Monitoring Requirements Research Articles

Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model

Due to the influences of the storage environment, water content change, particle settlement, natural loss, and other factors, the distribution density of wheat and the volume of grain pile in the storage process are gradually changed so that the single weight calculation method cannot objectively evaluate the storage quantity of wheat and also causes difficulties to the regular inspection of the quantity of wheat stock. To meet the practical needs of wheat inventory monitoring, a wheat inventory monitoring method based on inventory measurement and the support vector machine regression (SVR) prediction model is proposed. By collecting the working papers for the physical inspection of wheat in grain warehouses in Shanxi province, Hebei province, Henan province, Jiangsu province, and other places, the storage time, storage weight, storage moisture content, measured moisture content, measured volume weight, measured net volume, and measured weight for inspection were selected as training samples for the SVR prediction model, and kernel function selection and parameter optimization were carried out. We developed an optimal prediction model for the amount of wheat in the grain depots. In the actual grain store measurement process, the net volume of wheat in the current grain store was obtained by a laser volumetric measuring apparatus, the actual bulk density of wheat was sampled, and the actual moisture content of wheat was measured by sampling. The three samples, their storage time, their storage moisture content, and their storage weight were fed into the trained SVR prediction model as new samples, and the predicted weight of the wheat in the current grain store was obtained from the output. The error rate calculation procedure was introduced to achieve an anomalous judgment error rate for grain depots. The experimental results showed that the SVR prediction model based on the linear kernel function had a very low mean squared error and high determination coefficient, and the average prediction accuracy of the grain stock error rate reached 93.2 percent, which can meet the requirements of wheat quantity monitoring in grain warehouses.

Coaxial Flexible Fiber-Shaped Triboelectric Nanogenerator Assisted by Deep Learning for Self-Powered Vibration Monitoring.

Self-powered vibration sensor is highly desired for distributed and continuous monitoring requirements of Industry 4.0. Herein, a flexible fiber-shaped triboelectric nanogenerator (F-TENG) with a coaxial core-shell structure is proposed for the vibration monitoring. The F-TENG exhibits higher adaptability to the complex surfaces, which has an outstanding application prospect due to vital compensation for the existing rigid sensors. Initially, the contact characteristics between the dielectric layers, that related to the perceiving performance of the TENG, are theoretically analyzed. Such a TENG with 1D structure endows high sensitivity, allowing for accurately responding to a wide range of vibration frequencies (0.1 to 100Hz). Even applying to the real diesel engine, the error in detecting the vibration frequencies is only 0.32% compared with the commercial vibration sensor, highlighting its potential in practical application. Further, assisted by deep learning, the recognition accuracy in monitoring nine operating conditions of the system achieves 97.87%. Overall, the newly designed F-TENG with the merits of high-adaptability, cost-efficiency, and self-powered, has offered a promising solution to fulfill an extensive range of vibration sensing applications in the future.

The application of multi factor method in the site selection of reserve warehouse for nuclear and radiation emergency

This article explains the power generation of civilian nuclear power plants and the development trend of nuclear technology utilization. The construction of regional nuclear and radiation emergency monitoring material reserve warehouse (hereinafter referred to as the “reserve warehouse”) can ensure nuclear and radiation safety. According to the requirements for emergency monitoring of nuclear accidents and radiation accidents, the article summarizes several factors for site selection. The article conducts research on candidate locations for reserve warehouse and emergency monitoring demand location in China (hereinafter referred to as the “demand locations”). The multi factor method was applied to the location selection of reserve warehouses, and the following conclusions were obtained: (1) The multi factor method is feasible. It has high computational accuracy and fast speed. The site selection model has a significant optimization effect on the location of reserve warehouses. (2) The site selection model proves that the location of reserve warehouses need to consider multiple factors. When the number of reserves is 3–8, about 1–2 cities which is the location of nuclear power plants are selected as candidate cities. (3) When the number of reserve warehouse is 8, the maximum distance is less than 2400 km, meeting the distance limit set for nuclear and radiation emergency monitoring. Considering comprehensive road conditions and other factors, China needs to establish at least 5 reserve warehouses to achieve an emergency ideal situation with a maximum distance of 1600 km.

