Volume-11 Issue-3, January 2022 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

C97210111322

IJITEE Journal — Sat, 19 Feb 2022 06:42:39 +0000

The International Journal of Innovative Technology and Exploring Engineering (IJITEE) has ISSN: 2278-3075 (online), an open-access, peer-reviewed, periodical monthly international journal. This journal is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in January, February, March, April, May, June, July, August, September, October, November and December.The journal aims to publish high–quality peer–reviewed original articles in the area of Engineering and Technology that covers Computer Science and Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering and all interdisciplinary streams of Engineering and Technology. #Smart Computing and Information Processing #Signal and Speech Processing #Image Processing and Pattern Recognition #WSN#Artificial Intelligence and machine learning #Data mining and warehousing #Data Analytics #Deep learning #Bioinformatics #High Performance computing #Advanced Computer Networking #Cloud Computing #IoT #Parallel Computing on GPU #Human Computer Interactions #Recent Trends in Microelectronics and VLSI Design #Process & Device Technologies #Low-Power Design #Nanometer-Scale Integrated Circuits #Application specific ICs (ASICs) #FPGAs #Nanotechnology #Nano Electronics and Quantum Computing #3. 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Air gap Eccentricity Analysis in Induction Motor using Decision tree Algorithm
Rama Mishra¹, E. Vijay Kumar²
¹Rama Mishra, Sarvepalli Radhakrishnan University, Bhopal (M.P), India.
²Dr. E Vijay Kumar, Department of Electrical & Electronics Engineering, Sarvepalli Radhakrishnan University, Bhopal (M.P), India.
Manuscript received on January 12, 2022. | Revised Manuscript received on February 12, 2022. | Manuscript published on February 28, 2022. | PP: 85-88 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.C97210111322 | DOI: 10.35940/ijitee.C9721.0111322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper presents yeah the eccentricity Fault in the induction motor. the decision tree and Power spectrum Analysis methods are used to analyze the spectrum. Induction motor is very much like in the industry. It is also used a lot due to its easy control quality. All three-phase induction motors have mismatch eccentricity. Due to this eccentricity problem we got speed pulsation, vibration acoustic noise, and friction problem between stator and rotor. The proposed methodology is useful on Real-time data and achieves 90% true Value. The installation of various Sensors in order to maintain the Good condition of the induction motor is very costly. In the small industry, I would like to avoid this cost. The Status current contains unique fault spectrum components find fault with using a decision tree algorithm .We can easily analyze the air gap eccentricity faults.
Keywords: Induction Motor, Spectrum Analysis, Decision Tree Algorithm, Air Gap Eccentricity, Pattern Recognition
Scope of the Article: Pattern Recognition

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C97130111322

IJITEE Journal — Sat, 19 Feb 2022 06:35:30 +0000

Assessment of Factors Influencing the Survival of Breast Cancer Patients using a Machine Learning Approach
Shivani Motarwar¹, Dixshant Kumar Jha²
¹Shivani Motarwar, School of Electronics Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
²Dixshant Kumar Jha*, School of Electrical Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on January 09, 2022. | Revised Manuscript received on February 14, 2022. | Manuscript published on February 28, 2022. | PP: 80-84 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.C97130111322 | DOI: 10.35940/ijitee.C9713.0111322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Breast cancer is one of the deadliest diseases, claiming approximately 627,000 lives worldwide in 2018–2019. Therefore, early detection of breast cancer through automation in the prediction of the disease will help the medical industry to cure this disease at an early stage and thereby reduce the risk of death drastically. In the present study, the Breast Cancer Wisconsin (Diagnostic) Data Set has been taken from the University of California Irvine (UCI) Machine Learning Repository. The dataset (n=699) contained a total of 30 predictor parameters and one dependent parameter. The dependent variable referred to the type of cancer tissue, i.e., benign or malignant. To predict the type of cancer tissue present in the patient, prediction models were built using 1) Logistic Regression (LR), 2) Decision Tree Classifier (DTC), 3) Random Forest Classifier (RFC), 4) K Nearest Neighbor (KNN), 5) Support Vector Machine (SVM), and 6) Ada Boost Classifier (ABC). To improve the accuracy of the model, a correlation matrix was used and the top 8 features were selected. To improve the accuracy even further, the Synthetic Minority Oversampling Technique (SMOTE) was used to eliminate the problem of class imbalance, and then accuracy was compared before and after SMOTE. The Precision, Recall, and F1 scores are the performance metrics that have been taken into consideration for selecting the best model for the analysis. The results of the study reveal that the KNN algorithm gives the highest accuracy of 95.321% after the SMOTE technique is applied to each of the six algorithms. It has been revealed that while SMOTE aids in the accuracy of some algorithms, it affects the performance of others. This research may be turned into realistic tools that can be utilized in the medical field to more accurately predict the stage of disease for better treatment management.
Keywords: Breast cancer, database, K Nearest Neighbors, Machine learning, random forest, SMOTE.
Scope of the Article: Machine Learning

