turbulence medium on the phase structure function (PSF) and mutual coherence function (MCF) of the light beam propagating through the medium. The non-Kolmogorov and anisotropic convective air turbulence is created using an electrical heater with a surface size of 50 cm × 100 cm. The heater temperature is changeable from room temperature to 100 °C. The laser beam with a diameter of 20 cm passes through the turbulent medium at different distances from the heater surface. At the end of the path, the wavefront of the light beam is reconstructed using a two-channel moiré wavefront sensor. Then, the PSF and MCF are calculated, and the effects of different TGs and the different beam path heights from the heater are studied. Results show that with increasing the TG and decreasing the beam path distance from the heat source, the turbulence intensity and PSF increase, while the MCF decreases.

Levenshtein graphs: Resolvability, automorphisms & determining sets

We introduce the notion of Levenshtein graphs, an analog to Hamming graphs but using the edit distance instead of the Hamming distance; in particular, vertices in Levenshtein graphs may be strings (i.e., words or sequences of characters in a reference alphabet) of possibly different lengths. We study various properties of these graphs, including a necessary and sufficient condition for their shortest path distance to be identical to the edit distance, and characterize their automorphism group and determining number. We also bound the metric dimension (i.e. minimum resolving set size) of Levenshtein graphs. Regarding the latter, recall that a run is a string composed of identical characters. We construct a resolving set of two-run strings and an algorithm that computes the edit distance between a string of length k and any single-run or two-run string in O(k) operations.

Quantum evolution speed induced by Hamiltonian

In the issue of quantum evolution, quantum evolution speed is usually quantified by the time rate of change of state distance between the initial sate and its time evolution. In this paper, the path distance of quantum evolution is introduced to study the evolution of a quantum system, through the approach combined with basic theory of quantum evolution and the linear algebra. In a quantum unitary system, the quantum evolution operator contains the path information of the quantum evolution, where the path distance is determined by the principal argument of the eigenvalues of the unitary operator. Accordingly, the instantaneous quantum evolution speed is proportional to the distance between the maximum and minimum eigenvalues of the Hamiltonian. As one of the applications, the path distance and the instantaneous quantum evolution speed could be used to form a new lower bound of the real evolution time, which depends on the evolution operator and Hamiltonian, and is independent of the initial state. It is found that the lower bound presented here is exactly equal to the real evolution time in the range &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \left[ {0, {\pi }/({{2{\omega _{\rm{H}}}}}}) \right]$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="22-20231009_M2.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="22-20231009_M2.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;. The tool of path distance and instantaneous quantum evolution speed introduced here provides new method for the related researches.

On the Characterization of Eco-Friendly Paths for Regional Networks

Macroscopic traffic models represent a promising tool to design strategies for ecological routing. To benefit from this tool, we must first characterize the relationship between path emissions and distance traveled or travel time on aggregated networks, i.e., a regional network. This paper investigates this relationship between two toy networks and a real urban network representing the city of Innsbruck (Austria). We utilize an accumulation-based model based on the Macroscopic Fundamental Diagram to mimic the traffic dynamics in the network and utilize the COPERT IV model to estimate the travel emissions, focusing on the carbon dioxide CO2. We show that there is a linear relationship between the total emissions of CO2 and the average travel time of internal paths, i.e. paths that take place completely within a single region. We also show that in some cases, there is a linear relationship between the total emissions and the average travel distance or travel time of paths that cross multiple regions in the network. However, the latter is not always true as traffic dynamics play an important role in path emissions. In other words, eco-friendly paths on regional networks do not necessarily follow the shortest paths in terms of distance or time.

A Highly Energy-Efficient Body-Coupled Transceiver Employing a Power-on-Demand Amplifier.

Wearable body sensor nodes require massive data transmission under limited energy. However, it suffers from drastically varying channel loss, which limits its energy efficiency in practical scenarios. This paper presents a power-driven body-channel transceiver (TRX), whose power consumption can be adaptively tuned against varying channel loss. An out-band programmable gain amplifier (PGA) is proposed to save power and generate a quasi-linear correlation between PGA gain and power. By using the quasi-linear gain-power relationship, we propose an auto gain/power control technique to realize on-demand power consumption. In addition, a differential balanced transmitter is designed to eliminate base-band harmonics in on-off keying modulation and increase the power delivered by the transmitter (TX). The TX and receiver (RX) of the prototype were integrated into 1 chip and fabricated in a 55-nm complementary metal oxide semiconductor process. During the measurement, 2 chips were configured as TX and RX, respectively. Both the TX and the RX were wearable, powered by lithium batteries, and attached to the subject's hands. The prototype achieved a 5.25-Mbps data rate with 16-pJ/bit energy efficiency at a 1.5-m straight-line ground path distance. Furthermore, the proposed TRX maintained stable communication within a 1.5-m distance, while dynamically reducing power consumption.

