Simulation Study Research Articles

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.

The accuracy of reliability coefficients: A reanalysis of existing simulations.

Controversy over which reliability estimators should be used persists due to a lack of knowledge about their accuracy. Simulation is an effective tool to obtain an answer, but existing simulation studies yield contradictory results regarding which reliability estimators are the best. The causes of these inconsistent conclusions have yet to be discussed. This study reanalyzes existing studies to understand these contradictions. The most important reason is that previous studies consider only a few reliability estimators. This study examines approximately 30 reliability estimators and finds that there is no single, most accurate reliability estimator across all data types. Instead, several reliability estimators are accurate to comparable levels for unidimensional data (congeneric reliability, Guttman's lambda2, and ten Berge-Zegers's mu). Likewise, multiple reliability estimators perform similarly for multidimensional data (multidimensional parallel reliability, correlated factors reliability, and second-order factor reliability). Whereas many recent studies support factor analysis (FA) reliability estimators, this study shows that not all FA reliability estimators are accurate and that some cause severe overestimation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

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.

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.

Unraveling Bulk versus Surface Passivation Effects in Highly Efficient p–i–n Perovskite Solar Cells Using Thiophene‐Based Cations

Defect passivation is nowadays considered a must‐have route for high‐efficiency perovskite solar cells. However, a general rule that correlates the choice of passivating agents with performance enhancements is still missing. Herein, two different thiophene salts that are used as passivating agents are compared, namely thiophene methylammonium chloride and thiophene ethylammonium chloride (TEACl), which are used for the passivation of bulk and surface defects in triple‐cation‐based metal halide perovskites. First, it is observed that the surface passivation method leads to better device performances reaching a power conversion efficiency of 23.56%, with reduced voltage losses and increased fill factor when compared with the reference. Second, it is demonstrated that the chemical structure of the cation dictates its capability either in passivating bulk defects effectively or to form a superficial two‐dimensional/three‐dimensional heterostructure, which happens only for the TEACl case. The chemical composition and the cation dimension are responsible for device performance enhancement as observed by a joint spectroscopic and density functional theory simulations study, providing rational guidelines for further smart device design.

Assessment of the E-value in the presence of bias amplification: a simulation study

BackgroundThe E-value, a measure that has received recent attention in the comparative effectiveness literature, reports the minimum strength of association between an unmeasured confounder and the treatment and outcome that would explain away the estimated treatment effect. This study contributes to the literature on the applications and interpretations of E-values by examining how the E-value is impacted by data with varying levels of association of unobserved covariates with the treatment and outcome measure when covariate adjustment is applied. We calculate the E-value after using regression and propensity score methods (PSMs) to adjust for differences in observed covariates. Propensity score methods are a common observational research method used to balance observed covariates between treatment groups. In practice, researchers may assume propensity score methods that balance treatment groups across observed characteristics will extend to balance of unobserved characteristics. However, that assumption is not testable and has been shown to not hold in realistic data settings. We assess the E-value when covariate adjustment affects the imbalance in unobserved covariates.MethodsOur study uses Monte Carlo simulations to evaluate the impact of unobserved confounders on the treatment effect estimates and to evaluate the performance of the E-Value sensitivity test with the application of regression and propensity score methods under varying levels of unobserved confounding. Specifically, we compare observed and unobserved confounder balance, odds ratios of treatment vs. control, and E-Value sensitivity test statistics from generalized linear model (GLM) regression models, inverse-probability weighted models, and propensity score matching models, over correlations of increasing strength between observed and unobserved confounders.ResultsWe confirm previous findings that propensity score methods – matching or weighting – may increase the imbalance in unobserved confounders. The magnitude of the effect depends on the strength of correlation between the confounder, treatment, and outcomes. We find that E-values calculated after applying propensity score methods tend to be larger when unobserved confounders result in more biased treatment effect estimates.ConclusionsThe E-Value may misrepresent the size of the unobserved effect needed to change the magnitude of the association between treatment and outcome when propensity score methods are used. Thus, caution is warranted when interpreting the E-Value in the context of propensity score methods.

