Simulation Study Research Articles

Simulation Study on Diffusion of Chlorides in Concretes with Load-Induced Heterogeneous Stress

Simulation Study on Diffusion of Chlorides in Concretes with Load-Induced Heterogeneous Stress

In silico Identification of HDAC Inhibitors for Multiple Myeloma: A Structure-based Virtual Screening, Drug Likeness, ADMET Profiling, Molecular Docking, and Molecular Dynamics Simulation Study

Background: Multiple myeloma (MM) is a hematological malignancy of plasma cells that produce a monoclonal immunoglobulin protein. Despite significant advances in the treatment of MM, currently available therapies are associated with toxicity and resistance. As a result, there is an increasing demand for novel, effective therapeutics. Inhibition of histone deacetylases (HDACs) is emerging as a potential method for treating cancer. HDAC6 is one of 18 different HDAC isoforms that regulate tubulin lysine 40 and function in the microtubule network. HDAC6 participates in tumorigenesis and metastasis through protein ubiquitination, tubulin, and Hsp90. Several studies have found that inhibiting HDAC6 causes AKT and ERK dephosphorylation, which leads to decreased cell proliferation and promotes cancer cell death via the PI3K/AKT and MAPK/ERK signaling pathways. Objective: The objective of this study is to target HDAC6 and identify potent inhibitors for the treatment of multiple myeloma by employing computer-aided drug design. Materials and Methods: A total of 199,611,439 molecules from five different chemical databases, such as CHEMBL25, ChemSpace, Mcule, MolPort, and ZINC, have been screened against HDAC6 by structure- based virtual screening, followed by filtering for various drug-likeness, ADME, toxicity, consensus molecular docking, and 100 ns MD simulation. Results: Our research work resulted in three molecules that have shown strong binding affinity (CHEMBL2425964 -9.99 kcal/mol, CHEMBL2425966 -9.89 kcal/mol, and CSC067477144 -9.86 kcal/mol) at the active site HDAC6, along with effective ADME properties, low toxicity, and high stability. Inhibiting HDAC6 with these identified molecules will induce AKT and ERK dephosphorylation linked to reduced cell proliferation and promote cancer cell death. Conclusion: CHEMBL2425964, CHEMBL2425966, and CSC067477144 could be effective against multiple myeloma.

Eugenol targeting CrtM inhibits the biosynthesis of staphyloxanthin in Staphylococcus aureus

Staphylococcus aureus is a serious foodborne pathogen threatening food safety and public health. Especially the emergence of methicillin-resistant Staphylococcus aureus (MRSA) increased the difficulty of S. aureus treatment. Staphyloxanthin is a crucial virulence factor of S. aureus. Blocking staphyloxanthin production could help the host immune system counteract the invading S. aureus cells. In this study, we first screened for staphyloxanthin inhibitors using a virtual screening method. The outcome of the virtual screening method resulted in the identification of eugenol (300 μg/mL), which significantly inhibits the staphyloxanthin production in S. aureus ATCC 29213, S. aureus Newman, MRSA ATCC 43300 and MRSA ATCC BAA1717 by 84.2 %, 63.5 %, 68.1 %, and 79.5 %, respectively. The outcome of the growth curve assay, field-emission scanning electron, and confocal laser scanning microscopy analyses confirmed that eugenol at the test concentration did not affect the morphology and growth of S. aureus. Moreover, the survival rate of S. aureus ATCC 29213 and MRSA ATCC 43300 under H2O2 pressure decreased to 51.9 % and 45.5 % in the presence of eugenol, respectively. The quantitative RT-PCR and molecular simulation studies revealed that eugenol targets staphyloxanthin biosynthesis by downregulating the transcription of the crtM gene and inhibiting the activity of the CrtM enzyme. Taken together, we first determined that eugenol was a prominent compound for staphyloxanthin inhibitor to combat S. aureus especially MRSA infections.

Decomposition by Approximation With Pulse Waves Allowing Further Research on Sources of Voltage Fluctuations

Voltage fluctuations are common disturbances in power grids. Initially, it is necessary to selectively identify individual sources of voltage fluctuations to take actions to minimize the effects of voltage fluctuations. Selective identification of disturbing loads is possible using a signal chain consisting of demodulation, decomposition, and assessment of the propagation of component signals. The accuracy of such an approach is closely related to the applied decomposition method. The paper presents a new method for decomposition by approximation with pulse waves. The proposed method allows for a correct identification of selected parameters, that is, the frequency of changes in the operating state of individual sources of voltage fluctuations and the amplitude of voltage changes caused by them. The article presents results from numerical simulation studies and laboratory experimental studies, based on which the estimation errors of the indicated parameters were determined by the proposed decomposition method and other empirical decomposition methods available in the literature. The real states that occur in power grids were recreated in the research. The metrological interpretation of the results obtained from the numerical simulation and experimental research is discussed.

