Computational Local Mobility Analysis of Mechanisms With Higher Kinematic Pairs

2016 ◽  
Author(s):  
Andreas Müller
Keyword(s):  
Computational Algorithm ◽  
Computational Approach ◽  
Computational Method ◽  
Canonical Coordinates ◽  
Mobility Analysis ◽  
Local Mobility ◽  
Lower Pair ◽  
Order Constraints ◽  
Algebraic Operations ◽  
Higher Kinematic Pairs

The finite mobility of a mechanism is reflected by its configuration space (c-space), and the mobility analysis aims at determining this c-space. Crucial for the computational mobility analysis is an adequate formulation of the constraints. For lower pair linkages an analytic formulation is the product-of-exponential (POE) formula in terms of the screw systems of the lower pair joints. In other words, the screw coordinates of a lower pair joint serve as canonical coordinates on the corresponding motion subgroup. For such linkages, a computational approach to the local mobility analysis has been reported recently. The approach is applicable to general multi-loop linkages. Higher pairs do not generate motion subgroups so that their motion cannot be expressed in terms of screw coordinates. Hence their kinematics cannot be expressed in terms of a POE, and there is no efficient and generally applicable computational method for the mobility analysis. In this paper a formulation of higher-order constraints for mechanisms with higher pair joints is proposed making use of the result for lower pair linkages. The method is applicable to mechanisms where each fundamental loop comprises no more than one higher pair, which covers the majority of mechanisms. Based on this, a computational algorithm is introduced that allows mobility determination. As for lower pair linkages, this algorithm only requires simple algebraic operations.

Position Analysis of Spatial Mechanisms

1984 ◽  
Vol 106 (2) ◽  
pp. 252-255 ◽  
Author(s):  
J. Llinares ◽  
A. Page
Keyword(s):  
Computational Algorithm ◽  
Matrix Notation ◽  
Position Analysis ◽  
Spatial Mechanisms ◽  
Lower Pair ◽  
Simple Equations

In this paper matrix notation is used to develop a computational algorithm for position analysis of spatial mechanisms having revolute (R) and prismatic (P) pairs. Since all lower pairs are equivalent to some combination of R and P pairs, the method works for spatial mechanisms containing any lower pair. By using the method, spatial mechanisms are describable by simple equations which are easily programmable.

Mobility Change in Two Types of Metamorphic Parallel Mechanisms

2009 ◽  
Vol 1 (4) ◽  
Author(s):  
Dongming Gan ◽  
Jian S. Dai ◽  
Qizheng Liao
Keyword(s):  
Parallel Mechanism ◽  
Type A ◽  
Parallel Mechanisms ◽  
Mobility Analysis ◽  
Topological Change ◽  
Mobility Factor ◽  
Local Mobility

This paper presents a new joint coined as the rT joint and proposes two types of metamorphic parallel mechanisms assembled with this rT joint. In the first type, the mechanism changes its topology by turning the rT joints in all limbs into different configurations. This change in mobility is completed by two cases illustrated by a 3(rT)PS metamorphic parallel mechanism having variable mobility from 3 to 6 and a 3(rT)P(rT) parallel mechanism having various configurations including pure translations, pure rotations, and mobility 4. In the second type, a central strut with the rT joint is added in a parallel mechanism. The variable mobility of the mechanism results from the topological change of the central (rT)P(rT) strut. This is illustrated in a 3SPS-1(rT)P(rT) metamorphic parallel mechanism, which changes its mobility from 4 to 5. It is demonstrated in mobility analysis that the change in local mobility of each limb results in the change in the platform mobility that a metamorphic process can be achieved. This particular analysis leads to advancement of improved Grübler–Kutzbach criterion by introducing the local mobility factor in the mobility analysis.

scholarly journals Error Upper Bounds for a Computational Method for Nonlinear Boundary and Initial-Value Problems

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Osvaldo Guimarães ◽  
José Roberto C. Piqueira
Keyword(s):  
Hilbert Space ◽  
Initial Value Problems ◽  
Nonlinear Boundary ◽  
Upper Bounds ◽  
Computational Approach ◽  
Computational Method ◽  
Operational Matrices ◽  
Initial Value ◽  
Accuracy Measures

This work develops a computational approach for boundary and initial-value problems by using operational matrices, in order to run an evolutive process in a Hilbert space. Besides, upper bounds for errors in the solutions and in their derivatives can be estimated providing accuracy measures.

scholarly journals A Computational Method Involving Surface Area to Volume Ratio to Estimate Inorganic Nanoparticle Efficacy