Numerical Analysis of the Effects of Fuel Injection Duration and Spray Angle on the Combustion Process in a Compression Ignition Engine

The changes in injection strategies for diesel engines have a major impact on the performance and pollutant emission characteristics of diesel engines. If injection strategies like injection duration, injection timing, injection pressure and spray angle are properly adjusted, combustion can be improved. The engine performance will increase and emissions will decrease with the combustion improvement. In this work, the influences of injection duration and spray angle on the combustion characteristics of single cylinder, natural aspirated, electronically controlled injection, compression ignition engine were investigated. In the first stage of the work, experiments were executed on a single cylinder CI engine using a Cussons P8160 DC dynamometer. After the experiments, the piston bowl geometry of the engine was modeled and numerical simulation studies were achieved at 7 different injection durations and 7 different spray angles using Converge CFD software. As a result of this study, it was observed that there is a good match between experimental and simulation data of heat release rate (HRR) and in-cylinder pressure. In-cylinder pressure decreased with longer injection duration. The highest max. in-cylinder pressure was roughly 101.0 bar at 4°CA injection duration and the lowest max. in-cylinder pressure was roughly 82.0 bar at 10°CA injection duration. When the HRR data were analyzed, it was seen that as the injection duration increased, the amount of heat released by combustion decreased. When examining the results of the spray angle analysis, it was concluded that there were not very large differences in-cylinder pressure and HRR data, and there was a difference of 1.4 bar between the highest and lowest max. in-cylinder pressure values. In addition, the highest in-cylinder pressure of approximately 86.7 bar was obtained at a spray angle of 77°. It was observed that the CA50 value was obtained at angles closer to the top dead center by increasing the spray angle and decreasing the injection duration. Moreover, the longest combustion durations were realized at 60° spray angle and 10°CA injection duration.

Case weighted power priors for hybrid control analyses with time-to-event data.

We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCTs) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g., unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted power prior provides robust inference under various forms of incompatibility between the external controls and RCT population.

Two-sample test for stochastic block models via the largest singular value

The stochastic block model is widely used for detecting community structures in network data. However, the research interest in much of the literature focuses on the study of one sample of stochastic block models. Detecting the difference between the two community structures is a less studied issue for stochastic block models. In this article, we propose a novel test statistic based on the largest singular value of a residual matrix obtained by subtracting the geometric mean of two estimated block mean effects from the sum of two observed adjacency matrices. We prove that the null distribution of the proposed test statistic converges in distribution to the Tracy-Widom distribution with index 1, and we show the difference between the two samples for stochastic block models can be tested via the proposed method. We show that the proposed test is asymptotically powerful against alternative models. Further, we extend the proposed method to the degree-corrected stochastic block model. Both simulation studies and real-world data examples indicate that the proposed method works well.

SIoV Mobility Management using SDVN-enabled Traffic Light Cooperative Framework

Social Internet of Vehicles (SIoV) is an emerging connected vehicular networking framework among specialized social vehicles to share and disseminate important information like traffic updates, weather conditions, parking slots, etc. This study aims to form an SIoV network among emergency vehicles for their frequent communication to improve the throughput, average waiting time, queue length, and speed during vehicular movement while crossing the intersection in the city. To address this, we propose a novel smart traffic light controller-assisted software-defined vehicular networking-enabled SIoV framework for emergency vehicles. Emergency vehicles form an SIoV network by utilizing Software-Defined Vehicular Networking (SDVN) architecture in Vehicle to Vehicle and Vehicle to Infrastructure communication. The SDVN module is used to offer two essential services: 1) SIoV-based road-lane prioritization, and 2) congestion prevention signal generation for the smart traffic light controller. An SDVN-MP algorithm is proposed to generate an effective traffic light control signal with an SDVN controller feedback signal. Furthermore, to improve the SIoV movement in the city, two levels of prioritization: 1) SIoV, and 2) the road lane with SIoV, are done. The first level of prioritization is to assign higher weightage to the social vehicular entities, and the second level is to prioritize the respective road lane based on SIoV quantity. The proposed framework is validated through a realistic simulation study on the Indian city OpenStreetMap utilizing the Simulation of Urban MObility simulator. The experimental findings demonstrate that the SDVN-MP model enhances (state-of-the-art) comparative performance by 22.5% to 55.2%, 1.2% to 82.7%, 1.6% to 38.4%, and 1.8% to 12.4% for average waiting time, average speed, average queue length, and average throughput metrics, respectively.

