Posted: June 6th, 2022

Designated brain model article

Homework 5,6

Question 1 – Use the notes and the designated Brain Model article which is called “Graph Models of Brain Networks” I am attaching below
Question 2 – Notes and designated Brain Model Article
Question 3 – Same as above
Question 4 – The two sample articles named ” Demand and capacity sharing decisions ” and “Real-time optimization and control mechanisms for collaborative demand”
Question 5 – Review the chapter 49 I have attached below use the instructions in notes “1” and the other instructions in the homework document.

Homework 5,6
(I). Designated brain model article
A). What is defined as a “brain model” based on your assigned article
Over the years, the brain network has evolved and grown tremendously in its interaction and connectivity patterns. Additionally, the brain network is explained through several graph models, where the optimal and best graph model has not been identified to date. The brain network uses some graph models, such as the random graphs, the erdos-renyi random graphs, the n-dimensional geometric random graphs, the Barabasi-Albert type scale-free network, and the stickiness model networks. A brain model is a model that describes the structural nature of and properties of the structural brain network and a model that influences the typical dynamic and fictional behavior of the network structure. On the other hand, the brain model helps capture and promote connectivity of the structural brain network. According to the article, one of the best brain models is the STICKY model, with a higher connection strength, faster, and higher synchronization speed than other models.
B. )What are the purpose, limitations, and advantages of the model?
The sticky model being the best brain network model in terms of speed, ad other properties, has several advantages and disadvantages. The sticky model is the best since it can fit in both local and global properties of graphs. Also, the brain model synchronizes well using dynamic network synchronization properties. The STICKY brain model has a close relationship with the accurate data and has the two most critical global properties, the number of graphics properties and the average diameter properties. Compared to another mode,s the STICKY model easily synchronizes with other models, especially in terms of speed, hence using a shorter time. However, the brain model influence or affect the dynamic behavior of the structural network.
C). How, what is analyzed about this model in the article
The article uses state-of-art graph analysis tools and methods that help measure both the local and global graphs features and evaluate many graphs and use a functional simulation experiment to measure the synchronization behavior of the structural network system (Milano, et,al.,2017). The brain model’s main analyzed proprieties are the multi-resolution structural network construction, structural graph properties, which include both the local and global structural graph properties. Also, the analysis checks on the synchronization speed of the brain network model.
D). Cyber roles and functions of the brain model.
The cyber-physical production system comprises both digital and physical systems, a significant investment that is currently made across the world (Milano, et,al.,2017). Additionally, the cyber-physical production system has five main components that Help in computation and easy connection with the outside world. The cyber-physical production system’s five components include the cyber, the intelligent connection, cognition, configuration, and data conversion, which involves converting data into information.
The conversion component Helps in changing data into a more usable form, mostly done through data visualization (Milano, et,al.,2017). Additionally, connection involves the use of IoT, sensor networks, and play devices. The configuration process involves the use of flexible manufacturing and use of artificial intelligence. As one of the essential components in the CPPS, the cyber component Helps in the analysis, monitoring, communication control, and data mining, which are the leading roles in the brain model (Milano, et,al.,2017). Cyber play an essential role in the brain model, especially in preventing external attacks and preventing standard brain model cyber security issues, such as confidentiality and availability issues. However, cyber plays an important role, especially in controlling communication, Help in data mining, and analysis and Help in monitoring information and structural network processes.
On the other hand, the cyber-physical production system’s configuration Helps in dealing with cybersecurity issues that affect the BCI cycle and the processes that take place in the brain model. Some of the attacks prevented from attacking the brain model include spoofing attacks, malware attacks, and other threats designed against the physical properties of the model, affecting the physical properties and disrupting the stimulation process. The data processing component and conversion Help in preparing data for the next stage in the brain model (Milano, et,al.,2017). The data is made free from cyber worms and other types of vulnerabilities. The cyber-physical production process Helps in dealing with cybersecurity issues that occur and cause vulnerabilities in the brain model, affecting the confidentiality, safety, integrity, and availability of data and systems. The cyber-physical and production process Helps in promoting synchronization, enhancing the interaction between the brain model and the users; for instance, the high synchronization of the STICKY brain model is the best and effective brain model.
(ii). Cyber-physical systems are operated and monitored through the internet and play a vital role in collaborative production administration, such as robotic technology, e-teaching, and other collaborative activities, especially in a manufacturing environment (Milano, et,al.,2017). On the other hand, the brain model can operate a collaborative production physical system such as a robot, exoskeleton, and use of artificial intelligence, which can operate well when used operated by the brain model. For instance, running collaborative telerobotics, intelligent grids, CI hub, and shared services. Organizations currently use brain-inspired cyber-physical systems and other technologies, such as cognitive computing and machine learning. However, the brain model Helps in operating the emerging application, such as the intelligent X and the autonomous systems, such as the uncrewed air vehicle, autonomous robots, self-driving cars, and other intelligent cyber-Brain-inspired computing architectures and models for CCPS physical-human systems(CPHS).
The brain model Helps in conducting activities and operations such as perception, attention, memory, the reasoning of the systems, problem-solving, and knowledge representation. Attention is primarily used in fault detection, while the brain model Helps in applying human memory with both the long-term and short-term memory mechanisms. Memory is considered an integral feature for intelligent behavior for collaborative production physical systems and hybrid cell architecture.
(ii). Product Scheduling and planning are critical in caber-physical production systems, as the process helps improve production activities (Milano, et,al.,2017). Additionally, the process helps streamline the decision-making process, identify the cause of the fundamental issue, and its relationship with the product planning process. As a popular aspect of most industries today, the cyber-physical planning production systems require control and decentralization and production control, which provides a higher degree of subsequent scheduling (Milano, et,al.,2017). The development of cyber-physical systems and cyber-physical production systems has promoted big data and Helped in monitoring and handling data to prevent cybersecurity issues. Interaction of human and artificial intelligence helps promote cyber production management systems, especially when the multi-brain systems take over some cognitive tasks that Help in supporting and improving human thinking.
2. How does the model support even the argument of CCT principles?
The principles of collaborative control theory(CCT) includes the conflict/ error detection and prevention(CEDP) principle, the e-work parallelism? (EWP) principle, the keep it simple system(KISS), the collaboration requirement planning(CRP), the fault tolerance by teaming(FTT), and the Association /disassociation(AD) principle (Milano, et,al.,2017). The principle, however, is supported by the brain models, such as the stickiness model networks (STICKY), the n-dimension geometric random graphs, the Barabasi -Albert type sale free network, the Erdos-any random graphs, and the random graph with the same degree distribution as the data.collaboration requirement planning(CRP) Help in the integration of knowledge and Help in making discoveries that Help in dealing with attractive-collaborate work (Milano, et,al.,2017). Also, Help in delivering the discoveries that art required in the control of cyber-physical systems in collaborative production, such as robots.