Real-time Monitoring System for Driver Phone Usage Based on Improved YOLOv5s

In response to the impact of driver's violations, such as using a mobile phone, on vehicle safety during the driving process, we propose an improved real-time monitoring algorithm based on YOLOv5s with lightweight optimization. Firstly, we replace the C3 module (CSP Bottleneck with 3 convolutions) in the backbone network of YOLOv5s with a lightweight Ghost Module to reduce the model's parameter count, enhance detection speed, and maintain inference accuracy unaffected, thus meeting the requirements of real-time monitoring. Secondly, we introduce the RepConv (Receptive Field Block) module into the Feature Extraction Network (PANet) structure to increase the neural network's receptive field for input images and further reduce the model's computational load. Experimental results show that the improved network achieves an mAP@0.5 of 95.7%, a detection speed of 140 FPS, and a model size reduction to 10.6MB, meeting the demand for real-time and reliable detection on embedded devices.

A continuous leakage real-time localization method based on space phase image of elastic wave field with improved CNN

To satisfy the requirement of real-time monitoring and locating of the leakage in large pressure vessels, a method of leakage source location based on improved convolutional neural network (CNN) is proposed in this paper. In this method, the polar coordinate model of elastic wave propagation excited by continues leakage is established, and the improved CNN is constructed according to its spatial field distribution, and the training set of the network is obtained by finite element simulation. The experimental results show that this method can be applied to locate the leak at any time after it occurs, and only a single sensor array is used. In the experiment of 1 m2 stiffened test plate, the absolute distance error was less than 0.1 m and the absolute angle error was less than 0.1 rad in 90.2 % of all samples.

Condition monitoring for nuclear turbines with improved dynamic partial least squares and local information increment

Performing online condition monitoring for nuclear turbines in the rapidly changing environment is a challenging but imperative task to enhance the safety and reliability of nuclear power plants. Given the nonlinear and dynamic properties of nuclear turbine operation, this paper proposes an innovative method for condition monitoring. Specifically, the paper first redesigns time augmented matrices based on lagged data to reflect the process dynamics. Subsequently, a dynamic auto-regressive model, integrated with the variant of kernel partial least squares, is built between input and output variables, which represents auto-correlations and cross-correlations of operation data simultaneously. The prediction of the model serves as the baseline for the monitoring indicator. Additionally, since the operation process involves variable working excitation and random noise, making static control limits insufficient to satisfy the requirements of condition monitoring, the proposed method utilizes a novel monitoring indicator based on local information increment. The indicator comprehensively incorporates the prediction value and past measurement for monitoring statistics and control limits. Finally, the proposed method is applied to a real nuclear turbine operation process, and the results are compared with three other methods to demonstrate its superiority.

Cetacean and Sea Turtle Observations in the Remote Mid-Atlantic (NW) Ocean

Due to its remoteness, little is known about the occurrence and abundance of cetaceans and sea turtles in the pelagic mid-Atlantic Ocean. Data on cetacean and sea turtle occurrence and distribution were collected by dedicated biological observers to address U.S. monitoring and mitigation requirements associated with a vessel-based academic geophysical survey in international waters in the northwestern and mid-Atlantic Ocean from 14 June through 16 July 2018. A total of 6,949 km (503 h) of visual observations occurred while surveying north from Bermuda and ending in Woods Hole, Massachusetts. A total of 928 cetaceans representing at least 14 species and 15 sea turtles representing three species were observed. The most frequently observed cetacean species was the Atlantic white-sided dolphin (Lagenorhynchus acutus) (340 individuals; 37%) followed by the short-beaked common dolphin (Delphinus delphis) (286 individuals; 31%) and pilot whales (Globicephala spp.) (95 individuals; 10%). These sighting data also included an extralimital sighting of a sperm whale (Physeter macrocephalus) calf at 43.44° N latitude and 36.85° W longitude, and extralimital sightings of Atlantic white-sided dolphins below 38° N latitude. Loggerhead sea turtles (Caretta caretta) were seen most frequently (6 individuals). This study addresses a data gap in documented occurrence and lack of occurrence of cetaceans and sea turtles over a large pelagic area in the northwestern Atlantic Ocean during summer.