2021 ◽  
Author(s):  
Wesley A. Williams ◽  
Ashley J. Denslow ◽  
Peter W. Radulovic ◽  
Daniel J. Denmark ◽  
Shyam S. Mohapatra
Keyword(s):  
Operating System ◽  
Drug Delivery ◽  
Surface Area ◽  
Imaging Modality ◽  
Drug Loading ◽  
Volume Ratio ◽  
Computational Approach ◽  
Inorganic Nanoparticles ◽  
Computational Method ◽  
Simultaneous Sensing

Inorganic nanoparticles are utilized for therapeutic, diagnostic, or theranostic purposes and the latter involve simultaneous sensing, imaging, or tracking of drug delivery. Further, these nanoparticles differ in their morphologies, which affect outcomes such as the effectiveness of hyperthermia, induction, drug loading, circulation time by escaping the body's immune system, imaging modality clarity, and biosensing. However, design of these theranostics is limited by the lack of a method to predict their therapeutic efficacy. Herein, we report a computational approach involving the surface area (SA) to volume (V) ratios (SA:V), which can help predict the efficacy of the inorganic nanoparticles. The approach comprises a coding platform for the comparator pro-gram and uses a Python 3 on a Windows 10 operating system. Analyses of 22 polyhedral morphologies that inorganic nanoparticles could assume ex silico showed that only particular concave morphologies in this size regime are more productive over the standard sizes. Our results provide a method that can aid in the predicting efficacy of inorganic nanoparticles with certain morphology.

scholarly journals Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach (Preprint)

2020 ◽  
Author(s):  
Junaed Younus Khan ◽  
Md Tawkat Islam Khondaker ◽  
Iram Tazim Hoque ◽  
Hamada R H Al-Absi ◽  
Mohammad Saifur Rahman ◽  
...  
Keyword(s):  
Language Processing ◽  
Scientific Literature ◽  
Treatment Plan ◽  
Scientific Evidence ◽  
Clinical Status ◽  
Confidence Score ◽  
Computational Approach ◽  
Computational Method ◽  
Literature Mining ◽  
Novel Approach

BACKGROUND Novel coronavirus disease 2019 (COVID-19) is taking a huge toll on public health. Along with the non-therapeutic preventive measurements, scientific efforts are currently focused, mainly, on the development of vaccines and pharmacological treatment with existing drugs. Summarizing evidences from scientific literatures on the discovery of treatment plan of COVID-19 under a platform would help the scientific community to explore the opportunities in a systematic fashion. OBJECTIVE The aim of this study is to explore the potential drugs and biomedical entities related to coronavirus related diseases, including COVID-19, that are mentioned on scientific literature through an automated computational approach. METHODS We mined the information from publicly available scientific literature and related public resources. Six topic-specific dictionaries, including human genes, human miRNAs, diseases, Protein Databank, drugs, and drug side effects, were integrated to mine all scientific evidence related to COVID-19. We employed an automated literature mining and labeling system through a novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis, and deep learning. We also applied the concept of cosine similarity to confidently infer the associations between diseases and genes. RESULTS Based on the literature mining, we identified 1805 diseases, 2454 drugs, 1910 genes that are related to coronavirus related diseases including COVID-19. Integrating the extracted information, we developed the first knowledgebase platform dedicated to COVID-19, which highlights potential list of drugs and related biomedical entities. For COVID-19, we highlighted multiple case studies on existing drugs along with a confidence score for their applicability in the treatment plan. Based on our computational method, we found Remdesivir, Statins, Dexamethasone, and Ivermectin could be considered as potential effective drugs to improve clinical status and lower mortality in patients hospitalized with COVID-19. We also found that Hydroxychloroquine could not be considered as an effective drug for COVID-19. The resulting knowledgebase is made available as an open source tool, named COVID-19Base. CONCLUSIONS Proper investigation of the mined biomedical entities along with the identified interactions among those would help the research community to discover possible ways for the therapeutic treatment of COVID-19.

scholarly journals KSIMC: Predicting Kinase–Substrate Interactions Based on Matrix Completion

2019 ◽  
Vol 20 (2) ◽  
pp. 302 ◽  
Author(s):  
Jingzhong Gan ◽  
Jie Qiu ◽  
Canshang Deng ◽  
Wei Lan ◽  
Qingfeng Chen ◽  
...  
Keyword(s):  
Scientific Literature ◽  
State Of The Art ◽  
Matrix Completion ◽  
Computational Approach ◽  
Computational Method ◽  
Substrate Interactions ◽  
Kinase Substrate ◽  
Cellular Processes ◽  
Substrate Interaction ◽  
The Matrix

