Simulated Dynamic Environment Research Articles

Encapsulation of hydroxycamptothecin within porous and hollow poly(L-lactide-co-ε-caprolactone) microspheres as a floating delivery system for intravesical instillation.

Intravesical instillation is an effective post-treatment for bladder cancer performed by delivering medications directly into the bladder to target the remaining cancer cells. The current study thus aimed to develop porous poly(L-lactide-co-ε-caprolactone) (PLCL) microspheres encapsulated with 10-hydroxycamptothecin (HCPT) via microfluidics to serve as a drug delivery system with persistent floating capacity and sustained HCPT-release property for intravesical instillation. A microfluidic device was designed to fabricate PLCL microspheres and encapsulate HCPT (HCPT-MS) within them; methanol and tridecane were introduced into an oil phase as a co-solvent and pore-forming agent, respectively, to regulate the floating ability of microspheres. The physicochemical properties of the resulting microspheres were characterized, and the floating behavior, release profile and anti-tumor effects of HCPT-MS were investigated. The obtained spherical HCPT-MS were 119.23 μm in size, monodisperse, and featured a porous concave surface and hollow structure. The encapsulation efficiency and drug loading of HCPT within HCPT-MS was around 67% and 4.9%, respectively. HCPT-MS exhibited impressive floating capabilities in water, PBS and artificial urine even in a simulated bladder dynamic environment. These microspheres remained afloat after being subjected to 90 repeated simulated urination processes. The sustained release of HCPT from these floating microspheres lasted for more than 10 days. The IC50 (half maximal inhibitory concentration) of HCPT-MS was calculated to be 52.14 μg mL-1. T24 cells (human bladder cancer cells) when cultured with HCPT-MS at such a concentration were severely inhibited, and the inhibition further enhanced with an increase in culture time. Hence, the feasibility of the current porous and floating HCPT-MS as a formulation for intravesical instillation to deliver medications into the bladder with sustained release and stability was thus substantiated.

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.

Corrosion Behavior of AZ80 Magnesium Alloy in Simulated Static and Dynamic Fluid Environments with Different pH Values

Corrosion Behavior of AZ80 Magnesium Alloy in Simulated Static and Dynamic Fluid Environments with Different pH Values

IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments

The development process of high-fidelity SLAM systems depends on their validation upon reliable datasets. Towards this goal, we propose IBISCape, a simulated benchmark that includes data synchronization and acquisition APIs for telemetry from heterogeneous sensors: stereo-RGB/DVS, Depth, IMU, and GPS, along with the ground truth scene segmentation and vehicle ego-motion. Our benchmark is built upon the CARLA simulator, whose back-end is the Unreal Engine rendering a high dynamic scenery simulating the real world. Moreover, we offer 34 multi-modal datasets suitable for autonomous vehicles navigation, including scenarios for scene understanding evaluation like accidents, along with a wide range of frame quality based on a dynamic weather simulation class integrated with our APIs. We also introduce the first calibration targets to CARLA maps to solve the unknown distortion parameters problem of CARLA simulated DVS and RGB cameras. Finally, using IBISCape sequences, we evaluate four ORB-SLAM3 systems (monocular RGB, stereo RGB, Stereo Visual Inertial (SVI), and RGB-D) performance and BASALT Visual-Inertial Odometry (VIO) system on various sequences collected in simulated large-scale dynamic environments. Keywords: benchmark, multi-modal, datasets, Odometry, Calibration, DVS, SLAM

Optimal Path Planning of Autonomous Marine Vehicles in Stochastic Dynamic Ocean Flows Using a GPU-Accelerated Algorithm

Autonomous marine vehicles play an essential role in many ocean science and engineering applications. Planning time and energy optimal paths for these vehicles to navigate in stochastic dynamic ocean environments are essential to reduce operational costs. In some missions, they must also harvest solar, wind or wave energy (modeled as a stochastic scalar field) and move in optimal paths that minimize net energy consumption. Markov decision processes (MDPs) provide a natural framework for sequential decision making for robotic agents in such environments. However, building a realistic model and solving the modeled MDP becomes computationally expensive in large-scale real-time applications, warranting the need of parallel algorithms and efficient implementation. In this article, we introduce an efficient end-to-end graphical processing unit (GPU)-accelerated algorithm that 1) builds the MDP model (computing transition probabilities and expected one-step rewards) and 2) solves the MDP to compute an optimal policy. We develop methodical and algorithmic solutions to overcome the limited global memory of GPUs by 1) using a dynamic reduced-order representation of the ocean flows; 2) leveraging the sparse nature of the state transition probability matrix; 3) introducing a neighboring subgrid concept; and 4) proving that it is sufficient to use only the stochastic scalar field’s mean to compute the expected one-step rewards for missions involving energy harvesting from the environment, thereby saving memory and reducing the computational effort. We demonstrate the algorithm on a simulated stochastic dynamic environment and highlight that it builds the MDP model and computes the optimal policy 600–1000 times faster than conventional CPU implementations, making it suitable for real-time use.