Generator condition monitoring method based on SAE and multi-source data fusion

With the increasing number of units involved in power system regulation and the increasing proportion of industrial load, a single data source has been unable to meet the accuracy requirements of online monitoring of unit conditions in the new power system. Based on the stacked autoencoder (SAE) network, combined with multi-source data fusion technology and adaptive threshold, a generator condition monitoring method is proposed. First, a SCADA–PMU data fusion method based on the weighted D–S evidence theory is proposed. Second, the auto-coding technology is introduced to build a stacked self-coding deep learning network model, extract the deep features of the training dataset, and build a generator fault detection model. Finally, by smoothing the reconstruction error and combining it with the trend change in the state monitoring quantity detected by the adaptive threshold, the fault judgment is realized. The simulation results show that, compared with the traditional method based on a single data source, the proposed method has higher robustness and accuracy, thus effectively improving the refinement level of generator condition monitoring.

SR-Unet: A Super-Resolution Algorithm for Ion Trap Mass Spectrometers Based on the Deep Neural Network.

The mass spectrometer is an important tool for modern chemical analysis and detection. Especially, the emergence of miniature mass spectrometers has provided new tools for field analysis and detection. The resolution of a mass spectrometer reflects the ability of the instrument to discriminate between adjacent mass-to-charge ratio ions, and the higher the resolution, the better the discrimination of complex mixtures. Quadrupole ion traps are generally considered as a low-resolution mass spectrometry method, but they have gained wide attention and development in recent years because of their suitability for miniaturization and high qualitative capability. For an ion trap mass spectrometer, the mass sensitivity and resolution can be mutually constrained and need to be balanced by setting an appropriate scanning speed. In this study, a super-resolution U-net algorithm (SR-Unet) is proposed for ion trap mass spectrometry, which can estimate the possible ions from the overlapping ion peaks of low-resolution spectra and improve the equivalent resolution while ensuring sufficient sensitivity and analysis speed of the instrument. By determining the mass spectra of a linear ion trap mass spectrometer (LTQ XL) in Turbo and Normal scan modes, the same unit mass resolution as that at a scan speed of 16,667 Da/s was successfully obtained at 125,000 Da/s. Also, the experiments demonstrated that the algorithm is capable of the mass-to-charge ratio and instrument migration. SR-Unet can be migrated and applied to a miniature mass spectrometer for cruise detection of volatile organic compounds (VOCs), and the identification of VOC species in Photochemical Assessment Monitoring Stations (PAMS) was improved from 31 to 50 species with the same monitoring and analysis speed requirement. Further, super-unit mass resolution peptide detection was achieved on a miniature mass spectrometer with the help of the SR-Unet algorithm, which reduced the full width at half-maxima (FWHM) of bradykinin divalent ions (m/z 531) from 0.35 to 0.15 Da at a scan speed of 375 Da/s and improved the equivalent resolution to 3540. The proposed method provides a new idea to enhance the field mixture detection capability of miniature ion trap mass spectrometers.

Development of an Interactive Tool to Support Shared Decision-Making in Rheumatoid Arthritis: Treatment Attribute Preference.

An interactive tool identifying treatment attributes important to patients can enhance shared decision-making (SDM) in rheumatoid arthritis (RA). A formative survey was conducted to identify the most important treatment attributes from patients' perspective, which can be used to develop an interactive SDM tool. The survey was performed in two phases: qualitative interviews and quantitative surveys. The qualitative interviews were conducted to inform the design of the quantitative survey. In qualitative interviews, patients with RA (n=10) and rheumatologists (n=10) were introduced to the SDM tool concept. Feedback on the design and scope of the SDM tool was used to develop a quantitative survey, conducted in a large sample size of patients. Patient preferences for treatment attributes (route of administration and dosing frequency, serious side effects, out-of-pocket costs, efficacy, and monitoring requirement) were assessed via adaptive conjoint exercise involving ranking of hypothetical RA treatment configurations. A total of 944 patients (males: 43%, females: 57%) with RA participated in the quantitative survey. Route of administration and dosing frequency (38%) followed by serious side effects (33%) were the two most important treatment attributes for individual patients. The recontact survey (n=172/944) estimated tool stability of 72% (n=124/172) in terms of the relative importance of treatment attributes. The findings of this survey could be used in the development of an SDM tool that can potentially provide insights into patient preferences and is generally well received by patients and rheumatologists with good agreement and reliability.