Numerical Simulation Study of the Optimal Parameters for Dust Reduction by an External Air Curtain (EAC) in Continuous Mining Tunnel

Numerical Simulation Study of the Optimal Parameters for Dust Reduction by an External Air Curtain (EAC) in Continuous Mining Tunnel

Performance Optimization and Numerical Simulation Study of Sludge Low-Temperature Drying System

Performance Optimization and Numerical Simulation Study of Sludge Low-Temperature Drying System

Adaptive Online Neural Predictive Control of Hydraulic Actuator Using FPGA Acceleration

Hydraulic actuators have recently been widely used in control systems as efficient output devices. However, high-precision control of hydraulic actuators in industrial applications remains challenging due to the conflict between controllers' complexity, especially in theoretical modeling, and their generalization abilities, including adaptability to parametric uncertainties and versatility for various working conditions. To address this issue, we propose an adaptive online neural predictive control (AONPC) scheme, which is capable of handling unmodeled nonlinearities and uncertainties with a simple structure and low demand for system modeling. Specifically, an artificial neural network (ANN) is responsible for data-driven system identification and predictive control, while upper confidence bound (UCB) algorithm is introduced for hyperparameter self-adaptation. The ANN structure and the iteration number for solving the optimization problem are determined through a simulation study. Then, to complete the whole control loop within a short and fixed tracking interval, the proposed controller is implemented on a field programmable gate array (FPGA), which can effectively improve the tracking precision. Finally, extensive experimental results on a realistic hydraulic actuator device demonstrate the superiority of the proposed controller.

Gaussian Process Latent Variable Model-Based Multi-Output Modeling of Incomplete Data

The rapid development of sensor technologies allows the acquisition of high dimensional sensing data. Multi-output modeling techniques have been developed to leverage the data for decision making. However, the data often contain segments of missing values, which cause great information loss and thus affect the modeling performance. This study explores the missing pattern and the correlation structure of missing segments and maximally exploits useful information in the data to improve multi-output modeling accuracy. Specifically, a new multi-output modeling method is developed based on Gaussian Process Latent Variable Model (GPLVM). A decision score is developed to seek an optimal modeling strategy and then a tailored Expectation-Maximization (EM) algorithm based on GPLVM is designed to estimate the missing segments while optimizing model parameters. The proposed method demonstrates superior performance in both a simulation study and a case study, which makes it a powerful tool to enable process automation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In real-life applications, missing values are constantly present in multi-output sensing data, which greatly affects data-driven decision-making. Modeling of such data becomes more difficult when consecutive observations within or across different outputs are missing. Existing methods often discard the missing values and extract information only from the available observations. However, the pattern of missing values may contain important messages that can potentially boost the modeling performance. This research develops a new framework based on GPLVM to model multi-output data with segmented missing patterns. A tailored EM algorithm is developed to iteratively impute the missing values and optimize model parameters. In addition, a decision score that quantifies both the missing pattern and correlation is designed to determine an optimal modeling strategy. The proposed method can benefit many applications across different industries that require modeling of multi-output incomplete data, especially when the data have many segments of missing observations.

Distributed Event-Triggered Quantized Fault-Tolerant Control of Linear Multiagent Systems With External Disturbances and Parameter Uncertainties.

In this article, the issue of fault-tolerant leader-following consensus under a distributed dynamic event-triggered mechanism is addressed for linear multiagent systems (MASs) in the presence of unknown parameter uncertainties, external disturbances, and actuator faults, including loss of effectiveness and bias, in which the mechanism is with quantized state measurements. Due to the fact that information is transmitted via a bandwidth-limited communication network, a quantized control scheme with a uniform quantizer is introduced for leader-following consensus. In order to decrease the communication load and save the limited communication network resources, a distributed event-triggered mechanism is studied for leader-following consensus problem of linear MASs with quantized state measurements. In the presence of actuator faults, external disturbances, and unknown parameter uncertainties, an adaptive coupling gain for the controller is presented. Based on the Lyapunov function approach, the stability of the closed-loop system and the convergence of consensus errors are proved. Furthermore, the Zeno behavior is excluded for the triggering time sequences. Finally, simulation studies are given to verify the effectiveness of the proposed event-triggered fault-tolerant control scheme.

