Static Environment Research Articles

Mathematical Model of Phytoplankton Interspecific Competition for Food Resource

The phytoplankton in an aquatic ecosystem is the basis of its life activity and the main producing link. The functioning of phytoplankton in same time depends on environmental factors: mineral nutrition, photosynthetically active solar radiation, water temperature and other less significant ones. Sunlight is a stable factor, varying predictably over time and space. The water temperature is the small regulatory factor. Concentrations of mineral substances can change quite quickly and significantly, this much influences on plant organisms. Thus, mineral nutrition is a basic environmental factor of influence to phytoplankton. On the other hand, in large aquatic basin such as seas and oceanic areas the distribution of living organisms is very heterogeneous in space. These two aspects – nutrient and spatial heterogeneity – are the focus of this article. A model of competitive interaction is considered using the example of two species of phytoplankton. The phytoplankton move passively in water what is simulated by the diffusion process. The model contains one non-trivial stationary and spatially homogeneous equilibrium and two trivial ones, i.e. degenerate in at least one species of phytoplankton. Trivial equilibria are stable only in some “degenerate” situations. The non-trivial equilibrium in “normal” conditions is stable to temporal and spatial disturbances. The behavior of solutions near a nontrivial equilibrium for a stationary living environment and in cases of its nonstationary is studied. Perturbation of a nontrivial equilibrium in a stationary environment leads to relatively long-term deviations from equilibrium and a slow return to it. The instability of trivial equilibria increases the spatial heterogeneity of solutions. At the same time, the nontrivial equilibrium computationally demonstrates weak properties of global stability in time. The unsteadiness of the environment is simulated by the unsteadiness of the influx of nutrients. It has been shown that the distribution of nutrients can lead to significant heterogeneity in the distribution of individuals across the spatial habitat.

ENHANCED HYBRID PATH PLANNING ALGORITHM BASED ON APF AND A-STAR

Abstract. The use of robots had increased widely in various fields, such as air transport systems, search and rescue, and agriculture, necessitating the need for path planning and obstacle avoidance systems to ensure safe and autonomous navigation toward the intended goal. Many Path-planning techniques are used to guide the mobile robot toward its goal with an optimized path and time. Among these techniques, the Artificial Potential Field (APF) algorithm stands out as one of the effective approaches, capable of operating in both static and dynamic environments to achieve optimal path planning. The APF is built by fusing two forces that attract the robot toward a goal location and repulsive forces that repel the robot away from the obstacle. Despite its effectiveness, the APF algorithm faces a major challenge, which is falling into a local-minimum issue. This paper presents a hybrid approach that combines the modified APF algorithm global optimization capabilities with the A-Star path-planning technique's real-time adaptability to overcome traditional APF local minimum issues. The transition back and forth between the two algorithms where carried out by a manager that can determine the adequate algorithm to be used instantaneously. Several experiments are presented to demonstrate the hybrid algorithm's effectiveness in various environments. The results show that the enhanced APF reach an optimal path to goal 50% faster compared to A-star, and managed to get out of local minimum compared to traditional APF and find a path shorter than A-star.

Leveraging national forestry data repositories to advocate wildfire modeling towards simulation-driven risk assessment

Modeling wildfire dynamics is complex and challenging due to the multiple scales involved in fire propagation, from physical–chemical processes to the interaction with topography and meteorological conditions. To provide reliable indicators of the risk of an ongoing wildfire, models aimed at informing policy-making should quantify the primary sources of uncertainty in their predictions. In this paper, we introduce a novel methodology built on top of Cellular Automata to assess the impact of uncertainty by implementing wildfire ensemble modeling using data from the Spanish National Forestry Data Repositories. Uncertainty is embedded in the model considering the ±2σ deviations from the medians of linear regressions of the canopy stratum with LiDAR metrics as explainable variables. The relevance of dynamic meteorological conditions in contrast to static environment conditions is analyzed. Our results suggest that an accurate account of the fuel model, including time-dependent wind and moisture maps, is mandatory to provide reliable predictions. Using a real case study (Concentaina’s extreme wildfire), we also illustrate the importance of assessing the impact of the firefighters’ mitigation efforts.