A Novel Individual Aircraft Life Monitoring Method Based on Reliable Life Consumption Assessment

Individual life monitoring is crucial for ensuring aircraft flight safety. Conventional life-consumption-based monitoring methods ignore reliability, thus disjoining them from the aircraft’s reliable life determination and extension, where high confidence and reliability are required. Therefore, this paper proposes a reliable life consumption and individual life monitoring method for aircraft structure fatigue. In the paper, the P-S-N curve, i.e., the relationship between the aircraft structure’s life (N) and fatigue load (S) under a certain probability (P), is established, by which the lower confidence limit of the aircraft structure’s reliable life can be evaluated under any fatigue loads. Based on that and the aircraft’s monitored fatigue loads, the indexes of reliable life consumption and remaining reliable life percentages are proposed and assessed in real time for individual aircraft life monitoring and online life management. Case studies indicate that the proposed method can guarantee high confidence and reliability requirements in individual life monitoring, consistent with the aircraft’s life determination and extension, which are widely accepted nowadays in engineering practice.

Application and Outlook of Remote Sensing Technology in Water Environment Monitoring

Water environment monitoring is a critical tool for effective environmental protection and management. However, traditional methods often fall short in meeting the requirements of large-scale and real-time monitoring. To overcome these limitations, remote sensing technology has emerged as a non-contact, rapid, and efficient approach, playing a pivotal role in water environment monitoring. This paper provides a comprehensive overview of the principles and methodologies employed in remote sensing technology for monitoring water quality pollutants, detecting changes in water bodies, and preserving wetland ecosystems in the context of water environment monitoring. The practical application and significance of remote sensing technology are exemplified through a range of specific case studies. Moreover, the paper analyzes the existing status and challenges of domestic water environment remote sensing monitoring, while also presenting future development directions. The research and application outlined in this paper serve as a valuable reference, driving innovation and progress in water environment remote sensing monitoring in our country. Additionally, the findings of this study are of great significance in improving the efficiency and effectiveness of water environmental protection and management, as well as promoting the wider application of remote sensing technology within the environmental domain.

Adaptive Neural Coordinated Control for Multiple Euler-Lagrange Systems With Periodic Event-Triggered Sampling.

This article addresses the event-triggered coordinated control problem for multiple Euler-Lagrange systems subject to parameter uncertainties and external disturbances. Based on the event-triggered technique, a distributed coordinated control scheme is first proposed, where the neural network-based estimation method is incorporated to compensate for parameter uncertainties. Then, an input-based continuous event-triggered (CET) mechanism is developed to schedule the triggering instants, which ensures that the control command is activated only when some specific events occur. After that, by analyzing the possible finite-time escape behavior of the triggering function, the real-time data sampling and event monitoring requirement in the CET strategy is tactfully ruled out, and the CET policy is further transformed into a periodic event-triggered (PET) one. In doing so, each agent only needs to monitor the triggering function at the preset periodic sampling instants, and accordingly, frequent control updating is further relieved. Besides, a parameter selection criterion is provided to specify the relationship between the control performance and the sampling period. Finally, a numerical example of attitude synchronization for multiple satellites is performed to show the effectiveness and superiority of the proposed coordinated control scheme.

The Effect of Udenafil on Heart Rate and Blood Pressure in Adolescents With the Fontan Circulation

The Fontan Udenafil Exercise Longitudinal (FUEL) trial showed that treatment with udenafil was associated with improved exercise performance at the ventilatory anaerobic threshold in children with Fontan physiology. However, it is not known how the initiation of phosphodiesterase 5 inhibitor therapy affects heart rate and blood pressure in this population. These data may help inform patient selection and monitoring after the initiation of udenafil therapy. The purpose of this study is to evaluate the effects of udenafil on vital signs in the cohort of patients enrolled in the FUEL trial. This international, multicenter, randomized, double-blind, placebo-controlled trial of udenafil included adolescents with single ventricle congenital heart disease who had undergone Fontan palliation. Changes in vital signs (heart rate [HR], systolic [SBP] and diastolic blood pressure [DBP]) were compared both to subject baseline and between the treatment and the placebo groups. Additional exploratory analyses were performed to evaluate changes in vital signs for prespecified subpopulations believed to be most sensitive to udenafil initiation. Baseline characteristics were similar between the treatment and placebo cohorts (n=200 for each). The groups demonstrated a decrease in HR, SBP, and DBP 2 hours after drug/placebo administration, except SBP in the placebo group. There was an increase in SBP from baseline to after 6-min walk test in the treatment and placebo groups, and the treatment group showed an increase in HR (87.4 ± 15.0 to 93.1 ± 19.4 beats/min, p <0.01) after exercise. When comparing changes from baseline to the 26-week study visit, small decreases in both SBP (-1.9 ± 12.3 mm Hg, p=0.03) and DBP (-3.0 ± 9.6 mm Hg, p <0.01) were seen in the treatment group. There were no clinically significant differences between treatment and placebo group in change in HR or blood pressure in the youngest age quartile, lightest weight quartile, or those on afterload-reducing agents. In conclusion, initiation of treatment with udenafil in patients with Fontan circulation was not associated with clinically significant changes in vital signs, implying that for patients similar to those enrolled in the FUEL trial, udenafil can be started without the requirement for additional monitoring after initial administration.