CMT and PBL study in the high-pressure phase on the advance core-cooling mechanism experiment with RELAP 5

The passive core cooling system is an important improvement for the emergency core cooling system of pressurized water reactor. However, although there’re studies focus on each component, the study on the integral effect still not sufficient. Especially the study on the core makeup tanks (CMTs). Our simulation study is based on the SBLOCA experiment on Advanced Plant Experiment (APEX) and Advance Core-cooling Mechanism Experiment (ACME) facilities by RELAP 5 MOD 3.2. This study contributes to identify the simulation error and tries to provide some effective way to establish the better describing model on RELAP 5.

Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment.

This article is concerned with the problem of planning optimal maneuver trajectories and guiding the mobile robot toward target positions in uncertain environments for exploration purposes. A hierarchical deep learning-based control framework is proposed which consists of an upper level motion planning layer and a lower level waypoint tracking layer. In the motion planning phase, a recurrent deep neural network (RDNN)-based algorithm is adopted to predict the optimal maneuver profiles for the mobile robot. This approach is built upon a recently proposed idea of using deep neural networks (DNNs) to approximate the optimal motion trajectories, which has been validated that a fast approximation performance can be achieved. To further enhance the network prediction performance, a recurrent network model capable of fully exploiting the inherent relationship between preoptimized system state and control pairs is advocated. In the lower level, a deep reinforcement learning (DRL)-based collision-free control algorithm is established to achieve the waypoint tracking task in an uncertain environment (e.g., the existence of unexpected obstacles). Since this approach allows the control policy to directly learn from human demonstration data, the time required by the training process can be significantly reduced. Moreover, a noisy prioritized experience replay (PER) algorithm is proposed to improve the exploring rate of control policy. The effectiveness of applying the proposed deep learning-based control is validated by executing a number of simulation and experimental case studies. The simulation result shows that the proposed DRL method outperforms the vanilla PER algorithm in terms of training speed. Experimental videos are also uploaded, and the corresponding results confirm that the proposed strategy is able to fulfill the autonomous exploration mission with improved motion planning performance, enhanced collision avoidance ability, and less training time.

Give them a fishing rod, if it is not urgent: The impact of help type on support for helpers' leadership.

Taking a follower's perspective on leadership and contributing to the new research stream on behaviors conducive to its emergence, we examined how distinct types of instrumental (task focused) helping-autonomy- versus dependency-helping-affected recipients' support for their helpers' leadership. Based on the literature on employees' needs for autonomy and mastery, combined with the empowering nature of autonomy-helping, we reasoned that autonomy- (vs. dependency-) helping typically signals greater benevolence toward recipients, enhancing their support for their helpers' leadership. Our findings were generalized across various samples (of U.S. and Israeli employees), manipulations, and research settings: simulations (Studies 1 and 2b), workplace role-play scenario (Study 2a), and recollections of helping events in the workplace (Study 3). We found that autonomy- (vs. dependency-) helping increased recipients' support for their helpers' leadership by heightening perceptions of helpers' benevolence-based (rather than ability-based) trustworthiness (Studies 1 and 3). We also showed time pressure to be a boundary condition under which the advantage of autonomy-helping disappeared (Studies 2a and 2b)-with dependency-helping then inducing comparable levels of perceived benevolence and thus similar support for the helper's potential leadership. Overall, we shed light on the development of informal leadership by uncovering how recipients interpret and respond to the two help types. Practically, this analysis opens the door to new ways for aspiring managers to enhance support for their leadership from potential followers, available even to those unlikely to be appointed to formal leadership positions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Fixed-Time Rigidity-Based Formation Maneuvering for Nonholonomic Multirobot Systems With Prescribed Performance.

This article presents rigidity-based formation maneuvering for a group of nonholonomic mobile robots subject to limited sensing capability, where the performance bounds are introduced to constrain the distance and angle errors. The time-varying and asymmetric performance constraints can prescribe the transient and steady-state performance of the closed-loop systems, which further specify collision avoidance and connectivity maintenance among neighboring robots and avoid the controller singularity issue. To satisfy the constraint requirements and fixed-time convergence, universal barrier Lyapunov functions are incorporated with control design such that angle errors are fixed-time stable and distance errors can converge to a small neighborhood around zero in fixed time. Under the proposed control protocol, all robots can track the desired time-varying velocity while generating and maintaining the predefined formation defined by a minimally and infinitesimally rigid graph. Simulation and experiment studies are carried out to illustrate the effectiveness of the proposed control protocol.