Antimalarial phytochemicals as potential inhibitors of SARS-CoV-2 guanine N7-methyltransferase (nsp 14): an integrated computational approach

The inhibition of capping enzymes such as guanine-N7-methyltransferase (GMT) is an attractive target for regulating viral replication, transcription, virulence, and pathogenesis. Thus, compounds that target the Severe Acute Respiratory Syndrome Corona Virus 2 GMT (S2GMT) will enhance drug development against COVID-19. In this study, an in-house library of 249 phytochemicals from African medicinal plants was screened using computational approaches including homology modeling, molecular docking, molecular dynamic simulations, binding free energy calculations based on molecular mechanics/Poisson-Boltzmann surface area (MMPBSA) and Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) analysis for inhibitors of S2GMT. The top-ten ranked phytochemicals (TTRP) obtained from the docking analysis to S2GMT were further docked to SARS-COV N7-MTase. Among the TTRP, the top-four ranked phytocompounds (TFRP) viz: 3 alkaloids (Isocryptolepine, 10’–Hydroxyusambarensine and Isostrychnopentamine) and a flavonoid (Mulberrofuran F) interacted strongly with critical catalytic residues whose interference either reduce or completely abolish N7-MTase activity, indicating their potential as capping machinery disruptors. The interactions of TFRP with the catalytic residues of S2GMT were preserved in a 100 ns simulated dynamic environment, thereby, demonstrating high degree of structural stability. The MMPBSA binding free energy calculations corroborated the docking scores with biscryptolepine having the highest binding free energy to S2GMT. The TFRP showed favourable drug-likeness and ADMET properties over a wide range of molecular descriptors. Therefore, the TFRP can be further explored as potential S2GMT inhibitors in in vitro and in vivo experiments. Communicated by Ramaswamy H. Sarma

SARS-CoV-2 host cell entry: an in silico investigation of potential inhibitory roles of terpenoids

BackgroundTargeting viral cell entry proteins is an emerging therapeutic strategy for inhibiting the first stage of SARS-CoV-2 infection. In this study, 106 bioactive terpenoids from African medicinal plants were screened through molecular docking analysis against human angiotensin-converting enzyme 2 (hACE2), human transmembrane protease serine 2 (TMPRSS2), and the spike (S) proteins of SARS-CoV-2, SARS-CoV, and MERS-CoV. In silico absorption-distribution-metabolism-excretion-toxicity (ADMET) and drug-likeness prediction, molecular dynamics (MD) simulation, binding free energy calculations, and clustering analysis of MD simulation trajectories were performed on the top docked terpenoids to respective protein targets. ResultsThe results revealed eight terpenoids with high binding tendencies to the catalytic residues of different targets. Two pentacyclic terpenoids (24-methylene cycloartenol and isoiguesteri) interacted with the hACE2 binding hotspots for the SARS-CoV-2 spike protein, while the abietane diterpenes were found accommodated within the S1-specificity pocket, interacting strongly with the active site residues TMPRSS2. 3-benzoylhosloppone and cucurbitacin interacted with the RBD and S2 subunit of SARS-CoV-2 spike protein respectively. These interactions were preserved in a simulated dynamic environment, thereby, demonstrating high structural stability. The MM-GBSA binding free energy calculations corroborated the docking interactions. The top docked terpenoids showed favorable drug-likeness and ADMET properties over a wide range of molecular descriptors. ConclusionThe identified terpenoids from this study provides core structure that can be exploited for further lead optimization to design drugs against SARS-CoV-2 cell-mediated entry proteins. They are therefore recommended for further in vitro and in vivo studies towards developing entry inhibitors against the ongoing COVID-19 pandemic.