Modelling the nesting-habitat of threatened vulture species in the caucasus: An ecosystem approach to formalising environmental factors in species distribution models

Abiotic factors play an important role in species localisation, but biotic and anthropogenic predictors must also be considered in distribution modelling for models to be biologically meaningful. In this study, we formalised the biotic predictors of nesting sites for four threatened Caucasian vultures by including species distribution models (wild ungulates, nesting tree species) as biotic layers in the vulture Maxent models. Maxent was applied in the R dismo package and the best set of the model parameters were defined in the R ENMeval package. Performance metrics were continuous Boyce index, Akaike's information criterion, the area under receiver operating curve and true skill statistics. We also calculated and evaluated the null models. Kernel density estimation method was applied to assess the overlap of vulture ecological niches in the environmental space. The accessibility of anthropogenic food resources was estimated using the Path Distance measure that considers elevation gradient. The availability of pine forests (Scots Pine) and wild ungulates (Alpine Chamois and Caucasian Goat) contributed the most (29.6% and 34.3%) to Cinereous Vulture (Aegypius monachus) nesting site model. Wild ungulate distribution also contributed significantly (about 46%) to the Bearded Vulture (Gypaetus barbatus) model. This scavenger nests in the highlands of the Caucasus at a minimum distance of 5–10 ​km from anthropogenic facilities. In contrast, livestock as a food source was most important in colony distribution of Griffon Vulture (Gyps fulvus). The contribution of distances to settlements and agricultural facilities to the model was 45%. The optimal distance from Egyptian Vulture (Neophron percnopterus) nesting sites to settlements was only 3–10 ​km, to livestock facilities no more than 15 ​km with the factor contribution of about 57%. Excluding the wild ungulate availability, the ecological niches of studied vultures overlapped significantly. Despite similar foraging and nesting requirements, Caucasian vultures are not pronounced nesting and trophic competitors due to the abundance of nesting sites, anthropogenic food sources and successful niche sharing.

Covariate construction of nonconvex windows for spatial point patterns

In some standard applications of spatial point pattern analysis, window selection for spatial point pattern data is complex. Often, the point pattern window is given a priori. Otherwise, the region is chosen using some objective means reflecting a view that the window may be representative of a larger region. The typical approaches used are the smallest rectangular bounding window and convex windows. The chosen window should however cover the true domain of the point process since it defines the domain for point pattern analysis and supports estimation and inference. Choosing too large a window results in spurious estimation and inference in regions of the window where points cannot occur. We propose a new algorithm for selecting the point pattern domain based on spatial covariate information and without the restriction of convexity, allowing for better estimation of the true domain. Amodified kernel smoothed intensity estimate that uses the Euclidean shortest path distance is proposed as validation of the algorithm. The proposed algorithm is applied in the setting of rural villages in Tanzania. As a spatial covariate, remotely sensed elevation data is used. The algorithm is able to detect and filter out high relief areas and steep slopes; observed characteristics that make the occurrence of a household in these regions improbable. Keywords: Covariate, Euclidean shortest path, Nonconvex, Spatial point pattern, Window selection

Deviation Correction Path Planning Method of Full‐Width Horizontal Axis Roadheader Based on Improved Particle Swarm Optimization Algorithm

Aiming at the problem of optimal path planning for deviation correction of full‐width horizontal axis roadheader in the confined roadway of the coal mine, a deviation correction path planning method of full‐width horizontal axis roadheader based on improved particle swarm optimization (I‐PSO) algorithm is proposed. First, according to the driving characteristics of roadheader and deviation correction strategy, the mathematical model of full‐width horizontal axis roadheader is established, and the constraints based on pose analysis are determined. Then, taking the shortest total distance from the initial position to the terminal position of roadheader as the objective function of path planning, an optimization model of deviation correction path planning is established. Finally, the I‐PSO algorithm is proposed to quickly and accurately solve the deviation correction path model, in order to plan a safe and shorter distance path of full‐width horizontal axis roadheader. In the I‐PSO algorithm, the fitness difference of particles is used to dynamically adjust the inertia weight parameters to optimize the exploration of the whole or part of activity space, and the learning factor is adjusted to speed up particle convergence and optimization of particles, and a random factor is introduced to update the position of particles to avoid local traps as much as possible. The performance test of the I‐PSO algorithm shows that the I‐PSO algorithm is superior to other algorithms. The EJM340/4‐2 full‐width horizontal axis roadheader is used as the simulation object to verify the effectiveness of the I‐PSO algorithm in the deviation correction path planning of roadheader. The simulation results show that the I‐PSO algorithm proposed can quickly and accurately plan a safe and shorter distance path than the WPSO algorithm and CPSO algorithm and has better convergence and stability. It has laid a good foundation for the intelligent navigation of full‐width horizontal axis roadheader in coal mine.