L-NORM: Learning and Network Orchestration at the Edge for Robot Connectivity and Mobility in Factory Floor Environments

Robotic factory floors will revolutionize the future of manufacturing and the service industry by automating tasks. However, to fully supplement human effort, these robots will need low-latency, reliable connectivity throughout the work zone through links established by wireless access points (APs). This will allow the robot to assuredly respond to programming directives that rely on the real-time relaying of robot-generated sensor data to the Mobile Edge Computing (MEC) server. In this paper, we propose L-NORM, a multi-AP and multi-robot coordination framework, as a multi-tiered solution for such autonomous edge networks. First, multi-robot motion planning through reinforcement learning occurs at the MEC, using as input multi-modal robot sensor data. Second, multi-AP resource orchestration is performed using another reinforcement learning-based method that maps a subset of available APs to each robot toward meeting their sensor data delivery requirements. Furthermore, we suggest diversity combination of uplink channels with the 802.11ax scheduled access mode that will (i) support high reliability of multi-robot uplink sensor packets and (ii) enable multi-AP coordination, for optimized resource utilization. Through extensive simulation studies, we show that the probability of robot deviation to remain within 0.5 m from its optimal path, is 19% more in L-NORM compared to classical 802.11ax based edge network solution, considering <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 1 MB of sensor data per robot.

Numerical Simulation Study on Optimizing the Conical Cutter Bit to Break Deep Strata

Background: Methods that are used to obtain oil and gas resources by penetrating deep strata are gradually being developed. The complex lithology of deep strata causes difficulties for traditional drill bits to cut into the rock effectively, which affects the drilling efficiency. Objective: The goal of this work is to optimize the bit structure and design a patented drill bit for more efficient rock breaking. Methods: Through a series of tests, the rock mechanics parameters of deep strata in a block of Jilin Oilfield were measured, and the drill bit model XFXR5195 was selected to best match the lithology of the formation by using the Virtual Strength Index and the Technique for Order Preference by Similarity to an Ideal Solution. Simulation of the rock breaking process of the conical PDC cutter and cylindrical PDC cutter with the finite element method. Results: The simulation results show that the pre-crushing of the conical cutter can effectively reduce the difficulty of the subsequent cylindrical cutter in breaking and cutting rocks under 100 MPa compressive strength rocks; under 50 MPa compressive strength rocks, the combination of the conical cutter and cylindrical cutter are not effective with regards to drilling. Conclusion: The conical PDC cutter has a more uniform force range and more concentrated stress on the rock than the cylindrical PDC cutter. The rock-breaking ability of the conical PDC cutter is higher than that of the cylindrical PDC cutter under different compressive strength rocks and especially in high compressive strength rocks.

Stochastic Optimal Control for Robot Manipulation Skill Learning Under Time-Varying Uncertain Environment.

In this article, a novel stochastic optimal control method is developed for robot manipulator interacting with a time-varying uncertain environment. The unknown environment model is described as a nonlinear system with time-varying parameters as well as stochastic information, which is learned via the Gaussian process regression (GPR) method as the external dynamics. Integrating the learned external dynamics as well as the stochastic uncertainties, the complete interaction system dynamics are obtained. Then the iterative linear quadratic Gaussian with learned external dynamics (ILQG-LEDs) method is presented to obtain the optimal manipulation control parameters, namely, the feedforward force, the reference trajectory, as well as the impedance parameters, subject to time-varying environment dynamics. The comparative simulation studies verify the advantages of the presented method, and the experimental studies of the peg-hole-insertion task prove that this method can deal with complex manipulation tasks.

A Stepwise Multivariate Granger Causality Method for Constructing Hierarchical Directed Brain Functional Network.