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L956210101221

IJITEE Journal — Sat, 12 Feb 2022 09:10:45 +0000

A Comparative Study of Circular Water Tank in Two Different Loading Conditions (Seismic and Wind Load Condition) using Staad-Pro
Krishnakant Kanhaiya¹, Ajay Kumar Jain²
¹Krishnakant Kanhaiya*, Pursuing Masters of Engineering in Structural Engineering, National Institute of Technical Teacher’s Training and Research, Bhopal (MP), India.
²Dr. Ajay Kumar Jain, Professor, Department of Civil and Environmental Engineering Education, National Institute of Technical Teacher’s Training and Research, Bhopal (MP), India.
Manuscript received on October 14, 2021. | Revised Manuscript received on February 12, 2022. | Manuscript published on February 28, 2022. | PP: 75-79 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.L956210101221 | DOI: 10.35940/ijitee.L9562.0111322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this paper a comparison of the two loading combinations of a circular water tank i.e. seismic and wind load conditions have been made. The STAAD Pro software has been used to analyze the circular water tank structure and to compare both the loading combinations. Two similar models of a circular water tank were created, provided with the required attributes (i.e. loading conditions, zones etc.) and were analyzed in both, seismic load condition and wind load condition. The comparison of results from both the analyses revealed that, due to lower seismic zones, the effect of earthquake is slightly less than the effect of wind. It was concluded that, wind load condition should be preferred over seismic load condition while designing, because the wind load condition gives safer design in the severe conditions. This can be used for the design of a circular water tank having similar attributes.
Keywords: Circular Water Tank, Seismic Load, Wind load, STAADPro.
Scope of the Article: Behaviour of Structures under Seismic Loads

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C97480211322

IJITEE Journal — Sat, 12 Feb 2022 09:00:10 +0000

Alternative Ways to Control an Object using Plc S7 1200 Controller
Monika Rybczak¹, Damian Radzimski²
¹Monika Rybczak*, Department of Ship Automation, Gdynia Maritime University, Gdynia, Poland.
²Damian Radzimski, Department of Ship Automation, Gdynia, Maritime University, Gdynia, Poland.
Manuscript received on January 24, 2022. | Revised Manuscript received on February 11, 2022. | Manuscript published on February 28, 2022. | PP: 70-74 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.C97480211322 | DOI: 10.35940/ijitee.C9748.0111322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The paper concerns an alternative ways of object control using S7 1200 controller. Author’s solution is based on an external Matlab program with Simulink extension. Standard ways of controlling an object with the use of a controller to control and analyze the operation of this object use either the controller itself or a graphical representation of the system using an HMI operator panel. An alternative way of controlling a three-phase electrical motor using an external software Matlab with Simulink extension was invented and created in the university laboratory. Algorithm of controlling the object has been created in Matlab environment, through which it is possible to control the object and to read parameters of voltage, output current or even torque. The program is fully compatible with TIA Portal software, which uses TSEND_C and TRCV_C function blocks to exchange data with a PC running Matlab. The entire control process is based on the TCP protocol, while the object is connected through the inverter to the controller using the Modbus RTU protocol. A comparison of the presented solution with currently used solutions has been made. At the end of the article, all advantages and disadvantages of the discussed solution are presented, as well as personal comments on the solution.
Keywords: PLC, Matlab, Simulink, Object System Control
Scope of the Article: Nano electronics and Quantum Computing

