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An Energy-Efficient Task Scheduling using BAT Algorithm for Cloud Computing

Authors:

Arif Ullah, Umeriqbal, Ijaz Ali Shoukat, Abdul Rauf, O Y Usman, Sheeraz Ahmed, Zeeshan Najam

DOI NO:

https://doi.org/10.26782/jmcms.2019.08.00050

Abstract:

Cloud computing is new style of technology the demand of end user increase day by day it cases more energy consumption.Energy consumption directly connected with the utilization of resource .Batter resource management reduce energy system in the network for that reason in this paper BATalgorithm implement for load balancing technique with different parameter it result compare with ABC algorithm. By implementing BAT algorithm in VM policy it reduces 3% of energy consumption in the network. This result can be achieved by implementing proper load balancing technique due to that it can reduce energy management system in cloud computing.

Keywords:

Cloud computing,Energy Management System,Virtualmachine,loadbalancing,Energy Consumption,

Refference:

I. Almasi, S., &Pratx, G. (2019). Cloud computing for big data. Big Data in
Radiation Oncology
II. Aldakheel, E. A. (2011). A cloud computing framework for computer science
education (Doctoral dissertation, Bowling Green State University)
III. Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource
allocation heuristics for efficient management of data centers for cloud
computing. Future generation computer systems, 28(5), 755-768
IV. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., &Brandic, I. (2009). Cloud
computing and emerging IT platforms: Vision, hype, and reality for delivering
computing as the 5th utility. Future Generation computer systems, 25(6), 599-
616.
V. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., &Brandic, I. (2009). Cloud
computing and emerging IT platforms: Vision, hype, and reality for delivering
computing as the 5th utility. Future Generation computer systems, 25(6), 599-
616.

VI. Tso, F. P., White, D. R., Jouet, S., Singer, J., &Pezaros, D. P. (2013, July). The
glasgow raspberry pi cloud: A scale model for cloud computing infrastructures.
In 2013 IEEE 33rd International Conference on Distributed Computing Systems
Workshops (pp. 108-112). IEEE.
VII. Erker, S., Lichtenwoehrer, P., Zach, F., &Stoeglehner, G. (2019).
Interdisciplinary decision support model for grid-bound heat supply systems in
urban areas. Energy, Sustainability and Society, 9(1), 11.
VIII. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., &Brandic, I. (2009). Cloud
computing and emerging IT platforms: Vision, hype, and reality for delivering
computing as the 5th utility. Future Generation computer systems, 25(6), 599-
616.
IX. Eager, D. L., Lazowska, E. D., &Zahorjan, J. (1986). Adaptive load sharing in
homogeneous distributed systems. IEEE transactions on software engineering,
(5), 662-675.
X. Ghomi, E. J., Rahmani, A. M., &Qader, N. N. (2017). Load-balancing algorithms
in cloud computing: a survey. Journal of Network and Computer
Applications, 88, 50-71.
XI. Gupta, P., Seetharaman, A., & Raj, J. R. (2013). The usage and adoption of cloud
computing by small and medium businesses. International Journal of Information
Management, 33(5), 861-874
XII. Lawrence, W. J., & Chang, M. J. K. (2018). cloud computing and Virtualization:
the “Entrepreneur without Borders” Workbench for 21st century Enterprise
development. GSTF Journal on Computing (JoC), 1(1).
XIII. Mushtaq, M. F., Akram, U., Khan, I., Khan, S. N., Shahzad, A., & Ullah, A.
(2017). Cloud computing environment and security challenges: A
review. International Journal of Advanced Computer Science and
Application, 8(10), 183-195.
XIV. Mell, P., &Grance, T. (2011). The NIST definition of cloud computing.
XV. Ronchi, A. M. (2019). Interaction Design Essentials. In e-Citizens (pp. 125-156).
Springer, Cham.
XVI. Ullah, A., Nawi, N. M., Shahzad, A., Khan, S. N., &Aamir, M. (2017). An Elearning
System in Malaysia based on Green Computing and Energy
Level. JOIV: International Journal on Informatics Visualization, 1(4-2), 184-187.
XVII. Umar, S., & Baseer, S. (2016, August). Perception of cloud computing in
universities of Peshawar, Pakistan. In 2016 Sixth International Conference on
Innovative Computing Technology (INTECH) (pp. 87-91). IEEE.

XVIII. Subashini, S., &Kavitha, V. (2011A). A survey on security issues in service
delivery models of cloud computing. Journal of network and computer
applications, 34(1), 1-11.
XIX. Subashini, S., &Kavitha, V. (2011B). A survey on security issues in service
delivery models of cloud computing. Journal of network and computer
applications, 34(1), 1-11.

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Analysis and Prediction of Heart Attacks Based on Design of Intelligent Systems

Authors:

Sozan Sulaiman Maghdid, Tarik Ahmed Rashid, Sheeraz Ahmed, Khalid Zaman, M.Khalid Rabbani

DOI NO:

https://doi.org/10.26782/jmcms.2019.08.00051

Abstract:

Nowadays, artificial intelligence systems become actively used for the identification of different diseases using their medical data. Most of existing traditional medical systems are based on the knowledge of experts-doctors. In this thesis, the application of soft computing elements is considered to automate the process of diagnosing diseases, in particularly diagnosing of a heart attack. The research work will offer probable help to the medical practitioners and healthcare sector in making instantaneous resolution during the diagnosis of the diseases. The intelligent system will predict heart attacks from the patient dataset utilizing algorithms and help doctors in making diagnose of these illnesses. In this study, three techniques such as a neural network (back propagation), Fuzzy Inference System (FIS) and Adaptative Neuro-Fuzzy System (ANFIS) are considered for the design of the prediction system. The systems are designed using data sets. The data sets contain 1319 samples that includes 8 input attributes and one output. The output refers presence of a heart attack in the patient. For comparative analysis, the simulation results of the ANFIS model is compared with the simulation results of the neural network-based prediction model. The ANFIS model has shown better performance and outperformed NN based model. The obtained simulation results demonstrate the efficiency of using ANFIS model in the identification of heart attacks.