Testing Complete Spatial Randomness on Linear Networks: Leon County Traffic Accident Example

A relatively new sub-area within this is the statistical analysis of point processes on linear networks, that is, processes of events occurring randomly in space but with locations constrained to lie on a linear network. For example, traffic accidents occur at random locations constrained to lie on a network of streets. In this case, the network is idealized as a network of line segments in the plane or three-dimensional space. The development of statistical techniques for the analysis of point processes on linear networks is still in its infancy. Many standard statistical techniques for analyzing point processes cannot be directly applied to data arising from linear networks and require suitable modification. Test of Complete Spatial Randomness (CSR) for point processes on the plane based on quadrat counts or nearest neighbors cannot be applied to point processes on linear networks. This paper defines a Voronoi tessellation of the linear network which uses the shortest path distance along the network instead of Euclidean distance, and then develops a chi-square test of CSR for linear networks based on the event counts in the tiles of this tessellation. This test is applied to data on traffic accidents in Leon County, Florida, USA.

A Ferroelectric Memristor-Based Transient Chaotic Neural Network for Solving Combinatorial Optimization Problems

A transient chaotic neural network (TCNN) is particularly useful for solving combinatorial optimization problems, and its hardware implementation based on memristors has attracted great attention recently. Although previously used filamentary memristors could provide the desired nonlinearity for implementing the annealing function of a TCNN, the controllability of filamentary switching still remains relatively poor, thus limiting the performance of a memristor-based TCNN. Here, we propose to use ferroelectric memristor to implement the annealing function of a TCNN. In the ferroelectric memristor, the conductance can be tuned by switching the lattice non-centrosymmetry-induced polarization, which is a nonlinear switching mechanism with high controllability. We first establish a ferroelectric memristor model based on a ferroelectric tunnel junction (FTJ), which exhibits the polarization-modulated tunnel conductance and the nucleation-limited-switching (NLS) behavior. Then, the conductance of the ferroelectric memristor is used as the self-feedback connection weight that can be dynamically adjusted. Based on this, a ferroelectric memristor-based transient chaotic neural network (FM-TCNN) is further constructed and applied to solve the traveling salesman problem (TSP). In 1000 runs for 10-city TSP, the FM-TCNN achieves a shorter average path distance, a 32.8% faster convergence speed, and a 2.44% higher global optimal rate than the TCNN.

Habitat suitability and connectivity modeling predict genetic population structure and priority control areas for invasive nutria (Myocastor coypus) in a temperate river basin.

The nutria (Myocastor coypus), also known as the coypu, is a semi-aquatic, invasive rodent native to South America that causes damage to natural riverine and wetland habitats in many parts of the world, including South Korea. Understanding habitat use, connectivity, and gene flow of nutria populations is critical for the sound management of local and regional ecosystems. Here, we assessed habitat suitability and connectivity in relation to the genetic structure of nutria populations in the Nakdong River Basin of South Korea. A total of 321 nutria occurrence sites and seven environmental variables were used to perform ensemble habitat suitability modeling using five species distribution models (SDMs), including boosted regression trees, maximum entropy model, random forest, generalized linear model, and multivariate adaptive regression splines. Using graph and circuit theory approaches, we assessed the population gene flow and current flow betweenness centrality (CFBC) of suitable habitats derived from the ensemble SDM. All SDMs performed well with a range of test AUC values from 0.962 to 0.970 (mean = 0.966) with true skill statistic values over 0.8. The minimum temperature of the coldest month, mean temperature of the warmest quarter, precipitation of the driest quarter, and distance from water bodies were important predictors in nutria habitat modeling. Nutria population gene flow was significantly correlated with the least-cost path distance on a cost resistance surface based on ensemble habitat suitability modeling and roads (Mantel's r = 0.60, p < 0.05). Finally, the CFBC positively correlated with the genetic diversity of nutria populations was used to identify priority control areas. Habitat suitability and connectivity modeling not only revealed environmental conditions and areas that support the survival and spread of nutrias, but also improved our understanding of the animals' genetic population structure, thereby indicating priority areas to target for eradication.