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C97600211322

IJITEE Journal — Thu, 10 Feb 2022 09:18:10 +0000

Design of 2.4 GHz LNA of 400 MHz Bandwidth
Abhay Chopde¹, Prashik Sadar², Ashutosh Sabale³, Piyush Thite⁴, Raghvendra Zarkar⁵
¹Abhay Chopde*, Department of E & TC Engineering, Vishwakarma Institute of Technology, Pune, India.
²Prashik Sadar, Department of E & TC Engineering, Vishwakarma Institute of Technology, Pune, India.
³Ashutosh Sabale, Department of E & TC Engineering, Vishwakarma Institute of Technology, Pune, India.
⁴Piyush Thite, Department of E & TC Engineering, Vishwakarma Institute of Technology, Pune, India.
⁵Raghvendra Zarkar, Department of E & TC Engineering, Vishwakarma Institute of Technology, Pune, India.
Manuscript received on January 01, 2022. | Revised Manuscript received on February 05, 2022. | Manuscript published on February 28, 2022. | PP: 65-69 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.C97600211322 | DOI: 10.35940/ijitee.C9760.0111322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Low Noise Amplifier (LNA) is the most important front-end block of the receiver. LNA’s Noise figure (NF) and Scattering Parameters affect the overall performance of the whole receiver circuit. Nowadays in the era of 5G technology, The quality of data that is being transmitted is increased. So there is a need for higher bandwidth to transfer data with higher speed. In such a case, communication blocks need an update. The research is carried out for the advancement of the LNA. The primary goal of LNA design is to lower the Noise Figure and return losses. The paper aims to design a 2.4 GHz LNA having a bandwidth of 400 MHz. The circuit is designed with the help of single-stub microstrip lines. We tried to keep the length of microstrip lines as minimum as possible. The transistor ATF-21170 Gallium Arsenide Field Effect Transistor (GaAs FET) is used in this work. The circuit is simulated in the Keysight Advance Design System (ADS). The amplifier is manually designed using standard methods. LNA is unconditionally stable for the frequency range of 2.2 GHz to 2.6 GHz. To build impedance matching circuits of the amplifier smith chart is used. It is observed that the LNA gain (S21) is greater than 15.3 dB, NF less than 1.2 dB, Input return loss (S11) is less than -13.3 dB, Output return loss (S22) is less than -17.1 dB over the 400 MHz bandwidth ranging from 2.2 to 2.6 GHz. This has, to the best of the authors’ knowledge, not been presented in literature before.
Keywords: Cascode, ATF-21170, Frii’s Formula, Miller Effect, Scattering (S) Parameter.
Scope of the Article: Exact And Parameterized Computation