3. Create a collaborative brain model supporting multi-brain interaction for cyber production planning, control, and learning (Milano, et,al.,2017).
The collaborative brain model develops the brains of the company and Helps in using collaborative software and developing a cooperative brain. The collaborative brain interface Helps in accelerating human decision-making and in supporting several multi-brain interactions. The best collaborative has characteristics and features of the human brain, which Help in working together
4.) Explain how the CBM benefit CPS cyber production systems and how the CBM can benefit the project.
Demand and capacity sharing protocol is a very beneficial system used in the enterprise’s collaborative network (CN), especially in promoting decision-making and improving demand satisfaction rate (Milano, et,al.,2017). Condition-based -Maintenance plays a vital role in cyber production systems, especially in offering continuous monitoring services that help evaluate the physical systems for future use. On the other hand, condition-based maintenance helps improve and promote the overall equipment efficiency(OEE), mainly based on efficiency, quality, and availability (Milano, et,al.,2017). However, the CBM Helps in increasing assets’ availability, Helping in the planning, scheduling process, reducing the storage needs for spare parts, and unplanned downtime. However, the CBN Helps in monitoring several parameters by applying analytic tools, such as the fault tree analysis(FTA0, and the failure mode and effect analysis(FMEA).
On the other hand, the condition-based maintenance information Helps in identifying the cause of the problem in the CPPS and the maintenance of the damaged and broken parts of the systems (Milano, et,al.,2017). Additionally, the CBM Helps in data acquisition in the assessment process, advisory generator, and data manipulation. Data acquisition involved converting some physical phenomena into easily interpretable, ad readable signals. On the other hand, the CBM Helps in conducting some activities based on a statistical approach, promoting pattern recognition and machine learning algorithms. Despite the positive impacts of the CBM on CPPS, the condition-based maintenance consumes a lot of time, especially when getting training or initial data, where most of the time needed may not be provided.
Maintenance of cyber-physical production systems is essential, especially with the current age of cybersecurity issues. CBM, however, Helps in managing and maintaining the physical systems in the present and future, especially in reducing machine downtime. Howe3ver, the broken or damages physical systems can still work, especially when the scheduled date is not due for repair. The Open system architecture for condition-based maintenance (OSA-CBM) helps develop the open information standards for operating and maintenance in manufacturing(MIMOSA), especially in health care systems under the ISO-13374 standards.
(5). Review Questions
1.) Yes, the topic reflects the state-of-art of the brain model, which discusses the brain models’ role, especially in application to the new technologies and some of the up-to-date cyber-physical production systems. The topic, however, provides the effective use of the brain model, especially in collaborative control systems, such as robots, and how the human computerized brain plays a vital role in the control of humanoid machines. The brain model topic also shows how the concept is related to argumentation and the role of the collaborative brain-computer interface in the decision-making process. Lastly, the article uses state-of-art tools and algorithms in measuring the graphs. ultimately, especially considering that the article is not well completed, most of the information is missing.
2.) The article is very shallow for a reader who needs a wide range of information. However, the article’s content is well represented, beginning with an introduction that provides a vivid discussion and explanation concerning the human brain and the typical structure of brain networks. Also, the article goes direct into the methods of research and findings instead of first having an in-depth discussion concerning brain models. The author uses some data presentation methods, such as using a graph, in discussing the result, especially on how the models are correlated with the local and global graphs.
3. Yes, the most important data concerning graph models for the description of brain networks is presented in graph form, especially in showing the five modes’ structural graph properties, including the sticky model, which shows the closest relationship with the accurate data.
4.) Yes, the bibliography consists of the fundamental literature that has been used in discussing the brain model. For instance, most of the literature used is concerned with brain network graphs, connectivity in complex brain networks, scaling in random networks, and mapping human whole-brain structural and functional systems.
5.) The title should be improved, primarily focusing on brain networks and types of graph models used. However, the topic is not well put in and maybe complex to understand the writer’s aim. However, the topic represents the content, but a more vivid topic should be used to understand the content more accessible.

Reference
Milano, M., Guzzi, P. H., Tymofieva, O., Xu, D., Hess, C., Veltri, P., & Cannataro, M. (2017). An extensive assessment of network alignment algorithms for comparison of brain connectomes. BMC bioinformatics, 18(6), 31-45.

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