Keywords:

Artificial neural network,adaptive neuro-fuzzy inference system,fuzzy inference System (FIS),neural network (back propagation),heart attack,

Refference:

I. Adeli, A., &Neshat, M. (2010, March). A fuzzy expert system for heart disease
diagnosis. In Proceedings of International Multi Conference of Engineers and
Computer Scientists, Hong Kong (Vol. 1).
II. American Heart Association (2015). Heart.org/answers by heartNational
Center7272.Greenville.Ave.Dallas, TX 75231Customer Service
1-800-AHA-USA-11-800-242-8721 from https://www.heart.org/en/healthtopics/
consumer-healthcare/answers-by-heart-fact-sheets/answers-by-heart-factsheets-
lifestyle-and-risk-reduction /
III. Feshki, M. G., &Shijani, O. S. (2016, April). Improving the heart disease
diagnosis by evolutionary algorithm of PSO and Feed Forward Neural Network.
In Artificial Intelligence and Robotics (IRANOPEN), 2016 (pp. 48-53). IEEE.
IV. Haykin S (2009) Neural network and machine learning.3rd edCopyright © 2009
by Pearson Education, Inc., Upper Saddle River, New Jersey 07458. Pearson
Prentice Hall, New York ISBN-13: 978-0-13-147139-9 ISBN-10: 0-13-147139-2.
V. Teng, H., Liu, X., Liu, A., Shen, H., Huang, C., & Wang, T. (2018). Adaptive
transmission power control for reliable data forwarding in sensor based
networks. Wireless Communications and Mobile Computing, 2018.
VI. Narcisse, M. R., Rowland, B., Long, C. R., Felix, H., &McElfish, P. A. (2019).
Heart Attack and Stroke Symptoms Knowledge of Native Hawaiians and Pacific
Islanders in the United States: Findings From the National Health Interview
Survey. Health promotion practice, 1524839919845669.

VII. Takdastan, A., Mirzabeygi, M., Yousefi, M., Abbasnia, A., Khodadadia, R.,
Soleimani, H., …&Naghan, D. J. (2018). Neuro-fuzzy inference system
Prediction of stability indices and Sodium absorption ratio in Lordegan rural
drinking water resources in west Iran. Data in brief, 18, 255-261.
VIII. Nicole, O., Bell, D. M., Leste-Lasserre, T., Doat, H., Guillemot, F., &Pacary, E.
(2018). CaMKIIβ regulates nucleus-centrosome coupling in locomoting neurons
of the developing cerebral cortex. Molecular psychiatry, 23(11), 2111.
IX. Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE
transactions on systems, man, and cybernetics, 23(3), 665-685.
X. Kaya, E., Oran, B., &Arslan, A. (2011). A diagnostic fuzzy rule-based system for
congenital heart disease. World academy of science, Engineering and
technology, 78, 253-256.
XI. Patil, S. B., &Kumaraswamy, Y. S. (2009). Intelligent and effective heart attack
prediction system using data mining and artificial neural network. European
Journal of Scientific Research, 31(4), 642-656.
XII. Shanthi. S (February 2017). Customized Prediction of Heart Disease with
Adaptive Neuro Fuzzy Inference System International Journal of Advanced
Research in Computer and Communication Engineering, SO 3297:2007 Certified
XIII. Shinde, P. P., Oza, K. S., &Kamat, R. K (2016). An Analysis of Data Mining
Techniques in Aggregation with Real Time Dataset for the Prediction of Heart
Disease. InternationalJournal of Control Theory and Applications, 9(20), 327-336.
XIV. Sundar, N. A., Latha, P. P., & Chandra, M. R. (2012). Performance analysis of
classification data mining techniques over heart disease database. IJESAT]
International Journal of engineering science & advanced technology ISSN,
2250-3676.
XV. Vipul, A. S. (2009). Adaptive Neuro-Fuzzy Inference System for Effect of Wall
Capacitance in a Batch Reactor. Advances in Fuzzy Mathematics ISSN, 69-75.
XVI. WHO (2015). Cardiovascular diseases. Retrieved August 28, 2015 from
http://www.who.int/ media centre/factsheets/fs317/en /.
XVII. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353
XVIII. Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex
systems and decision processes. IEEE Transactions on systems, Man, and
Cybernetics, (1), 28-44.

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The Space – Time is Flat at an Absolute Free Space. It is the Mass that Makes Space – Time Curved in. The Physical Time is Discrete or Continuous is An Observer Dependent Realism only

Authors:

Prasenjit Debnath

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00001

Abstract:

According to Einstein, the astronomical bodies try to move in a straight line – it is the curved space – time that makes their paths curved in. This paper proposes that the space – time is originally a flat space – time (at an absolute free space), it is the presence of mass that makes space – time curved in. Whether the physical time is discrete or continuous, is an observer dependent realism only. An observer like human being uses neither too small units of time nor too big units of time. An observer like human being uses average or moderate units of time which makes time continuous and flat. The physical time is discrete and flat for too small units of time. The physical time is continuous and curved in for too big units of time. The space – time can be curved in into a point for infinite mass concentrated into a point. Theoretically, it should be the center of our universe.

Keywords:

Absolute free space,Discrete,Continuous,The physical time,Infinite mass,

Refference:

I. Roger Penrose, “Cycles of Time”, Vintage Books, London, pp. 50-56
II. Stephen Hawking, “The Beginning of Time”, A Lecture.
III. Stephen Hawking, “A Briefer History of Time”, Bantam Books, London, pp.
1-49.
IV. Stephen Hawking, “Black holes and Baby Universes and other essays”,
Bantam Press, London 2013, ISBN 978-0-553-40663-4
V. Stephen Hawking, “The Grand Design”, Bantam Books, London 2011
VI. Stephen Hawking, “A Brief History of Time”, Bantam Books, London 2011,
pp. 156-157. ISBN-978-0-553-10953-5
VII. Stephen Hawking, “The Universe in a Nutshell”, Bantam Press, London
2013, pp. 58-61, 63, 82-85, 90-94, 99, 196. ISBN 0-553-80202-X
VIII. Stephen Hawking, “A stubbornly persistent illusion-The essential scientific
works of Albert Einstein”, Running Press Book Publishers, Philadelphia,
London 2011.
IX. Stephen Hawking, “Stephen Hawking’s Universe: Strange Stuff Explained”,
PBS site on imaginary time.