A systematic review of clozapine-associated inflammation and related monitoring.

Clozapine is an effective antipsychotic medication used for treatment-resistant schizophrenia. However, it is underutilized due to rigorous hematologic monitoring requirements and many adverse drug reactions. Publications have highlighted the occurrence of inflammatory reactions, some life-threatening, particularly during the early stages of clozapine treatment. Although guidelines have suggested monitoring for inflammatory processes during clozapine initiation, screening in clinical practice is not universal. This systematic review aimed to investigate the relationship between clozapine and inflammation and assess the importance of monitoring for inflammatory reactions. A comprehensive literature search yielded 6,915 unique publication records after removal of duplicates. After a rigorous screening process, 75 publications were included in the review, which focused on three main aspects: i) the impact of clozapine on inflammatory markers, ii) monitoring cardiac and other organ function during clozapine-associated inflammatory processes, and iii) monitoring non-specific signs and symptoms of inflammation. Elevated levels of C-reactive protein (CRP) and several proinflammatory cytokines have been observed in association with clozapine treatment. However, the practicality of measuring specific markers in clinical practice remains uncertain. Current evidence supports monitoring CRP levels during the first 4 to 8 weeks of treatment, especially to facilitate myocarditis screening. Further research is needed to establish clinically-relevant CRP thresholds for intervention. The implementation of monitoring protocols during the early phase of clozapine treatment may mitigate adverse reactions and allow for continued use of clozapine. Future studies should also explore the association between clozapine-associated inflammation and pneumonia, as well as investigate the impact of inflammation on clozapine metabolism to predict the need for dose adjustment. These endeavors may facilitate the development and implementation of evidence-based guidelines for the monitoring of clozapine-associated inflammation.

Application of SLAM in bolt tightening monitoring process based on intelligent data algorithm analysis

Abstract In this paper, firstly, according to the requirements of static monitoring of bolt tightening quality, the bolt tightening process can be divided into four stages: snug, elastic deformation, yielding and plastic deformation, and the SLAM technology is applied to bolt monitoring modeling and building engineering modeling. Next, the pre-tightening force for the bolt is measured using the waveform expansion method, and the bolt tightening index can be determined. Then, all the collected bolt data are randomly combined into two categories of loosening and tightening to form a training data set, and the improved intelligent data algorithm is used to train and predict them. The results show a linear correlation between tightening degree x |τ ɛ| and preload force change ΔF under 0.31kN and 0.22kN loading gradients, and the linear correlation coefficient of the fitted curve is greater than 0.9215. The results of the data analysis verify the correctness of the theoretical analysis.

Determination of neurotoxic shellfish poisoning toxins in shellfish by liquid chromatography-tandem mass spectrometry coupled with dispersive solid phase extraction

An innovative method based on dispersive solid phase extraction (d-SPE) in conjunction with LC-MS/MS had been developed for the simultaneous quantitative determination of three brevetoxins (BTXs), which can result in neurotoxic shellfish poisoning (NSP), in shellfish. The toxins were extracted with a 50 % acetonitrile (v/v) and cleaned by alumina-neutral sorbent. After chromatographic separation on a C18 column, the analytes were qualitatively and quantitatively detected using multiple reaction monitoring (MRM) in positive ionization mode. The created approach was validated by SANTE 11312/2021. The LOQs were 5 μg/kg for each toxin, below the advised regulatory limit of 800 μg BTX-2/kg. The mean recoveries of brevetoxins were in the range of 75.9 %–114.1 %, and the ranges of their intra- and inter-day precisions were 0.9–9.7 % and 0.6–7.2 %, respectively. The matrix effects for three BTXs in four shellfish matrices were in the range of 85.6 %–114.8 %. The method demonstrated great consistency and high sensitivity, and it can meet the requirements of daily monitoring.