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C97610211322

IJITEE Journal — Thu, 10 Feb 2022 09:09:58 +0000

Music Player using Emotion Recognition
Abhay Chopde¹, Anuradha Lohar², Mitul³, Swaraj Mane⁴, Shaurya Mhaske⁵
¹Abhay Chopde, Department of Electronics & Telecommunication Engineering, Vishwakarma Institute of Technology, Pune (Maharashtra), India.
²Anuradha Lohar*, Department of Electronics & Telecommunication Engineering, Vishwakarma Institute of Technology, Pune (Maharashtra), India.
³Mitul, Department of Electronics & Telecommunication Engineering, Vishwakarma Institute of Technology, Pune (Maharashtra), India.
⁴Swaraj Mane, Department of Electronics & Telecommunication Engineering, Vishwakarma Institute of Technology, Pune (Maharashtra), India.
⁵Shaurya Mhaske, Department of Electronics & Telecommunication Engineering, Vishwakarma Institute of Technology, Pune (Maharashtra), India.
Manuscript received on January 30, 2022. | Revised Manuscript received on February 05, 2022. | Manuscript published on February 28, 2022. | PP: 59-64 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.C97610211322 | DOI: 10.35940/ijitee.C9761.0111322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Expression of humans has a very crucial role in determining the present state and the mood of a person. It helps in the extraction of the emotion by understanding the different features of the face like cheeks, forehead, eyes, or maybe the curvature of the smile. Music plays a crucial role in the daily lifestyle. It is essentially a kind of thing that soothes and calms the body, brain and soul of a human. During this fast-paced lifestyle, everyone goes through a roller coaster of emotions and changes in moods within seconds. People tend to listen to music according to their emotions. Hence, we have chosen emotion and music and combined them together. Our program will first detect facial expressions and using that it will recognize the emotion of a human being. This is done through counting of repeated integers in a trained model and playing music favorable with the mood detected. This will alleviate the mood or just calm the individual. It may also be able to fetch quicker songs consistent with the mood, saving time looking up for different songs. Parallelly developing a software which will be used anywhere with the assistance of providing the functionality of playing music consistent with the emotion detected. People tend to concentrate on songs consistent with their moods, and it feels very frustrating if a tragic song starts playing when you are happy. It seems like a burden to constantly visit the music application to vary the song. Therefore, in this work, we’ve come up with a solution in which we will use computer vision to detect the emotion of an individual and therefore the system shall play a song consistent with the mood of the client.
Keywords: Music, Tensor flow, Emotion, OpenCV.
Scope of the Article: Image Processing and Pattern Recognition

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C97620211322

IJITEE Journal — Sat, 05 Feb 2022 08:48:17 +0000

EAPRAST: Extensive Approach for Product Ranking in Aspect-Based Sentiment Analysis using TRIE
Nibedita Panigrahi¹, Asha T²
¹Nibedita Panigrahi*, Assistant Professor, Department of Information Science & Engineering, RV Institute of Technology, Bangalore, (Karnataka) India.
²Dr. Asha. T, Professor, Department of Computer Science & Engineering, Bangalore, Institute of Technology, Bengaluru, (Karnataka) India.
Manuscript received on January 30, 2022. | Revised Manuscript received on February 03, 2022. | Manuscript published on February 28, 2022. | PP: 51-58 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.C97620211322 | DOI: 10.35940/ijitee.C9762.0111322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: To assist prospective consumers make educated purchasing choices, we are analyzing and mining data from product reviews on online shopping websites. Two methods exist for extracting aspects. Rule-based and Highest Adjective Count (HAC) algorithms. The aspect ranking will use MAX opinion score method and enhanced SentiWordNet opinion score. SentiWordNet uses a hash map structure to turn keys into tiny values that may be used to index data. Hashing can search, insert, and remove in O(L) time. The disadvantage is that if two keys give the same hashCode value, the hashMap’s speed suffers. When HashMap buckets are full, they need to be resized. We replaced it with TRIE, which can insert and locate strings in O(L) time, where L is the word length. TRIE is quicker than Hashing because of its implementation. Here hash function and collision handling is not required (like we do in open addressing and separate chaining). TRIE also allows us to print all words in alphabetical order, which is not feasible using hashing. TRIE can effectively search for prefixes. On the other hand, we offer a method that ranks items based on their similarity in terms of features and price. The Suggested method is applied to three conventional databases such as Amazon, Yelp, and IMDB and the solution provides a more effective and dependable online buying experience.
Keywords: Sentiment Analysis, Natural Language Processing, Product Ranking, TRIE Algorithm, Hash Map
Scope of the Article: Natural Language Processing