Advancing Traffic Simulation Precision and Scalability: A Data-Driven Approach Utilizing Deep Neural Networks

In traditional traffic simulation studies, vehicle behavior has typically been modeled using complex analytical frameworks, which often struggle to encompass the full range of variables affecting vehicle operations. Addressing this gap, our research introduces an innovative data-driven framework for traffic simulation that incorporates human driving data into its decision-making processes. This enables the modeling of diverse vehicle behaviors by taking into account both vehicle-specific characteristics and environmental factors. At the core of this framework are two advanced deep neural networks, convolutional long short-term memory and convolutional gated recurrent unit, which underpin our vehicle traffic simulation model. Utilizing datasets from the Next Generation Simulation project, specifically the I-80 and US-101 road sections, our study further evaluates the framework’s performance through single-step continuous prediction, as well as transferability tests, employing the TransMSEloss function to optimize prediction accuracy. Our findings reveal that the proposed data-driven model significantly outperforms traditional models, achieving an exceptional accuracy of 97.22% in training and 95.76% in testing. Notably, in continuous prediction, our model maintains an 89.57% accuracy up to the fifth step, exceeding the traditional framework’s 82.82% by 5% to 10% at each step. Time cost analysis indicates that while the data-driven framework’s advantages are more pronounced in large-scale simulations, it also demonstrates strong transferability, with a 93.48% accuracy on diverse datasets, showcasing its applicability across different traffic scenarios. This study not only highlights the potential of deep learning in traffic simulation, but also sets a new benchmark for accuracy and scalability in the field.

Robust estimation of a regression function in exponential families

We observe n pairs of independent (but not necessarily i.i.d.) random variables X1=(W1,Y1),…,Xn=(Wn,Yn) and tackle the problem of estimating the conditional distributions Qi⋆(wi) of Yi given Wi=wi for all i∈{1,…,n}. Even though these might not be true, we base our estimator on the assumptions that the data are i.i.d. and the conditional distributions of Yi given Wi=wi belong to a one parameter exponential family Q¯ with parameter space given by an interval I. More precisely, we pretend that these conditional distributions take the form Qθ(wi)∈Q¯ for some θ that belongs to a VC-class Θ¯ of functions with values in I. For each i∈{1,…,n}, we estimate Qi⋆(wi) by a distribution of the same form, i.e. Qθ̂(wi)∈Q¯, where θ̂=θ̂(X1,…,Xn) is a well-chosen estimator with values in Θ¯. We establish non-asymptotic exponential inequalities for the upper deviations of a Hellinger-type distance between the true conditional distributions of the data and the estimated one based on the exponential family Q¯ and the class of functions Θ¯ we chose. We show that our estimation strategy is robust to model misspecification, contamination and the presence of outliers. Besides, when the data are truly i.i.d., the exponential family Q¯ is suitably parametrized and the conditional distributions Qi⋆(wi) of the form Qθ⋆(wi)∈Q¯ for some unknown Hölderian function θ⋆ with values in I, we prove that the estimator θ̂ of θ⋆ is minimax (up to a logarithmic factor). Finally, we provide an algorithm for calculating θ̂ when Θ¯ is a VC-class of functions of low or moderate dimension and we carry out a simulation study to compare its performance to that of the MLE and median-based estimators. The proof of our main result relies on an upper bound, with explicit numerical constants, on the expectation of the supremum of an empirical process over a VC-subgraph class. This bound can be of independent interest.