Robust RGB: D-SLAM in highly dynamic environments based on probability observations and clustering optimization

Visual simultaneous localization and mapping (SLAM) is the underlying support of unmanned systems. Currently, most visual SLAM methods are based on the static environment assumption so that dynamic objects in the camera’s field of view will seriously disrupt its working performance. In view of this, an RGB-D SLAM approach based on probability observations and clustering optimization for highly dynamic environments is proposed, which can effectively eliminate the influence of dynamic objects and accurately estimate the ego-motion of an RGB-D camera. The method contains a dual static map point detection strategy, which is carried out simultaneously in the current and previous frames. First, to enhance tracking robustness in highly dynamic environments, the probabilities of map points being static, calculated by both reprojection deviation and intensity deviation, are used to weight the cost function for pose estimation. Meanwhile, by taking the previous frames as a reference, a static velocity probability based on sparse scene flow is acquired to preliminarily recognize static map points and further improve the tracking accuracy. Then, an improved map point optimization strategy based on K-means clustering is designed, which effectively takes advantage of the clustering algorithm to refine the static map point labels while alleviating its stubborn problem. Finally, the experimental results on the TUM dataset and real scenes compared with the state-of-the-art visual SLAM methods illustrate that the proposed method achieves an extremely robust and accurate performance for estimating camera pose in highly dynamic environments.

An Improved Sampling Dijkstra Approach for Robot Navigation and Path Planning

The task of path planning is extremely investigated in mobile robotics to determine a suitable path for the robot from the source point to the target point. The intended path should satisfy purposes such as collision-free, shortest-path, or power-saving. In the case of a mobile robot, many constraints should be considered during the selection of path planning algorithms such as static or dynamic environment and holonomic or non-holonomic robot. There is a pool of path-planning algorithms in the literature. However, Dijkstra is still one of the effective algorithms due to its simplicity and capabilities to compute single-source shortest-path to every position in the workspace. Researchers propose several versions of the Dijkstra algorithm, especially in mobile robotics. In this paper, we propose an improved approach based on the Dijkstra algorithm with a simple sampling method to sample the workspace to avoid an exhaustive search of the Dijkstra algorithm which consumes time and resources. The goal is to identify the same optimal shortest path resulting from the Dijkstra algorithm with minimum time and number of turns i.e., a smoothed path. The simulation results show that the proposed method improves the Dijkstra algorithm with respect to the running time and the number of turns of the mobile robot and outperforms the RRT algorithm concerning the path length.

Multi-agent policy learning-based path planning for autonomous mobile robots

The study addresses path planning problems for autonomous mobile robots (AMRs), considering their kinematics, where performance and responsiveness are often incompatible. This study proposes a multi-agent policy learning-based method to tackle this challenge in dynamic environments. The proposed method features a centralized learning and decentralized execution-based path planning framework designed to meet performance and responsiveness requirements. The problem is modeled as a partial observation Markov Decision Process for policy learning while considering the kinematics using conventional neural networks. Then, an improved proximal policy optimization algorithm is developed with highlight experience replay that corrects failed experiences to speed up the learning processes. The experimental results show that the proposed method outperforms the baselines in both static and dynamic environments. The proposed method shortens the movement distance and time in static environments by about 29.1% and 5.7%, as well as in dynamic environments by about 21.1% and 20.4%, respectively. The runtime is maintained in milliseconds across various environments, taking only 0.07 s. Overall, the proposed method is valid and efficient in ensuring the performance and responsiveness of AMRs when dealing with complex and dynamic path planning problems.