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Characterization of Individual Mobility and Society Using CDR Data

Authors:

Mohammed Zohdy Abdulhady, Loay E. George

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00002

Abstract:

Through the previous years, a large number of cell phones information has become in the hand for the analysis patterns of people movements. This information’s carry a massive assurance for realizing behavior of human on a very large scale, as well as with an accuracy and precision never happened before can be allowed with surveys, censuses or other available data selection techniques. There are a number of researches that has open key advance into analyzing mobility of human utilizing this available recent data source, as well as there have been multiple various calculations of mobility applied. Mobility of human, or motion over large or short distances for narrow or vast durations of time, is an essential until continuous study for occurrence in the sciences of demographic and social systems. Meanwhile there have been harmonious progresses in compassionate migration (consider continuous pattern of mobility) as well as its effect on people happiness, social organizations, economic, and political organization, progresses in researches of mobility have been embarrass by complexity in measuring and recording how people move on a second and in detailed range. In this paper, the ability of using mobile network records will been described for analyzing the mobility of people and society for various objectives such as monitoring the mobility in cities and builds the suitable infrastructure for them. The mobility of individuals will be very benefit for observation the behavior of peoples and their effect in security issues. In order to test the system performance, a set of tests was applied on Zain calls dataset. The results indicates for the society mobility has been exported for the Baghdad Karkh area peoples. The results have been exported for two phases, one phases when the number of people’s routes where only 10 movement and the second phase when the people routes where 3 routes.

Keywords:

Call phones,Mobile network records,Mobility,Human's behavior,Zain,

Refference:

I. Chen Zhou, Xu, Z., & Huang, B., ”Activity Recognition from Call Detail Record:
Relation Between Mobile Behavior Pattern And Social Attribute Using
Hierarchical Conditional Random Fields”,International Conference on Green
Computing and Communications & IEEE/ACM International Conference on
Cyber, Physical and Social Computing, 2010.

II. Ghotekar, N., “Analysis and Data Mining of Call Detail Records using Big Data
Technology”, International Journal of Advanced Research in Computer and
Communication Engineering, Vol. 5, December 2016.
III. M.Donato, K..,”Current trends and patterns of female migration: Evidence from
Mexico”. International Migration Review, 27(4), 748-771, 1993.
IV. Massey DS.,”Social structure, household strategies, and the cumulative causation
of migrateon”. Population Index. 56:3–26. 10.2307/3644186, 1990.
V. Massey, D. S., & Espinosa, K. E., ”What’s driving Mexico-US migration? A
theoretical, empirical, and policy analysis”, American journal of sociology,
102(4), 939-999, 1997.
VI. Massey, D. S., Williams, N., Axinn, W. G., & Ghimire, D. J., ”Community
services and out-migration”. International Migration. 48(3), 1-41, 2010.
VII. Martin B.,”Mean and Standard Deviation”, report of applied statics, 2006
VIII. R., .Harris, J. & Todaro, M. P., ”Migration, unemployment and development: a
two-sector analysis” , The American economic review, 126-142, 1970.
IX. Ratul Sikder, Uddin, M. J., & Halder, S., ”An Efficient Approach of Identifying
Tourist by Call Detail Record Analysis” International Workshop on
Computational Intelligence (IWCI) 12-13 Dhaka, Bangladesh, December 2016.
X. S. Massey, D., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A., & Taylor, J.
E., ”Theories of international migration: A review and appraisal”, Population
and development review, 431–466, 1993.
XI. Stark, O., & Bloom, D. E., ”The new economics of labor migration”. The
american Economic review, 75(2), 173-178.1985.
XII. Stark O, Taylor JE. ”Migration incentives, migration types: The role of relative
deprivation”. The Economic Journal. 101:1163–1178. 10.2307/2234433, 1985.
XIII. Sara B. Elagib, Hashim, A. H. A., & Olanrewaju, R. F.”CDR Analysis using Big
Data Technology”, International Conference on Computing, Control,
Networking, Electronics and Embedded Systems Engineering, 2015
XIV. W. Kandel, J .Durand, Parrado, E. A., & Massey, D. S., ”International
migration and development in Mexican communities”. Demography, 33(2), 249-
264, 1996.
XV. Xuzhao Wang, Dong, H., Zhou, Y., Liu, K., Jia, L., & Qin, Y., ”Travel Distance
Characteristics Analysis Using Call Detail Record Data”, 29th Chinese Control
And Decision Conference (CCDC), 2017.
XVI. Zhang, S., Yin, D., Zhang, Y., & Zhou, W., “Computing on Base Station
Behavior Using Erlang Measurement and Call Detail Record”, IEEE transactions
on emerging topics in computing, 3(3), 444-453 2015.

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Team Building and Organizational Ambidexterity: A Relational Analysis

Authors:

Namrata Nanda, Siddharth Misra, Rajith K.R

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00003

Abstract:

This paper aims to identify and test the relationship of Team Building andOrganizational Ambidexterity by prompting bank employees to engage in commitment towards change.A structured questionnaire was prepared and distributed among employees of selected public and private banks across the country. A total of 240 valid responses were gathered from this survey using snowball and convenience sampling techniques. Descriptive statistics, regression analysis and factor analysis was used to interpret the results of the collected data. The analysis of data has been carried by using IBM SPSS and AMOS 20 version. The major takeaway of this research highlights the private sector banks where the commitment of employee towards change impacted team building leading to high ambidexterity as compared to that of public sector banks. Also, the results of the hypotheses formulated, holds true to the relationship of Team Building and Organizational Ambidexterity becomes stronger with a mediator Employee Commitment to Change and moderator, Psychological Safety in place.This research reflects on the importance of managing interpersonal threats hidden within every committed employee with the help of psychologically safe work environment and thus, promoting a strong culture of team spirit and being an ambidextrous organization. This paper confirms the effect of Team Building on Organizational Ambidexterity through Employee Commitment to Change and unlocks the dark box of how organizations can become ambidextrous by adding novelty to this research with the presence of Psychological Safety as a moderator.