Trajectory Planning Framework Combining Replanning and Hierarchical Planning

To enable autonomous vehicles to respond quickly to changes in the surrounding environment and to generate safe and stable trajectory, we propose a safety-based hierarchical trajectory planning method that divides the planning module into two parts: the planner and the replanning monitor. In the planner, we first propose a fast linear trajectory planning method that uses the mass point model instead of the vehicle model and linearizes the collision avoidance constraint using the Big-M method for linear programming (Big-M) to obtain a linear programming model. The complete vehicle kinematic model is then built in the nonlinear programming stage, the collision constraints and cost functions are refined, and the rough solution of the linear programming is brought into it to obtain the exact solution. The replanning monitor is divided into safety and comfort monitors. The safety monitor will always pay attention to the changes in the surrounding environment and calculate whether the vehicle is in danger of collision in the future period, while the comfort monitor is more concerned with the comfort of driving the vehicle; when the monitor requirements are not met, replanning will be carried out. By simulating the driving environment, the proposed algorithm can form a safe and comfortable trajectory, which verifies the rationality of the proposed method.

Optimal Network Design for Microseismic Monitoring in Urban Areas - A Case Study in Munich, Germany

Well-designed monitoring networks are crucial for obtaining precise locations, magnitudes and source parameters, both for natural and induced microearthqakes. The performance of a seismic network depends on many factors, including network geometry, signal-to-noise ratio (SNR) at the seismic station, instrumentation and sampling rate. Therefore, designing a high-quality monitoring network in an urban environment is challenging due to the high level of anthropogenic noise and dense building infrastructure, which can impose geometrical limitations and elevated construction costs for sensor siting. To address these challenges, we apply a numerical optimization approach to design a microseismic surveillance network for induced earthquakes in the metropolitan area of Munich (Germany), where several geothermal plants exploit a deep hydrothermal reservoir. First of all, we develop a detailed noise model for the city of Munich, to capture the heterogeneous noise conditions. Then, we calculate the expected location precision for a randomly chosen network geometry from the body-wave amplitudes and travel times of a synthetic earthquake catalog considering the modeled local noise level at each network station. In the next step, to find the optimum network configuration, we use a simulated annealing approach in order to minimize the error ellipsoid volume of the linearized earthquake location problem. The results indicate that a surface station network cannot reach the required location precision (0.5 km in epicentre and 2 km in source depth) and detection capability (magnitude of completeness Mc = 1.0) due to the city´s high seismic noise level. In order to reach this goal, borehole stations need to be added to increase the SNR of the microearthquake recordings, the accuracy of their body-wave arrival times and source parameters. The findings help to better quantify the seismic monitoring requirements for a save operation of deep geothermal projects in urban areas.

Spatial Ecology of an Arboreal Iguana (Oplurus cyclurus) in a Treeless Landscape.

Understanding the spatial ecology of species has important implications for conservation, as it helps identify suitable habitats and minimum requirements for biodiversity monitoring and management. The spiny-tailed lizard Oplurus cyclurus is a widespread endemic iguanid occurring in dry areas of southern and western Madagascar. While the species is known to be mostly arboreal, populations of the Isalo sandstone massif suggest local adaptation to a less forested savannah and a more exposed habitat. We radio-tracked 19 spiny-tailed lizards to investigate the species' rock-dwelling behaviour and spatial ecology at Isalo National Park. Tracked individuals showed high site and burrow fidelity, and a basking behaviour mostly tied to the accessibility of their burrow, the time of day, and their life stage. Activity peaked during the sunniest hours, while juveniles were more active than adults with unfavourable weather conditions. Despite high burrow fidelity, lizards used shelters non-exclusively, regularly changing (approx. once a week) with neighbouring burrows (average distance between burrows = 13.6 m). However, there was no obvious relation between lizards' body and/or tail size and the width and depth of selected burrows. Dynamic Brownian Bridge Movement Models estimated frequented areas over 247.8 m2 (95% isopleth), where territorial overlap is common. Our results challenge the notion that burrow-site fidelity is the sole driving factor behind space utilization in the studied population. We argue that the apparently unusual saxicolous habits imposed by habitat features (the absence of trees) may lead to local behavioural adjustments influencing antipredatory and foraging strategies, as well as intraspecific interactions.