Mobile robot’s path-planning and path-tracking in static and dynamic environments: Dynamic programming approach

One of the main issues in robotics systems is planning and tracking a safe path in diverse environments. This paper addresses an optimal methodology for generating the desired path and thereafter forces the mobile robot to follow the designed reference path. The proposed technique has the potential to tackle the inherent challenges and intricacies of the environment, enabling the robot to navigate both static and dynamic workspaces. Several simulations are exploited to verify the captured theoretical results. Three cases were examined in a static environment, achieving average performance metrics for reference signals in the X- and Y-directions, and heading tracking of 99.62%, 99.64%, and 95.08%, respectively. For the dynamic environment, two cases were studied, with average performance in following the Y-direction and heading angle recorded as 97.58% and 86.79%, respectively. Simulation results show that the controller calculated the optimal signal with an average computational time of 7 ms per iteration for static environments and 21.3 ms per iteration for dynamic environments.

RIS-Aided Physical Layer Key Generation of IoT Network in Static Environments (March 2023)

RIS-Aided Physical Layer Key Generation of IoT Network in Static Environments (March 2023)

Coexistence of Competing Microbial Strains under Twofold Environmental Variability and Demographic Fluctuations

Microbial populations generally evolve in volatile environments, under conditions fluctuating between harsh and mild, e.g. as the result of sudden changes in toxin concentration or nutrient abundance. Environmental variability (EV) thus shapes the long-time population dynamics, notably by influencing the ability of different strains of microorganisms to coexist. Inspired by the evolution of antimicrobial resistance, we study the dynamics of a community consisting of two competing strains subject to twofold EV. The level of toxin varies in time, favouring the growth of one strain under low drug concentration and the other strain when the toxin level is high. We also model time-changing resource abundance by a randomly switching carrying capacity that drives the fluctuating size of the community. While one strain dominates in a static environment, we show that species coexistence is possible in the presence of EV. By computational and analytical means, we determine the environmental conditions under which long-lived coexistence is possible and when it is almost certain. Notably, we study the circumstances under which environmental and demographic fluctuations promote, or hinder, the strains coexistence. We also determine how the make-up of the coexistence phase and the average abundance of each strain depend on the EV.

A Data Stream Ensemble Assisted Multifactorial Evolutionary Algorithm for Offline Data-Driven Dynamic Optimization.

Existing work on offline data-driven optimization mainly focuses on problems in static environments, and little attention has been paid to problems in dynamic environments. Offline data-driven optimization in dynamic environments is a challenging problem because the distribution of collected data varies over time, requiring surrogate models and optimal solutions tracking with time. This paper proposes a knowledge-transfer-based data-driven optimization algorithm to address these issues. First, an ensemble learning method is adopted to train surrogate models to leverage the knowledge of data in historical environments as well as adapt to new environments. Specifically, given data in a new environment, a model is constructed with the new data, and the preserved models of historical environments are further trained with the new data. Then, these models are considered to be base learners and combined as an ensemble surrogate model. After that, all base learners and the ensemble surrogate model are simultaneously optimized in a multitask environment for finding optimal solutions for real fitness functions. In this way, the optimization tasks in the previous environments can be used to accelerate the tracking of the optimum in the current environment. Since the ensemble model is the most accurate surrogate, we assign more individuals to the ensemble surrogate than its base learners. Empirical results on six dynamic optimization benchmark problems demonstrate the effectiveness of the proposed algorithm compared with four state-of-the-art offline data-driven optimization algorithms. Code is available at https://github.com/Peacefulyang/DSE_MFS.git.

Experimental Research on the Performance Characteristics of Grouting Slurry in a High-Ground-Temperature Environment