Keywords:

Team Building,Organizational Ambidexterity,Psychological Safety,Employee Commitment to change,Moderated mediation,

Refference:

I. Beckman, C. M. (2006). The influence of founding team company affiliations
on firm behavior. Academy of Management Journal, 49(4), 741-758.
II. Beer, M., Voelpel, S. C., Leibold, M., &Tekie, E. B. (2005). Strategic
management as organizational learning: Developing fit and alignment
through a disciplined process. Long Range Planning, 38(5), 445-465.
III. Burgelman, R. A. (1983). Corporate entrepreneurship and strategic
management: Insights from a process study. Management science, 29(12),
1349-1364.
IV. Carmeli, A., &Halevi, M. Y. (2009). How top management team behavioral
integration and behavioral complexity enable organizational ambidexterity:
The moderating role of contextual ambidexterity. The Leadership
Quarterly, 20(2), 207-218.
V. Carmeli, A., &Halevi, M. Y. (2009). How top management team behavioral
integration and behavioral complexity enable organizational ambidexterity:
The moderating role of contextual ambidexterity. The Leadership
Quarterly, 20(2), 207-218.
VI. Carson, J. B., Tesluk, P. E., &Marrone, J. A. (2007). Shared leadership in
teams: An investigation of antecedent conditions and performance. Academy
of management Journal, 50(5), 1217-1234.
VII. Černe, M., Hernaus, T., Dysvik, A., &Škerlavaj, M. (2017). The role of
multilevel synergistic interplay among team mastery climate, knowledge
hiding, and job characteristics in stimulating innovative work
behavior. Human Resource Management Journal, 27(2), 281-299.
VIII. Denis, J. L., Lamothe, L., & Langley, A. (2001). The dynamics of collective
leadership and strategic change in pluralistic organizations. Academy of
Management journal, 44(4), 809-837.
IX. Ensley, M. D., Pearson, A., & Pearce, C. L. (2003). Top management team
process, shared leadership, and new venture performance: A theoretical
model and research agenda. Human Resource Management Review, 13(2),
329-346.

X. Ensley, M. D., Pearson, A., & Pearce, C. L. (2003). Top management team
process, shared leadership, and new venture performance: A theoretical
model and research agenda. Human Resource Management Review, 13(2),
329-346.
XI. Forbes, D. P. (2007). Reconsidering the strategic implications of decision
comprehensiveness. Academy of Management Review, 32(2), 361-376.
XII. Fredrickson, J. W., & Mitchell, T. R. (1984). Strategic decision processes:
Comprehensiveness and performance in an industry with an unstable
environment. Academy of Management journal, 27(2), 399-423.
XIII. Gibson, C. B., &Birkinshaw, J. (2004). The antecedents, consequences, and
mediating role of organizational ambidexterity. Academy of management
Journal, 47(2), 209-226.
XIV. Gupta, A. K., Smith, K. G., &Shalley, C. E. (2006). The interplay between
exploration and exploitation. Academy of management journal, 49(4), 693-
706.
XV. He, Z. L., & Wong, P. K. (2004). Exploration vs. exploitation: An empirical
test of the ambidexterity hypothesis. Organization science, 15(4), 481-494.
XVI. Herscovitch, L., & Meyer, J. P. (2002). Commitment to organizational
change: Extension of a three-component model. Journal of applied
psychology, 87(3), 474.
XVII. Hitt, M. A., Ireland, R. D., Camp, S. M., & Sexton, D. L. (2001). Strategic
entrepreneurship: Entrepreneurial strategies for wealth creation. Strategic
management journal, 22(6‐7), 479-491.
XVIII. Ireland, R. D., & Webb, J. W. (2007). Strategic entrepreneurship: Creating
competitive advantage through streams of innovation. Business
horizons, 50(1), 49-59.
XIX. Ireland, R. D., Hitt, M. A., &Sirmon, D. G. (2003). A model of strategic
entrepreneurship: The construct and its dimensions. Journal of
management, 29(6), 963-989.
XX. Jansen, J. J., George, G., Van den Bosch, F. A., &Volberda, H. W. (2008).
Senior team attributes and organizational ambidexterity: The moderating role
of transformational leadership. Journal of Management Studies, 45(5), 982-
1007.
XXI. Jaros, S. (2010). Commitment to organizational change: A critical
review. Journal of Change Management, 10(1), 79-108.
XXII. KetchenJr, D. J., Ireland, R. D., & Snow, C. C. (2007). Strategic
entrepreneurship, collaborative innovation, and wealth creation. Strategic
entrepreneurship journal, 1(3-4), 371-385.
XXIII. Kleinbaum, A. M., &Tushman, M. L. (2007). Building bridges: The social
structure of interdependent innovation. Strategic Entrepreneurship
Journal, 1(1‐2), 103-122.

XXIV. Kour, H., &Gakhar, K. (2015). Innovative HRM Practices: A Comparison of
Public and Private Sector Banks of India. MANTHAN: Journal of Commerce
and Management, 2(1), 1-28.
XXV. Levitt, B., & March, J. G. (1988). Organizational learning. Annual review of
sociology, 14(1), 319-338.
XXVI. Lubatkin, M. H., Simsek, Z., Ling, Y., &Veiga, J. F. (2006). Ambidexterity
and performance in small-to medium-sized firms: The pivotal role of top
management team behavioral integration. Journal of management, 32(5),
646-672.
XXVII. Makumbe, W. (2016). Predictors of effective change management: A
literature review. African Journal of Business Management, 10(23), 585-593.
XXVIII. March, J. G. (1991). Exploration and exploitation in organizational
learning. Organization science, 2(1), 71-87.
XXIX. Netemeyer, R. G., Boles, J. S., McKee, D. O., &McMurrian, R. (1997). An
investigation into the antecedents of organizational citizenship behaviors in a
personal selling context. Journal of marketing, 61(3), 85-98.
XXX. O’Reilly III, C. A., &Tushman, M. L. (2008). Ambidexterity as a dynamic
capability: Resolving the innovator’s dilemma. Research in organizational
behavior, 28, 185-206.
XXXI. Pearce, C. L., & Sims Jr, H. P. (2002). Vertical versus shared leadership as
predictors of the effectiveness of change management teams: An examination
of aversive, directive, transactional, transformational, and empowering leader
behaviors. Group dynamics: Theory, research, and practice, 6(2), 172.
XXXII. Perry, M. L., Pearce, C. L., & Sims Jr, H. P. (1999). Empowered selling
teams: How shared leadership can contribute to selling team
outcomes. Journal of Personal Selling & Sales Management, 19(3), 35-51.
XXXIII. Schoorman, F. D., Mayer, R. C., & Davis, J. H. (2007). An integrative model
of organizational trust: Past, present, and future.
XXXIV. Shin, Y., Kim, M., Choi, J. N., & Lee, S. H. (2016). Does team culture
matter? Roles of team culture and collective regulatory focus in team task
and creative performance. Group & Organization Management, 41(2), 232-
265.
XXXV. Siren, C. A., Kohtamäki, M., &Kuckertz, A. (2012). Exploration and
exploitation strategies, profit performance, and the mediating role of strategic
learning: Escaping the exploitation trap. Strategic Entrepreneurship
Journal, 6(1), 18-41.
XXXVI. Slaby, J., Mühlhoff, R., &Wüschner, P. (2019). Affective
arrangements. Emotion Review, 11(1), 3-12.
XXXVII. Smith, W. K., &Tushman, M. L. (2005). Managing strategic contradictions:
A top management model for managing innovation streams. Organization
science, 16(5), 522-536.