Effect of cathodic protection current density on corrosion rate of high-strength steel wires for stay cable in simulated dynamic marine atmospheric rainwater

Impressed current cathodic protection (ICCP) is considered an effective method for reducing the corrosion of structures, but is seldom used in the corrosion protection of high-strength steel wires for bridge cables (e.g., stay cable) because the atmospheric environment in which the steel wires are located can barely provide electrolyte solutions for the ICCP system. However, rainwater accumulates on the cable surface and forms rivulets under rainfall conditions, and such rainwater rivulets can be used as electrolyte solutions for the ICCP system of high-strength steel wires. On this basis, this study proposed an ICCP method for high-strength steel wires in rainfall environment and investigated the effects of different protection current densities on the corrosion rate of stay cable steel wires in a simulated dynamic rainwater environment. In this work, the test time and environmental temperature were fixed at 20 d and 28 °C, and a 3% NaCl solution was employed as the simulated rainwater. Corrosion morphologies of steel wires were used to qualitatively characterize the corrosion degree. Weight loss and electrochemical measurements were used to quantitatively characterize the corrosion rate. Scanning electron microscopy and X-ray diffraction were used to analyze the microscopic morphology of corroded steel wires and the chemical composition of corrosion products, respectively. Results showed that in comparison with unprotected steel wires, the surface of steel wires with ICCP were flatter, the number and size of corrosion pits were significantly reduced, and the corrosion rate was significantly reduced. Thus, ICCP played a protective effect throughout the test period; however, the protection efficiency was slightly different under different flow rates. The steel wires were effectively protected at any protective current density under the flow rate of 0.7 mL/min, but were not effectively protected when the protection current density reached 60 mA/m2 under the flow rates of 7 mL/min and 14 mL/min.

A dynamic framework for tuning SVM hyper parameters based on Moth-Flame Optimization and knowledge-based-search

In the real world, most of the collections of data are dynamic in nature, i.e. their size may grow with time. This dynamic nature of the data not only reduces the performance of the classifiers but also demands more optimized models for retaining the performance. Due to this, machine learning models developed in a static environment cannot be deployed efficiently to solve the real-world problems. Nowadays, maximum existing works consider only the static behaviour of the data for the training of machine learning models where the size of the collection of training data does not change over time. This paperwork imposes Support Vector Machine (SVM) in a dynamic environment. It has been identified that shifting of the optimum values of two hyper-parameters C (Penalty Parameter) and γ (Kernel Parameter) in the search space is one of the primary reasons for the performance degradation of SVM in dynamic environment. This paper proposes a novel framework that uses a new optimization module Knowledge-Based-Search (KBS) along with Moth –Flame Optimization (MFO) to optimize C and γ in a dynamic environment to train SVM efficiently. KBS uses knowledge gathered at various instances of time, which are the bi-products of MFO. MFO in our framework is the base optimization algorithm which works underneath KBS. The experiments have shown that KBS helps in controlling the exponential growth of the time complexity of the optimization process where only MFO is used to optimize C andγ. Integration of KBS with MFO brings down the time complexity to a large extent. To validate the proposed framework we have used a simulated dynamic environment for profit/loss classification problem for organizations. The experiments have also shown that KBS's integration with MFO outperforms integration of KBS with other modern optimization techniques such as Particle Swarm Optimization (PSO), Multi-Verse Optimization (MVO), Grey-Wolf Optimization (GWO), Cuckoo Search (CS), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Fire-Fly Algorithm (FFA) and Salp Swarm Algorithm (SSA).

Biocorrosion behavior of micro-arc-oxidized AZ31 magnesium alloy in different simulated dynamic physiological environments

The micro-arc-oxidization (MAO) technique was used to form the protective coating on the surface of AZ31 magnesium alloy and the corrosion performance of the coated magnesium alloy in static and different dynamic flow environments with three flow rates in simulated body fluid (SBF) were systematically studied in this paper. The results suggested that the protective effect of MAO coating was relatively weakened in simulated dynamic environments than that in static solution and the corrosion rate was accelerated when the flow rate arose. It is noticed that the corrosion current density (icorr) of coated Mg alloy and the flow rate (v) matches the linear relationship of icorr−1 ~ v-1/2, and the relationships between log(icorr) and corrosion time (t) at different v were polynomial fitted based on the electrochemical results. The calculated threshold of t when the coating and products layer lost their protective effects became earlier as the v increased. The influence of the flow field on the biocorrosion behavior of MAO coated magnesium alloy and the evolution of the protective effect of the coating during the corrosion process was also discussed. Our work provides a novel theoretical reference for studying the biocorrosion performance of coated magnesium alloys for future biomedical applications.