The directed brain functional network construction gives us the new insights into the relationships between brain regions from the causality point of view. The Granger causality analysis is one of the powerful methods to model the directed network. The complex brain network is also hierarchically constructed, which is particularly suited to facilitate segregated functions and the global integration of the segregated functions. Therefore, it is of great interest to explore new approach to model the hierarchical architecture of the directed network. In the present study, we proposed a new approach, namely, stepwise multivariate Granger causality (SMGC), considering both the directed and hierarchical features of brain functional network to explore the stepwise causal relationship in the network. The simulation study demonstrated that the diverse and complex hierarchical organization could be embedded in the apparently simple directed network. The proposed SMGC method could capture the multiple hierarchy of the directed network. When applying to the real functional magnetic resonance imaging (fMRI) datasets, the core triple resting-state networks in human brain showed within-network directed connections in the first-level directed network and rich and diverse between-network pathways in the second-level hierarchical network. The default mode network (DMN) had a prominent role in the resting-state acting as both the causal source and the important relay station. Further exploratory research on the adaption of directed hierarchical network in athletes suggested the enhanced bidirectional communication between the DMN and the central executive network (CEN) and the enhanced directed connections from the salience network (SN) to the CEN in the athlete group. The SMGC approach is capable of capturing the hierarchical architecture of the brain directed functional network, which refreshes the new stepwise causal relationship in the directed network. This might shed light on the potential application for exploring the altered hierarchical organization of brain directed network in neuropsychiatric disorders.

In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Simulation Studies of Nucleoside Analogs for Drug Discovery- A Mini Review.

Nucleoside analogs have been widely used as antiviral, antitumor, and antiparasitic agents due to their ability to inhibit nucleic acid synthesis. Adenosine, cytidine, guanosine, thymidine and uridine analogs such as didanosine, vidarabine, remdesivir, gemcitabine, lamivudine, acyclovir, abacavir, zidovusine, stavudine, and idoxuridine showed remarkable anticancer and antiviral activities. In our previously published articles, our main intention was to develop newer generation nucleoside analogs with acylation-induced modification of the hydroxyl group and showcase their biological potencies. In the process of developing nucleoside analogs, in silico studies play an important role and provide a scientific background for biological data. Molecular interactions between drugs and receptors followed by assessment of their stability in physiological environments, help to optimize the drug development process and minimize the burden of unwanted synthesis. Computational approaches, such as DFT, FMO, MEP, ADMET prediction, PASS prediction, POM analysis, molecular docking, and molecular dynamics simulation, are the most popular tools to culminate all preclinical study data and deliver a molecule with maximum bioactivity and minimum toxicity. Although clinical drug trials are crucial for providing dosage recommendations, they can only indirectly provide mechanistic information through researchers for pathological, physiological, and pharmacological determinants. As a result, in silico approaches are increasingly used in drug discovery and development to provide mechanistic information of clinical value. This article portrays the current status of these methods and highlights some remarkable contributions to the development of nucleoside analogs with optimized bioactivity.

Indole-Bearing Azo Compounds: Molecular Docking and in silico ADMET Analysis

In this study, the interaction between the 12 indole-bearing azo compounds (a-l), which were previously synthesized by our research group, and two proteins, 2XIR and 5TGZ, was investigated using an in silico method. The ligand-protein interaction parameters and quantities were determined via molecular docking simulation studies. Since compound e has the lowest docking scores for both 2XIR and 5TGZ, it was selected for additional research on binding interactions. Both e-2XIR and e-5TGZ had docking scores that were lower than those of the control molecules. ADMET characteristics (absorption, distribution, metabolism, excretion, and toxicity) were anticipated using the ADMETlab 2.0 and ProTox-II server. Compound b was categorized as having the greatest levels of toxicity, falling into the sixth toxicity class.

Thrombin - A Molecular Dynamics Perspective.

Thrombin is a crucial enzyme involved in blood coagulation, essential for maintaining circulatory system integrity and preventing excessive bleeding. However, thrombin is also implicated in pathological conditions such as thrombosis and cancer. Despite the application of various experimental techniques, including X-ray crystallography, NMR spectroscopy, and HDXMS, none of these methods can precisely detect thrombin's dynamics and conformational ensembles at high spatial and temporal resolution. Fortunately, molecular dynamics (MD) simulation, a computational technique that allows the investigation of molecular functions and dynamics in atomic detail, can be used to explore thrombin behavior. This review summarizes recent MD simulation studies on thrombin and its interactions with other biomolecules. Specifically, the 17 studies discussed here provide insights into thrombin's switch between 'slow' and 'fast' forms, active and inactive forms, the role of Na+ binding, the effects of light chain mutation, and thrombin's interactions with other biomolecules. The findings of these studies have significant implications for developing new therapies for thrombosis and cancer. By understanding thrombin's complex behavior, researchers can design more effective drugs and treatments that target thrombin.