XXXVIII. Stinglhamber, F., Bentein, K., &Vandenberghe, C. (2002). Extension of the
Three-Component Model of Commitment to Five Foci: Development of
measures and substantive test. European journal of psychological
assessment, 18(2), 123.
XXXIX. Taneja, S., Pryor, M. G., & Toombs, L. A. (2011). Frederick W. Taylor’s
scientific management principles: Relevance and validity. Journal of Applied
Management and Entrepreneurship, 16(3), 60.
XL. Tushman, M. L., & O’Reilly III, C. A. (1996). Ambidextrous organizations:
Managing evolutionary and revolutionary change. California management
review, 38(4), 8-29.
XLI. Wang, C. L., &Rafiq, M. (2009). Organizational diversity and shared vision:
Resolving the paradox of exploratory and exploitative learning. European
Journal of Innovation Management, 12(1), 86-101.
XLII. Yu, M. C., Mai, Q., Tsai, S. B., & Dai, Y. (2018). An empirical study on the
organizational trust, employee-organization relationship and innovative
behavior from the integrated perspective of social exchange and
organizational sustainability. Sustainability, 10(3), 864.
XLIII. Yukl, G. (2008). How leaders influence organizational effectiveness. The
leadership quarterly, 19(6), 708-722.

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FRAMEWORK FOR ASSESSING SEISMIC RESILIENCE OF CITIES

Authors:

Yaseen Mahmood, Khan Shahzada, Usama Ali, Abdul Farhan, Syed Shujaat Ali Shah, Fawad Ahmad

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00004

Abstract:

This paper focuses on a framework for the seismic resilience of cities which incorporates the quantification of the seismic losses and developing models for assessing such losses(economic and human losses). By convolution of seismic hazard curve and fragility curve, a seismic loss curve has been obtained. Also the recovery paths have been chosen for the cities situated in south Asian countries by considering the pre-defined recovery curve.A general concept of resilience in cities has been presented by combining the losses and recovery in a in a single graph showing the resilience for the required city.

Keywords:

Resilience,Seismic, Hazards,Risks, Fragility,Losses,Recovery,Functionality,

Refference:

I. Federal Emergency Management Agency (FEMA), “HAZUS-MH MR4
Multi-Hazard Loss Estimation Methodology – Earthquake Model: Technical
Manual. Department of Homeland Security,” Fed. Emerg. Manag. Agency,
Washington, 2003.
II. G. P. Cimellaro, A. M. Reinhorn, and M. Bruneau, “Framework for analytical
quantification of disaster resilience,” Eng. Struct., vol. 32, no. 11, pp. 3639–
3649, 2010.
III. M. Bruneau and A. M. Reinhorn, “Overview of the resilience Concept,”
Proc. 8th US Natl. Conf. Earthq. Eng., no. 2040, pp. 2–6, 2006.
IV. N. Ahmad, “Development of a seismic risk / loss model for Mansehra city ,
Pakistan DEVELOPMENT OF A SEISMIC RISK / LOSS MODEL FOR
MANSEHRA CITY , PAKISTAN A Dissertation Submitted in
PartialFulfilment of the Requirements IstitutoUniveritario di StudiSuperiori,”
Thesis, no. June, 2014.
V. S. B. Manyena, “The concept of resilience revisited,” Disasters, 30, 434. vol.
30, no. 4, pp. 433–450, 2006.
VI. S. E. Chang and M. Shinozuka, “Measuring Improvements in the Disaster
Resilience of Communities Measuring and Improving the Disaster Resilience
of Communities,” Earthq. Spectra, vol. 98195, no. 206, 2004.
VII. S. L. Kramer, GEOTECHNICAL EARTHQUAKE ENGINEERING. Prenticehall
international series, 1996.
VIII. T. M. Frankie, B. Gencturk, and A. S. Elnashai, “Simulation-based fragility
relationships for unreinforced masonry buildings,” J. Struct. Eng. (United
States), vol. 139, no. 3, pp. 400–410, 2013.
IX. U. Ali, N. Ahmad, Y. Mahmood, H. Mustafa, and M. Munir, “A comparison
of Seismic Behavior of Reinforced Concrete Special Moment Resisting
Beam-Column Joints vs. Weak Beam Column Joints Using Seismostruct,” J.
Mech. Contin. Math. Sci., vol. 14, no. 3, 2019.
X. Y. K. Wen, B. R. Ellingwood, and J. Bracci, “Vulnerability Function
Framework for Consequence-based Engineering.” pp. 1–101, 2004.

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Visions and Global Developments in Artificial Intelligence for Identifying Intelligent Behavior in Machines

Authors:

B. V. V. Siva Prasad, B. Suresh Kumar, Ratna Raju Mukiri, Akshat Agrawal

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00005

Abstract:

Novel strategies of deep learning are assuring to also enhance the suggestion of AI outfitted with functionalities of self-improvement. However what are actually the greater social ramifications of this particular growth and to what extent are classical AI ideas still relevant? This paper talks about these issues consisting of an outline on standard principles as well as notions of AI in connection with big records. Particular emphasis lies on the functions, societal repercussions and also risks of machine and also deep learning. The newspaper says that the increasing significance of AI in culture bears significant threats of deep hands free operation prejudice enhanced through not enough machine learning quality, lacking mathematical responsibility and also shared risks of confounding up to incrementally aggravating conflicts in decision-making between human beings and also equipments. Big amounts of sensing unit readings as well as hyperspectral photos of plants may be utilized to pinpoint drought health conditions and to gain understandings in to when and also exactly how worry effects vegetation growth as well as progression and consequently how to an eye for an eye the trouble of planet appetite. Video game data can switch pixels right into activities within computer game, while empirical records may help enable robotics to comprehend complicated and also disorganized settings and to know manipulation skills.