Architectural Framework for Exploring Adaptive Human-Machine Teaming Options in Simulated Dynamic Environments

With the growing complexity of environments in which systems are expected to operate, adaptive human-machine teaming (HMT) has emerged as a key area of research. While human teams have been extensively studied in the psychological and training literature, and agent teams have been investigated in the artificial intelligence research community, the commitment to research in HMT is relatively new and fueled by several technological advances such as electrophysiological sensors, cognitive modeling, machine learning, and adaptive/adaptable human-machine systems. This paper presents an architectural framework for investigating HMT options in various simulated operational contexts including responding to systemic failures and external disruptions. The paper specifically discusses new and novel roles for machines made possible by new technology and offers key insights into adaptive human-machine teams. Landed aircraft perimeter security is used as an illustrative example of an adaptive cyber-physical-human system (CPHS). This example is used to illuminate the use of the HMT framework in identifying the different human and machine roles involved in this scenario. The framework is domain-independent and can be applied to both defense and civilian adaptive HMT. The paper concludes with recommendations for advancing the state-of-the-art in HMT.

Humanoid Gait Generation on Uneven Ground using Intrinsically Stable MPC

Abstract This paper presents a Model Predictive Control (MPC) scheme capable of generating a 3D gait for a humanoid robot. The proposed method starts from an assigned sequence of footsteps and generates online the trajectory of both the Zero Moment Point and Center of Mass. Starting from the moment balance (neglecting rotations) we derive a model characterizing all 3D trajectories that satisfy a linear differential equation along all three axes. Then a solution is found by extending our previously proposed intrinsically stable MPC, which employs a stability constraint for guaranteeing boundedness of the solution. The method is validated using a NAO robot in a simulated dynamic environment.

Remobilization of trace metals from contaminated marine sediment in a simulated dynamic environment.

In this study, release and redistribution of sediment bound trace metals due to resuspension were investigated by a lid-driven elongated annular flume (LEAF). The total suspended particulate matters (SPMs) increased significantly in quantity with the raised resuspension energies and varied distinctively in particle size and mineral composition. Except for Cu, Ni, Cd, Pb, and Zn showed an increase in dissolved phase as the resuspension energy increased. Relatively low Cu was observed in dissolved phase whereas it owned the highest original concentration in the sediment. This is primarily due to the very low solubility of Cu sulfide. In comparison to sediment, all metals were evidently enriched in SPMs which primarily contributed to the much more fine particles (silt/clay fraction) contained in the SPMs. Metals enrichment followed the Irving-Williams order of complex stability. However, metals content varied indistinctively in the SPMs among the three selected resuspension levels. The distribution coefficients (K d) exhibited opposite trend with the increasing resuspension level with the exception of Cu. It indicated that physical and chemical characters of sediment such as grain composition, Fe/Mn, and organic matter content may also act as major factors in the release of metals and control their phase distribution in the water column.

An Integrated Modular On-line Experiment Platform Proposal for Simulation-Based Experiments with Domain Experts as Participants

Researchers face difficulty in accessing subject-matter-experts as participants in lab settings for many common human factor research studies. Delivering such experiments online would allow researchers access to a much broader population of subject-matter-experts; however, currently, there appears to be a lack of demonstrated techniques to design and deliver complex research studies on the web. This is particularly the case for experiments involving communication between multiple participants, simulated dynamic task envi-ronments, and participant screening for domain expertise. This paper describes and demonstrates a novel technique of integrating current online video-sharing technology, web-site templating and embedding third-party technologies to create a re-usable, customizable, on-line experiment delivery platform. This allows researchers to include a broader population of subject-matter-experts in a time and cost efficient manner.

ITOMP: Incremental Trajectory Optimization for Real-Time Replanning in Dynamic Environments

We present a novel optimization-based algorithm for motion planning in dynamic environments. Our approach uses a stochastic trajectory optimization framework to avoid collisions and satisfy smoothness and dynamics constraints. Our algorithm does not require a priori knowledge about global motion or trajectories of dynamic obstacles. Rather, we compute a conservative local bound on the position or trajectory of each obstacle over a short time and use the bound to compute a collision-free trajectory for the robot in an incremental manner. Moreover, we interleave planning and execution of the robot in an adaptive manner to balance between the planning horizon and responsiveness to obstacle. We highlight the performance of our planner in a simulated dynamic environment with the 7-DOF PR2 robot arm and dynamic obstacles.