Protein phosphorylation is an important chemical modification catalyzed by kinases. It plays important roles in many cellular processes. Predicting kinase–substrate interactions is vital to understanding the mechanism of many diseases. Many computational methods have been proposed to identify kinase–substrate interactions. However, the prediction accuracy still needs to be improved. Therefore, it is necessary to develop an efficient computational method to predict kinase–substrate interactions. In this paper, we propose a novel computational approach, KSIMC, to identify kinase–substrate interactions based on matrix completion. Firstly, the kinase similarity and substrate similarity are calculated by aligning sequence of kinase–kinase and substrate–substrate, respectively. Then, the original association network is adjusted based on the similarities. Finally, the matrix completion is used to predict potential kinase–substrate interactions. The experiment results show that our method outperforms other state-of-the-art algorithms in performance. Furthermore, the relevant databases and scientific literature verify the effectiveness of our algorithm for new kinase–substrate interaction identification.

scholarly journals A computational method for free time optimal control problems, with application to maximizing the range of an aircraft-like projectile

1987 ◽  
Vol 28 (3) ◽  
pp. 393-413 ◽  
Author(s):  
K. L. Teo ◽  
G. Jepps ◽  
E. J. Moore ◽  
S. Hayes
Keyword(s):  
Optimal Control ◽  
Computational Algorithm ◽  
Optimal Control Problems ◽  
Computational Method ◽  
Control Problems ◽  
Control Parametrization ◽  
Final Time ◽  
Final State ◽  
Method Of Solution ◽  
Time Optimal

AbstractA class of non-standard optimal control problems is considered. The non-standard feature of these optimal control problems is that they are of neither fixed final time nor of fixed final state. A method of solution is devised which employs a computational algorithm based on control parametrization techniques. The method is applied to the problem of maximizing the range of an aircraft-like gliding projectile with angle of attack control.

scholarly journals Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach

10.2196/21648 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e21648
Author(s):  
Junaed Younus Khan ◽  
Md Tawkat Islam Khondaker ◽  
Iram Tazim Hoque ◽  
Hamada R H Al-Absi ◽  
Mohammad Saifur Rahman ◽  
...  
Keyword(s):  
Language Processing ◽  
Scientific Literature ◽  
Treatment Plan ◽  
Scientific Evidence ◽  
Clinical Status ◽  
Confidence Score ◽  
Computational Approach ◽  
Computational Method ◽  
Literature Mining ◽  
Novel Approach

Background Novel coronavirus disease 2019 (COVID-19) is taking a huge toll on public health. Along with the non-therapeutic preventive measurements, scientific efforts are currently focused, mainly, on the development of vaccines and pharmacological treatment with existing drugs. Summarizing evidences from scientific literatures on the discovery of treatment plan of COVID-19 under a platform would help the scientific community to explore the opportunities in a systematic fashion. Objective The aim of this study is to explore the potential drugs and biomedical entities related to coronavirus related diseases, including COVID-19, that are mentioned on scientific literature through an automated computational approach. Methods We mined the information from publicly available scientific literature and related public resources. Six topic-specific dictionaries, including human genes, human miRNAs, diseases, Protein Databank, drugs, and drug side effects, were integrated to mine all scientific evidence related to COVID-19. We employed an automated literature mining and labeling system through a novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis, and deep learning. We also applied the concept of cosine similarity to confidently infer the associations between diseases and genes. Results Based on the literature mining, we identified 1805 diseases, 2454 drugs, 1910 genes that are related to coronavirus related diseases including COVID-19. Integrating the extracted information, we developed the first knowledgebase platform dedicated to COVID-19, which highlights potential list of drugs and related biomedical entities. For COVID-19, we highlighted multiple case studies on existing drugs along with a confidence score for their applicability in the treatment plan. Based on our computational method, we found Remdesivir, Statins, Dexamethasone, and Ivermectin could be considered as potential effective drugs to improve clinical status and lower mortality in patients hospitalized with COVID-19. We also found that Hydroxychloroquine could not be considered as an effective drug for COVID-19. The resulting knowledgebase is made available as an open source tool, named COVID-19Base. Conclusions Proper investigation of the mined biomedical entities along with the identified interactions among those would help the research community to discover possible ways for the therapeutic treatment of COVID-19.

scholarly journals A new computational algorithm for the solution of second order initial value problems in ordinary differential equations

2018 ◽  
Vol 3 (1) ◽  
pp. 167-174 ◽  
Author(s):  
P.K. Pandey
Keyword(s):  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Initial Value Problems ◽  
Computational Algorithm ◽  
Second Order ◽  
Computational Method ◽  
Computationally Efficient ◽  
Initial Value ◽  
Computational Performance ◽  
Non Linear

AbstractIn this article, we propose a new computational method for second order initial value problems in ordinary differential equations. The algorithm developed is based on a local representation of theoretical solution of the second order initial value problem by a non-linear interpolating function. Numerical examples are solved to ensure the computational performance of the algorithm for both linear and non-linear initial value problems. From the results we obtained, the algorithm can be said computationally efficient and effective.

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