Keywords:

Artificial Intelligence,machine learning,deep learning,

Refference:

I. Al-Hmouz, Ahmed. “An adaptive framework to provide personalization for
mobile learners.”(2012).
II. Al-Hmouz, Ahmed, Jun Shen, and Jun Yan. “A machine learning based
framework for adaptive mobile learning.” Advances in Web Based Learning–
ICWL 2009. Springer Berlin Heidelberg, 2009.34-43.
III. Amodei, D., et al., Concrete Problems in AI Safety, Cornell University, 2016:
https://arxiv.org/abs/1606.06565.
IV. Broder, Andrei, and Vanja Josifovski. “Introduction to computational
advertising.”(2010).
V. Cunningham, Sally Jo, James Littin, and Ian H. Witten. “Applications of
machine learning in information retrieval.”(1997).
VI. ICRC,Autonomy,ArtificialIntelligence(AI)andRobotics:TechnicalAspectsofHuman
Control,reportofanexpertmeeting,2019 (forthcoming).
VII. ICRC, Ethics and autonomous weapon systems: An Ethical Basis for Human
Control?, op. cit. p. 13.

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Natural Convection Cooling of PCB Equipped with Perforated Fins Heat Sink including Inclination and Vibration Effects

Authors:

HibaMudhafarHashim, Ihsan Y. Hussain

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00006

Abstract:

A numerical simulation is proposed to investigate the thermal behavior ofa Central Processer Unit (CPU) as a single electronic component placed on Printed Circuit Board (PCB) equipped with a heat sink. Two types of heat sinks were used; the first is with solid fins and the other with perforated fins. Natural convection cooling is considered, with the inclusion of vibration and inclination effects. The power dissipated from the electronic component is (30W). In order to study the thermal behavior during the vibration effect, a frequency values of (0,2,5,9,16HZ) with constant amplitude (3 mm) was considered. The inclination effect is investigated with and without the vibration effect. The results showed that the vibration causesa decrease in the temperature of the component. The temperature of the component decreases with increasing the angle of inclination, Verification of the results gave good agreement.

Keywords:

PCB,Perforated Fins Heat Sink,Inclination,Vibration,Natural Convection.,

Refference:

I. Abbas J.Al-Jessani,Hussein.R.Al-Bugharbee “an experimental
investigation of free convection heat transfer rate enhancement of
rectangular fins with circular perforations”, international conference on
advances in sustainable engineering and applications ,IEEE(2018).
II. B. Keshavarzian and M. Khorsavi “numerical investigation of the
structural frequencies effects on flow induced vibration and heat
transfer “J.Mater.Enviro.Sci6(7)(2015),1949-1956,ISSN:2028-
2508,JMESCN.
III. C.B.Baxi, A.Ramachandrn”effect of vibration on heat transfer from
spheres”,journal of heat transfer, August (1969).
IV. Cengel Y.A. Heat transfer a practical approach (MGH, 2002)
V. F A Gdhaidh, K Hussain and H S Qi “numerical study of conjugate
natural convection heat transfer using one phase liquid cooling”
materials science and engineering 65 (2014) 012012, IOP publishing.
VI. K.A.Rajput and A.V.Kulkarni “finite element analysis of convective
heat transfer augmentation from rectangular fin by circular perforation
“,international journal on recent technologies in mechanical and
electrical engineering,ISSN:2349-7947,037-042.
VII. K.H.Dhanawade, H.S.Dhanawade “enhancement of forced convection
heat transfer from fin arrays with circular perforation “,IEEE(2010).
VIII. MdRuhul Amin Rana “numerical and experimental study on
orientation dependency of free convection heat sinks”M.SC. Thesis
(University of British Columbia) 2015
IX. N.D.Jadeja, Ta-Cheng Loo “heat induced vibration of a rectangular
plate”,journal of engineering for industry ,August 1974.
X. P.K.Nag ,A.Bhattacharya “effect of vibration on natural convection
heat transfer from vertical fin arrays “letters in heat and mass transfer ,
vol.9,pp.487-498,1982.
XI. Rodrigo G.L.JoseA.F.and Douglas M.R.”natural convection of vertical
flat plates “,from the internet 2003
XII. ShrikantChavan and AnilkumaeSathe “natural convection cooling of
electronic enclosure” ,international journal of trend in research and
development, volume 3(4), ISSN:2394-9333, 2016.
XIII. ThamirK.Ibrahim.etal “experimental study on the effect of perforations
shapes on vertical heated fins performance under forced convectionheat transfer “,international journal of heat and mass transfer
118(2018),832-846.
XIV. Wu.ShurgFu,andBao-Hong Tong “numerical investigation of heat
transfer from a heated oscillating cylinder in a cross flow “,
international journal of heat and mass transfer 45 (2002),3033-3043.
XV. Wu-Shung Fu, Chien-Ping Huang “effects of vibrational heat surface
on natural convection in a vertical channel flow”, international journal
of heat and mass transfer 49 (2006) 1340-1349
XVI. Z.Staliulionis,Z.Zhang,R.Pittini,M.A.E.Andersen,P.Tarvydas,A.Noreik
a “investigation of heat sink efficiency for electronic component
cooling applications” ELEKTRONIK IR ELEKTROTECHNKA,ISSN
1392-1215, VOL.20, NO.1, 2014.