Grouting materials with good thermal insulation and reinforcement properties are the key factors in solving the temperature control problems of high geothermal tunnels using curtain grouting, as the existing grouting materials are unable to take into account the working performance and thermal insulation properties of high-temperature environments. In view of the above problems, this paper configures a high geothermal tunnel red-mud-based grouting material (RMGS) using red mud, carries out tests on the working performance (viscosity, setting time, and compressive strength) and thermal insulation performance (thermal conductivity and specific heat capacity) of the grouting materials at different temperatures (20, 40, 60, and 80 °C), and analyses the variation rules and micro-mechanisms of the various properties at different temperatures. The results show that the increase in temperature will accelerate the viscosity development and condensation of the grouting material and will also lead to the acceleration of the attenuation of the thermal conductivity of the three types of grouting material and the reduction in specific heat capacity. In addition, the appropriate temperature can improve the compressive strength of the material. The increase in temperature will accelerate the hydration reaction speed of the grouting material and will also lead to the development of the internal pore space of the material, which affects the macroscopic properties of the material and is the reason for the effect of the temperature on the performance of the grouting material. In terms of application, the cement slurry is suitable for grouting in a static water environment, the cement–water glass bi-liquid slurry is suitable for grouting in a dynamic water environment, and the RMGS is suitable for grouting in a high-ground-temperature environment.

The use of plant extracts and bacteriophages as an alternative therapy approach in combatting bacterial infections: the study of lytic phages and Stevia rebaudiana.

In the light of the problem of antibiotic resistance, the use of combined alternative therapies in combatting bacteria-related disorders has gained popularity. Bacteriophages are one element implemented in new combination therapy. Stevia rebaudiana is known to have antimicrobial activity and regarded as potentially having a synergistic effect with bacteriophages. Therefore, possible interactions of lytic bacteriophages (MS2, T4 and Phi6) with acetone and methanol S. rebaudiana extracts (SRa and SRm) in the bacterial environment were examined. The interactions were tested using a microdilution method, phage-extract co-incubation assay, static interaction (synography) and dynamic growth profile experiments in a bioreactor. The interactions of the tested factors in a static environment differed from those in a dynamic environment. Dynamic conditions altered the effect of the extracts in a concentration-dependent manner. How different the effect of the SRa extract was to that of the SRm extract on bacterial growth in a dynamic environment depended on the species of the phage and bacterial host. The greatest differences were observed for E. coli strains and their phages, whereas Pseudomonas syringae and the Phi6 phage reacted very similarly to both extracts. Differences also emerged for the same extract in different E. coli strains and their phages. Every extract type should be tested on a case-by-case basis and experiment outcomes should not be generalised before gathering data. Moreover, many varied experiments should be performed, especially when examining such multifactorial mixtures. The tested mixtures could potentially be used in multidrug-resistant bacterial infection treatments.

Effect of Mechanical Stimuli on the Phenotypic Plasticity of Induced Pluripotent Stem-Cell-Derived Vascular Smooth Muscle Cells in a 3D Hydrogel.

Introduction: Vascular smooth muscle cells (VSMCs) play a pivotal role in vascular homeostasis, with dysregulation leading to vascular complications. Human-induced pluripotent stem-cell (hiPSC)-derived VSMCs offer prospects for personalized disease modeling and regenerative strategies. Current research lacks comparative studies on the impact of three-dimensional (3D) substrate properties under cyclic strain on phenotypic adaptation in hiPSC-derived VSMCs. Here, we aim to investigate the impact of intrinsic substrate properties, such as the hydrogel's elastic modulus and cross-linking density in a 3D static and dynamic environment, on the phenotypical adaptation of human mural cells derived from hiPSC-derived organoids (ODMCs), compared to aortic VSMCs. Methods and results: ODMCs were cultured in two-dimensional (2D) conditions with synthetic or contractile differentiation medium or in 3D Gelatin Methacryloyl (GelMa) substrates with varying degrees of functionalization and percentages to modulate Young's modulus and cross-linking density. Cells in 3D substrates were exposed to cyclic, unidirectional strain. Phenotype characterization was conducted using specific markers through immunofluorescence and gene expression analysis. Under static 2D culture, ODMCs derived from hiPSCs exhibited a VSMC phenotype, expressing key mural markers, and demonstrated a level of phenotypic plasticity similar to primary human VSMCs. In static 3D culture, a substrate with a higher Young's modulus and cross-linking density promoted a contractile phenotype in ODMCs and VSMCs. Dynamic stimulation in the 3D substrate promoted a switch toward a contractile phenotype in both cell types. Conclusion: Our study demonstrates phenotypic plasticity of human ODMCs in response to 2D biological and 3D mechanical stimuli that equals that of primary human VSMCs. These findings may contribute to the advancement of tailored approaches for vascular disease modeling and regenerative strategies.

Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review

Wheeled mobile robots (WMRs) have been a focus of research for several decades, particularly concerning navigation strategies in static and dynamic environments. This review article carefully examines the extensive academic efforts spanning several decades addressing navigational complexities in the context of WMR route analysis. Several approaches have been explored by various researchers, with a notable emphasis on the inclusion of stability and intelligent capabilities in WMR controllers attracting the attention of the academic community. This study traces historical and contemporary WMR research, including the establishment of kinetic stability and the construction of intelligent WMR controllers. WMRs have gained prominence in various applications, with precise navigation and efficient control forming the basic prerequisites for their effective performance. The review presents a comprehensive overview of stability analysis and navigation techniques tailored for WMRs. Initially, the exposition covers the basic principles of WMR dynamics and kinematics, explaining the different wheel types and their associated constraints. Subsequently, various stability analysis approaches, such as Lyapunov stability analysis and passivation-based control, are discussed in depth in the context of WMRs. Starting an exploration of navigation techniques, the review highlights important aspects including path planning and obstacle avoidance, localization and mapping, and trajectory tracking. These techniques are carefully examined in both indoor and outdoor settings, revealing their benefits and limitations. Finally, the review ends with a comprehensive discussion of the current challenges and possible routes in the field of WMR. The discourse includes the fusion of advanced sensors and state-of-the-art control algorithms, the cultivation of more robust and reliable navigation strategies, and the continued exploration of novel WMR applications. This article also looks at the progress of mobile robotics during the previous three decades. Motion planning and path analysis techniques that work with single and multiple mobile robots have been discussed extensively. One common theme in this research is the use of soft computing methods to give mobile robot controllers cognitive behaviors, such as artificial neural networks (ANNs), fuzzy logic control (FLC), and genetic algorithms (GAs). Nevertheless, there is still a dearth of applications for mobile robot navigation that leverage nature-inspired algorithms, such as firefly and ant colony algorithms. Remarkably, most studies have focused on kinematics analysis, with a small number also addressing dynamics analysis.

A Semantics-Guided Visual Simultaneous Localization and Mapping with U-Net for Complex Dynamic Indoor Environments

Traditional simultaneous localization and mapping (SLAM) system tends to operate in small-area static environments, and its performance might degrade when moving objects appear in a highly dynamic environment. To address this issue, this paper proposes a dynamic object-aware visual SLAM algorithm specifically designed for dynamic indoor environments. The proposed method leverages a semantic segmentation architecture called U-Net, which is utilized in the tracking thread to detect potentially moving targets. The resulting output of semantic segmentation is tightly coupled with the geometric information extracted from the corresponding SLAM system, thus associating the feature points captured by images with the potentially moving targets. Finally, filtering out the moving feature points can greatly enhance localization accuracy in dynamic indoor environments. Quantitative and qualitative experiments were carried out on both the Technical University of Munich (TUM) public dataset and the real scenario dataset to verify the effectiveness and robustness of the proposed method. Results demonstrate that the semantics-guided approach significantly outperforms the ORB SLAM2 framework in dynamic indoor environments, which is crucial for improving the robustness and reliability of the SLAM system.

Dynamics-aware navigation among moving obstacles with application to ground and flying robots

We present a novel method for navigation of mobile robots in challenging dynamic environments. The method, which is based on Nonlinear Model Predictive Control (NMPC), hinges upon a specially devised constraint for dynamics-aware collision avoidance. In particular, the constraint builds on the notion of avoidable collision state, taking into account the robot actuation capabilities in addition to the robot–obstacle relative distance and velocity. The proposed approach is applied to both ground and flying robots and tested in a variety of static and dynamic environments. Comparative simulations with an NMPC using a purely distance-based collision avoidance constraint confirm the superiority of the dynamics-aware version, especially for high-speed navigation among moving obstacles. Moreover, the results indicate that the method can work with relatively short prediction horizons and is therefore amenable to real-time implementation.

Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations

The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people’s homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals’ obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people’s use of dynamic food environments and links to diet and health.

Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm

Unmanned surface vehicles (USVs) are experiencing significant development across various fields due to extensive research, enabling these devices to offer substantial benefits. One kind of research that has been developed to produce better USVs is path planning. Despite numerous research efforts employing conventional algorithms, deep reinforcement learning, and evolutionary algorithms, USV path planning research consistently faces the challenge of effectively addressing issues within dynamic surface environments where USVs navigate. This study aims to solve USV dynamic environmental problems, as well as convergence problems in evolutionary algorithms. This research proposes a neuronal genetic algorithm that utilizes neural network input for processing with a genetic operator. The modifications in this research were implemented by incorporating a partially exponential-based fitness function into the neuronal genetic algorithm. We also implemented an inverse time variable to the fitness function. These two modifications produce faster convergence. Based on the experimental results, which were compared to those of the basic neural-network-based genetic algorithms, the proposed method can produce faster convergent solutions for USV path planning with competitive performance for total distance and time traveled in both static and dynamic environments.

Research and Implementation of a Demodulation Switch Signal Phase Alignment System in Dynamic Environments.

In the space gravitational wave detection mission, inertial sensors play the role of providing an inertial reference for the laser interferometric measurement system. Among them, the capacitance sensor serves as the core key technology of the inertial sensor, used to measure the relative position of the test mass (TM) in the electrode cage. The capacitance sensor utilizes synchronous demodulation technology to extract signals from the AC induction signal. When the phase of the demodulation switch signal is aligned, the synchronous demodulator can most effectively filter out noise, thus directly influencing the performance of the capacitance sensor. However, since the TM is in a suspended state, the information read by the capacitance sensor is dynamic, which increases the difficulty of demodulation phase alignment. In light of this, a method is proposed for achieving the phase alignment of the demodulation switch signal in a dynamic environment. This is accomplished by adjusting the phase of the demodulation switch signal, and subsequently computing the phase difference between the AC induction signal and the demodulation switch signal. At the same time, a measurement and evaluation method for phase deviation is also proposed. Ultimately, an automatic phase alignment system for the demodulation switch signal in dynamic environments is successfully implemented on an FPGA platform, and tests are conducted on a hexapod PI console platform to simulate dynamic environments. The experimental results demonstrate that the system accurately achieves phase alignment in the static environment, with a phase deviation of 0.1394 rad. In the simulated dynamic environment, the phase deviation is 0.1395 rad.

Coupled environmental and demographic fluctuations shape the evolution of cooperative antimicrobial resistance.

There is a pressing need to better understand how microbial populations respond to antimicrobial drugs, and to find mechanisms to possibly eradicate antimicrobial-resistant cells. The inactivation of antimicrobials by resistant microbes can often be viewed as a cooperative behaviour leading to the coexistence of resistant and sensitive cells in large populations and static environments. This picture is, however, greatly altered by the fluctuations arising in volatile environments, in which microbial communities commonly evolve. Here, we study the eco-evolutionary dynamics of a population consisting of an antimicrobial-resistant strain and microbes sensitive to antimicrobial drugs in a time-fluctuating environment, modelled by a carrying capacity randomly switching between states of abundance and scarcity. We assume that antimicrobial resistance (AMR) is a shared public good when the number of resistant cells exceeds a certain threshold. Eco-evolutionary dynamics is thus characterised by demographic noise (birth and death events) coupled to environmental fluctuations which can cause population bottlenecks. By combining analytical and computational means, we determine the environmental conditions for the long-lived coexistence and fixation of both strains, and characterise a fluctuation-driven AMR eradication mechanism, where resistant microbes experience bottlenecks leading to extinction. We also discuss the possible applications of our findings to laboratory-controlled experiments.