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C97520211322

IJITEE Journal — Fri, 04 Feb 2022 06:27:08 +0000

Analysis of Air Traffic Management Models
Shankaramma¹, Supreetha H V², Nagaraj G S³
¹Shankaramma*, M.Tech, Department of Computer Science and Engineering, Rashtreeya Vidyalaya College of Engineering, Bengaluru, (Karnataka), India.
²Supreetha H V, M.Tech, Department of Computer Science and Engineering, Rashtreeya Vidyalaya College of Engineering, Bengaluru, (Karnataka), India.
³Prof. Nagaraj G. S, Associate Dean, Department of Computer Science and Engineering, Rashtreeya Vidyalaya College of Engineering, Bengaluru, (Karnataka), India..
Manuscript received on January 25, 2022. | Revised Manuscript received on January 31, 2022. | Manuscript published on February 28, 2022. | PP: 47-50 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.C97520211322 | DOI: 10.35940/ijitee.C9752.0111322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The high growth of air traffic flow has increased more bottleneck traffic issues in the air traffic management (ATM) system. The challenges between flight flow, air traffic control service and airspace are the major key parameters which support capability of domestic and international air transportation need to be looked by stakeholders. Many models are designed to incorporate to address the potential bottleneck issues of ATM. However, in these models’ analysis was not clearly presented. The proposed research review paper presents an analysis and insights of different models used in an air traffic management which includes, Big Data, Artificial Neural Network, Cloud Computing and Enterprise models.
Keywords: Analysis, Air Traffic Management, Artificial Neural Network, Big Data, Cloud Computing, Enterprise Model.
Scope of the Article: Big Data Networking

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C97410111322

IJITEE Journal — Thu, 03 Feb 2022 12:07:04 +0000

Housing Price Prediction with Machine Learning
Amena Begum¹, Nishad Jahan Kheya², Md. Zahidur Rahman³
¹Amena Begum, Department of ICT, Comilla University, Cumilla , Bangladesh.
²Nishad Jahan Kheya, Department of ICT, Comilla University, Cumilla, Bangladesh.
³Md. Zahidur Rahman*, Department of CSE, Britannia University, Cumilla, Bangladesh.
Manuscript received on January 20, 2022. | Revised Manuscript received on January 01, 2022. | Manuscript published on February 28, 2022. | PP: 42-46 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.C97410111322 | DOI: 10.35940/ijitee.C9741.0111322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: For socioeconomic development and the well-being of citizens, developing a precise model for predicting housing prices is always required. So that, a real estate broker or a house seller/buyer can get an intuition in making well-knowledgeable decisions from the model. In this work, a various set of machine learning algorithms such as Linear Regression, Decision Tree, Random Forest are being implemented to predict the housing prices using available datasets. The housing datasets of 506 samples and 13 feature variables from January 2015 to November 2019 were taken from the StatLib library which is maintained at Carnegie Mellon University. Since housing price is emphatically connected to different factors like location, area, the number of rooms; it requires all of this information to predict individual housing prices. This paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models to explore various impacts of features on prediction methods. This paper will also provide an optimistic result for housing price prediction by comprehensively validating multiple techniques in model execution on regression.
Keywords: Prediction, Machine Learning, Linear Regression, Random Forest, Decision Tree.
Scope of the Article: Machine Learning

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C97430111322

IJITEE Journal — Sat, 29 Jan 2022 11:49:59 +0000

Analysis of Variable Viscosity and Thermal Conductivity of MHD flow of Mixed Convection over a Nonlinear Vertical Stretching Sheet
Geeti Gogoi
Geeti Gogoi*, Department of Mathematics, Assam University, Assam, India.
Manuscript received on January 20, 2022. | Revised Manuscript received on January 27, 2022. | Manuscript published on February 28, 2022. | PP: 36-41 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.C97430111322 | DOI: 10.35940/ijitee.C9743.0111322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this present study we examine analytically the MHD flow problem close by a stagnation point on a stretching sheet which is nonlinear. Similarity transformation is used to modify the fluid flow governing equations into a system of ordinary differential equations .The modified equations are resolved with the assist of MATLAB bvp4c. The influence of viscosity parameter, thermal conductivity parameter, suction parameter and magnetic parameter on velocity and temperature are computed and presented through graphs.
Keywords: Bvp4c, MHD flow, Stretching Sheet, Thermal Conductivity, Variable Viscosity.
Scope of the Article: Analysis of Variable Viscosity

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