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An Efficient Emergency Vehicle Clearance Mechanism for Smart Cities

Authors:

Biru Rajak, Shrabani Mallick, Dharmender SinghKushwaha

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00007

Abstract:

The transportation management system is becoming an overwhelming task across the globe due to Globalization and population growth. Increased traffic congestion poses several problems. The extended waiting time at traffic jam leading to air and noise pollution due to the amassed vehicle is a serious threat to human health and the environment. This situation aggravates the clearance of any emergency vehicle resulting in grave consequences for the patient. A better control over the transportation system can be achieved through the Internet of Thing (IoT) based smart infrastructure. To deal with such emergency situations, this paper proposes a framework for automatic emergency vehicle clearance system. Traffic signal dynamically suspends the routine movement of traffic flow to create a "Green Corridor" to pass the ambulance without any delay at the traffic junctions. IoT based RFID tag and reader at vehicle and traffic junction respectively is used to identify the ambulance at the traffic junction. The work is simulated in SUMO and detection of RFID is analyzed in NS2 with the integration of SUMO. Considering the criticality of the issue, a simulation of the proposed work does not suffice. Therefore to check the robustness of the proposed system, it has been tested in a laboratory environment. The average reduction in travel time for five different simulations for an emergency vehicle from source to destination is 254.6%, which is substantial.

Keywords:

Emergency vehicle,Green Corridor,RFID,Smart traffic management,SUMO,Traffic congestion,

Refference:

I. A. Chattaraj, S. Bansal & A. Chandra, “An intelligent traffic control system using
RFID”, Potentials, IEEE 28.3 (2009): 40-43.
II. A.K. Mittal and D. Bhandari, “A novel approach to implement green wave system
and detection of stolen vehicles,” Proc. IEEE 3rd Int. Adv. Comput., Feb. 2013, pp.
1055–1059.
III. A.R. Dobre, A.V. Nita, A. Ciobanu, C. Negrescu, D. Stanomir, “Low computational
methods for siren detection” , Proceedings of the IEEE 21st International
Symposium for Design and Technology in Electronicpackaging (SIITME), Brasov,
Romania, 22–25 October 2015; pp. 291–295.
IV. A.S.Eltayeb, H.O Abubakr & T. A. Attia, “A GPS based traffic light pre-emption
control system for emergency vehicles” 2013 International Conference on
Computing, Electrical and Electronic Engineering (ICCEEE). IEEE, 2013.
V. B. Fazenda, H. Atmoko, F. Gu, L. Guan, A. Ball, “Acoustic based safety emergency
vehicle detection for intelligent transport systems”, Proceedings of the IEEE
International Conference ICROS-SICE, Fukuoka,Japan, 18–21 August 2009; pp.
4250–4255.
VI. D. Smith, S. Djahel & J. Murphy, “A sumo based evaluation of road incidents’
impact on traffic congestion level in smart cities”, 39th Annual IEEE Conference on
Local Computer Networks Workshops, pages 702–710. IEEE, 2014.
VII. F. Meucci, L. Pierucci, E. del Re, L. Lastrucci, P. Desii, “Areal-time siren detection
to improve safety of guide in traffic environment”, Proceedings of the IEEE 16th
International Conference on European SignalProcessing, Lausanne, Switzerland, 25–
29 August 2008; pp. 1–5.
VIII. F.W.Cathey & D.J. Dailey, “A novel technique to dynamically measure vehicle
speed using uncalibrated roadway cameras”, IEEE Proceedings. Intelligent Vehicles
Symposium, 2005. (pp. 777-782). IEEE.
IX. G. Palubinskas, F. Kurz, & P. Reinartz, “Detection of traffic congestion in optical
remote sensing imagery” , IGARSS 2008-2008 IEEE International Geoscience and
Remote Sensing Symposium (Vol. 2, pp. II-426). IEEE.
X. http://www.atmel.com/Images/Atmel-42735-8-bit-AVR-Microcontroller –
ATmega328-328P_Summary.pdf.
XI. http://www.merinews.com/mobile/article/India/2014/10/17/give-way-to-ambulanceeducates-
people-that-saving-time-is-saving-life/15902114.
XII. K. Nellore, G. Hancke, “Traffic management for emergency vehicle priority based
on visual sensing.” Sensors 16.11 (2016): 1892.

XIII. N. Singh, “An Efficient Approach for Handwritten Devanagari Character
Recognition based on Artificial Neural Network”, 2018 5th International
Conference on Signal Processing and Integrated Networks (SPIN), Noida, 2018, pp.
894-897.
XIV. N. Singh & T. Kumar. “An Improved Intelligent Transportation System: An
Approach for Bilingual License Plate Recognition.” Information and
Communication Technology for Intelligent Systems. Springer, Singapore, 2019. 29-
38.
XV. P. Priya, A. Jose, and G. Sumathy, “Traffic light pre-emption control system for
emergency vehicles.” SSRG International Journal of Electronics and Communication
Engineering (SSRG-IJECE) 2.2 (2015).
XVI. R. Sundar, S. Hebbar & V. Golla, “Implementing Intelligent Traffic Control System
for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection”, IEEE
Sensor Journal, Vol. 15, No. 2, Febuary 2015.
XVII. S. Sharma, A. Pithora, G. Gupta, M. Goel, & M. Sinha, “Traffic light priority control
for emergency vehicle using RFID,” Int. J. Innov. Eng. Technol., vol. 2, no. 2, pp.
363–366, 2013.
XVIII. T. Idé, T. Katsuki, T. Morimura & R. Morris, “City-wide traffic flow estimation
from a limited number of low-quality cameras” IEEE Transactions on Intelligent
Transportation Systems, 18(4), 950-959.
XIX. T.J. Hall, M.A. Schwartz & S.M. Hamer, “Gps-based traffic control preemption
system.” U.S. Patent No. 5,539,398. 23 Jul. 1996.
XX. T. Kumar & D.S. Kushwaha, “An Approach for Traffic Congestion Detection and
Traffic Control System”, Proceedings of Third International Conference on ICTCS
2017.
XXI. T. Kumar & D.S. Kushwaha, “An efficient approach for detection and speed
estimation of moving vehicles”, Procedia Computer Science, 89, 726-731.
XXII. T. Kumar, R. K. Sachan, D. S. & Kushwaha, “Smart City Traffic Management and
Surveillance System for Indian Scenario in Recent Advances” Mathematics,
Statistics and Computer Science (2016) (pp. 486-493).
XXIII. T. Miyazaki, Y. Kitazono, M. Shimakawa, “Ambulance siren detection using FFT
and dsPIC”, Proceedings of the First IEEE/IIAE International Conference on
Intelligent System and Image processing, Kitakyushu,Japan, 26–27 September 2013;
pp. 266–269.