Network pharmacology and in silico approaches to uncover multitargeted mechanism of action of Zingiber zerumbet rhizomes for the treatment of idiopathic pulmonary fibrosis

Background Idiopathic pulmonary fibrosis (IPF) is a disease with high mortality, and there are only two specific drugs available for therapeutic management with limitations. The study aims to identify comprehensive therapeutic mechanisms of Zingiber zerumbet rhizomes (ZZR) to treat IPF by using network pharmacology followed battery of in silico studies. Methods The protein-protein interaction network was developed using Cytoscape to obtain core disease targets involved in IPF and their interactive molecules of ZZR. Based on the pharmacophore properties of phytomolecules from ZZR, the drug targets in IPF were explored. Protein-protein interaction network was built in Cytoscape to screen potential targets and components of ZZR. Molecular docking and dynamics were conducted as an empirical study to investigate the mechanism explored through network pharmacology in relation to the hub targets. Results The network analysis conferred kaempferol derivatives that had demonstrated a promising therapeutic effect on the perturbed, robust network hubs of TGF-β1, EGFR, TNF-α, MMP2 &amp; MMP9 reported to alter the biological process of mesenchymal transition, myofibroblast proliferation, and cellular matrix deposition in pulmonary fibrosis. The phytomolecules of ZZR act on two major significant pathways, namely the TGF-β-signaling pathway and the FOXO-signaling pathway, to inhibit IPF. Confirmational molecular docking and dynamics simulation studies possessed good stability and interactions of the protein-ligand complexes by RMSD, RMSF, rGyr, SASA, and principal component analysis (PCA). Validated molecular docking and dynamics simulations provided new insight into exploring the mechanism and multi-target effect of ZZR to treat pulmonary fibrosis by restoring the alveolar phenotype through cellular networking. Conclusions Network pharmacology and in silico studies confirm the multitargeted activity of ZZR in the treatment of IPF. Further in vitro and in vivo studies are to be conducted to validate these findings.

Enhancing the charging performance of the latent heat storage unit by gradient straight fins

To improve the charging performance of the latent heat storage units, this paper performs a numerical simulation study of the melting of phase change material under various gradient straight fins employing the enthalpy-porous medium model. Based on the evolution of phase interface and temperature distribution, along with the variation of liquid phase fraction and Nusselt number, we analyze the sole effects of gradient-length and gradient-angle on the melting of the phase change material. Further, we gain the optimized-gradient fins by cooperatively adjusting the geometrical parameters of both the gradient-length and gradient-angle using response surface methodology. The introduction of natural convection of the formed liquid divides the melting process into three stages. Optimizing the geometrical parameters of straight fins could facilitate the fast second stage containing natural convection, leaving no (or less) room for the slow third stage without natural convection. Compared with the uniform fin structure, gradient-length and gradient-angle could reduce the total melting time at most by 48.9% and 8%, respectively. The largest reduction (56.6%) exists in the optimized straight fin structure with the gradient-length of 5.56 mm and the gradient-angle of 5.36°, involving only the first two melting stages without the third stage.

Trajectory Control of Designed Experimental Mobile Robot

Due to advancing technology; nowadays mobile robot applications in hospitals have been increased. For that reason, it is very important and necessary to analyze the trajectory of such helping robotic system. However; there are many types of mobile robots have been utilized in hospital applications such as helping nurses. In this simulation study; a designed and controlled mobile robot was controlled by using standard feedback controllers. On the other hand, the robot was also tested with disturbances of ground surface roughness. The simulation results were improved that standard PID controller has superior performance to overcome surface roughness of the robot trajectory.