Extensive Experimental Validation of a Personalized Approach for Coping with Unfair Ratings in Reputation Systems

The unfair rating problem exists when a buying agent models the trustworthiness of selling agents by also relying on ratings of the sellers from other buyers in electronic marketplaces, that is in a reputation system. In this article, we first analyze the capabilities of existing approaches for coping with unfair ratings in different challenging scenarios, including ones where the majority of buyers are dishonest, buyers lack personal experience with sellers, sellers may vary their behavior, and buyers may provide a large number of ratings. We then present a personalized modeling approach (PMA) that has all these capabilities. Our approach allows a buyer to model both the private reputation and public reputation of other buyers to determine whether these buyers' ratings are fair. More importantly, in this work, we focus on experimental comparison of our approach with two key models in a simulated dynamic e-marketplace environment. We specifically examine the above mentioned scenarios to confirm our analysis and to demonstrate the capabilities of our approach. Our study thus provides the extensive experimental support for the personalized approach that can be effectively employed by reputation systems to cope with unfair ratings.

A Framework for the Simulation and Haptic Display of Dynamic Systems Subject to Holonomic Constraints

In this paper we present a framework that enables an operator to haptically and visually interact with a simulated dynamic environment subject to virtual holonomic constraints. The framework combines a geometric constraint solver with a constrained dynamics simulation engine that controls an admittance-type haptic display. This system takes on relevant issues in the context of assisted teleoperated tasks, from providing an intuitive interface for creating and combining virtual constraints, to haptically displaying rigid motion constraints in simulated environments subject to desired inertial dynamics. Two experiments carried out using the Cobotic Hand Controller haptic display are presented.

Motion planning for multiple non-holonomic robots: a geometric approach

SUMMARYIn this paper, a geometrical approach is developed to generate simultaneously optimal (or near-optimal) smooth paths for a set of non-holonomic robots, moving only forward in a 2D environment cluttered with static and moving obstacles. The robots environment is represented by a 3D geometric entity called Bump-Surface, which is embedded in a 4D Euclidean space. The multi-motion planning problem (MMPP) is resolved by simultaneously finding the paths for the set of robots represented by monoparametric smoothC2curves onto the Bump-Surface, such that their inverse images onto the initial 2D workspace satisfy the optimization motion-planning criteria and constraints. The MMPP is expressed as an optimization problem, which is solved on the Bump-Surface using a genetic algorithm. The performance of the proposed approach is tested through a considerable number of simulated 2D dynamic environments with car-like robots.

Energy-Minimizing Motion Design for Nonholonomic Robots Amidst Moving Obstacles

AbstractIn this paper, a new method is introduced for computing energy-minimizing motions in twodimensional environments cluttered with a priori known static and moving obstacles. The proposed method is based on a new four-dimensional motion-planning space represented by a Bump- Surface embedded in ℜ4. The energy-minimizing motion-design problem is expressed by a variational curve-design problem on the introduced Bump-Surface. The optimal motion is determined by a new algorithm for computing a B-spline curve on the Bump-Surface which is conformal to the motion-planning constrains. The proposed method is applied for designing the motion of nonholonomic robots in the presence of moving obstacles and its performance is tested in simulated 2D dynamic environments with car-like robots.

Internet technology for future space missions

Ongoing work at National Aeronautics and Space Administration Goddard Space Flight Center (NASA/GSFC), seeks to apply standard Internet applications and protocols to meet the technology challenge of future satellite missions. Internet protocols and technologies are under study as a future means to provide seamless dynamic communication among heterogeneous instruments, spacecraft, ground stations, constellations of spacecraft, and science investigators. The primary objective is to design and demonstrate in the laboratory the automated end-to-end transport of files in a simulated dynamic space environment using off-the-shelf, low-cost, commodity-level standard applications and protocols. The demonstrated functions and capabilities will become increasingly significant in the years to come as both earth and space science missions fly more sensors and the present labor-intensive, mission-specific techniques for processing and routing data become prohibitive. This paper describes how an IP-based communication architecture can support all existing operations concepts and how it will enable some new and complex communication and science concepts. The authors identify specific end-to-end data flows from the instruments to the control centers and scientists, and then describe how each data flow can be supported using standard Internet protocols and applications. The scenarios include normal data downlink and command uplink as well as recovery scenarios for both onboard and ground failures. The scenarios are based on an Earth orbiting spacecraft with downlink data rates from 300 kbps to 4 Mbps. Included examples are based on designs currently being investigated for potential use by the Global Precipitation Measurement (GPM) mission.