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All-Optical Logic Gates Based on Graphene Interferometric Waveguide

Authors:

Hassan FalahFakhruldeen, TahreerSafa’a Mansour, Yousif I. Hammadi

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00008

Abstract:

Novel types of all-optical logic gates based on graphene surface plasmonpolaritons (SSPs) are proposed in this study by utilizing linear constructive and destructive interferences among SSP waves in spatially separated graphene sheets. The realized logic gates are OR, AND, and XOR gates. The suggested transmission value threshold between the two states logic 0 and logic 1 is 0.5. Small modification in the structure has been conducted to implement the XOR gate with the same wavelength for all the proposed gates. The structure performance is measured on the basis of transmission efficiency of each implemented gate. The state of each input port can be easily controlled by switching the external gate voltage either ON or OFF. The function of the proposed gates can be achieved by modifying the chemical potential ( c  ), coupling length ( c L ), orinter spacing among the graphene sheets (d). These compact-sized logic gates are considered an important part in the integration of nanoscale photonic devices.

Keywords:

Graphene,Surface plasmonpolaritons (SPPs),,All-optical logic gate,Nanophotonic devices,Plasmonic logic gates,

Refference:

I. A. F. Aguiar, D. M. d. C. Neves, and J. B. R. Silva, “All-optical logic gates
devices based on SPP coupling between graphene sheets,” Journal of
Microwaves, Optoelectronics and Electromagnetic Applications, vol. 17, pp.
208-216, 2018.
II. A. Vakil and N. Engheta, “Transformation optics using graphene,” Science,
vol. 332, pp. 1291-1294, 2011.
III. B. Wang and G. P. Wang, “Surface plasmon polariton propagation in nanoscale
metal gap waveguides,” Optics letters, vol. 29, pp. 1992-1994, 2004.
IV. D. A. Miller, “The role of optics in computing,” Nature Photonics, vol. 4, p.
406, 2010.
V. F. Wang, Y. Zhang, C. Tian, C. Girit, A. Zettl, M. Crommie, et al., “Gatevariable
optical transitions in graphene,” science, vol. 320, pp. 206-209, 2008.
VI. H. J. Caulfield and S. Dolev, “Why future supercomputing requires optics,”
Nature Photonics, vol. 4, p. 261, 2010.
VII. H. J. Caulfield, C. S. Vikram, and A. Zavalin, “Optical logic redux,” Optik-
International Journal for Light and Electron Optics, vol. 117, pp. 199-209,
2006.
VIII. H. Wei, Z. Wang, X. Tian, M. Käll, and H. Xu, “Cascaded logic gates in
nanophotonic plasmon networks,” Nature communications, vol. 2, p. 387,
2011.
IX. Hassan Falah Fakhrulden and Tahreer Safa’a Mansour, “All-optical NoT Gate
Based on Nanoring Silver-Air Plasmonic Waveguide,” International Joural of
Engineering & Technology, vol. 7, pp.2818-2821, 2018.
X. K. J. Ooi, H. S. Chu, L. K. Ang, and P. Bai, “Mid-infrared active graphene
nanoribbon plasmonic waveguide devices,” JOSA B, vol. 30, pp. 3111-3116,
2013.
XI. K. J. Ooi, H. S. Chu, P. Bai, and L. K. Ang, “Electro-optical graphene
plasmonic logic gates,” Optics letters, vol. 39, pp. 1629-1632, 2014.

XII. M. Jablan, H. Buljan, and M. Soljačić, “Plasmonics in graphene at infrared
frequencies,” Physical review B, vol. 80, p. 245435, 2009.
XIII. M. L. Brongersma and P. G. Kik, Surface plasmon nanophotonics vol. 131:
Springer, 2007.
XIV. M. W. McCutcheon, G. W. Rieger, J. F. Young, D. Dalacu, P. J. Poole, and R.
L. Williams, “All-optical conditional logic with a nonlinear photonic crystal
nanocavity,” Applied Physics Letters, vol. 95, p. 221102, 2009.
XV. M. Yarahmadi, M. K. Moravvej-Farshi, and L. Yousefi, “Subwavelength
graphene-based plasmonic THz switches and logic gates,” IEEE Transactions
on Terahertz Science and Technology, vol. 5, pp. 725-731, 2015.
XVI. optics,” nature, vol. 424, p. 824, 2003.
XVII. S. H. Abdulnabi and M. N. Abbas, “All-optical logic gates based on nanoring
insulator–metal–insulator plasmonic waveguides at optical communications
band,” Journal of Nanophotonics, vol. 13, p. 016009, 2019.
XVIII. S. I. Bozhevolnyi, V. S. Volkov, E. Devaux, J.-Y. Laluet, and T. W. Ebbesen,
“Channel plasmon subwavelength waveguide components including
interferometers and ring resonators,” Nature, vol. 440, p. 508, 2006.
XIX. X. Wu, J. Tian, and R. Yang, “A type of all-optical logic gate based on
graphene surface plasmon polaritons,” Optics Communications, vol. 403, pp.
185-192, 2017.
XX. Y. Fu, X. Hu, C. Lu, S. Yue, H. Yang, and Q. Gong, “All-optical logic gates
based on nanoscale plasmonic slot waveguides,” Nano letters, vol. 12, pp.
5784-5790, 2012.
XXI. Y. Liu, F. Qin, Z.-M. Meng, F. Zhou, Q.-H. Mao, and Z.-Y. Li, “All-optical
logic gates based on two-dimensional low-refractive-index nonlinear photonic
crystal slabs,” Optics express, vol. 19, pp. 1945-1953, 2011.
XXII. Yousif I. Hammadi and Tahreer S. Mansour., “Multiwavelength Erbium doped
fiber laser based on microfiber Mach-Zehnder interferometer,” Journal of
Optoelectronics and Advanced Materials-Rapid Communications, vol.13, no.3-
4, pp.156 – 160, April 2019.
XXIII. Z. Li, E. A. Henriksen, Z. Jiang, Z. Hao, M. C. Martin, P. Kim, et al., “Dirac
charge dynamics in graphene by infrared spectroscopy,” Nature Physics, vol. 4,
p. 532, 2008.

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