SP Lecture 19 by Dr.P.A.Hagargi

SP Lecture 17 by Dr.P.A.Hagargi

SP Lecture 11 by Dr.P.A.Hagargi

SP Lecture 6 by Dr.P.A.Hagargi,

SP Lecture 14 Dr.P.A.Hagargi,

CORONA !!!!!!!!!! RONA NA !!!!!

COVID19

            In this Modern days of Science and Technology, though having good Advancement in Science and Technology of WHO failed to identify or discover the solution of Coronavirus, since from last Oct, 2019 or may be before also, the World Scientists from every corner of the world, in-spite of having lots advancement of Science, Technology, Scientist and Wealth. Today World’s Health is suffering from this pandemic virus that is Coronavirus. 

Here I have briefed through references about Corona Virus which is found in different Text books of Zoology, Biology and also from…..Springer Article about Coronavirus in 1986 “Biology of Coronaviruses 1986”

1) As per the CBSE, CORONA is also categorized as one of the Common Cold, as per the text book there are few common cold like :

a) Nasopharyngitis, 
b) Acute Rhinopharyngitis, 

c) Acute Coryzna, or Cold 

All are a viral infection disease of the upper respiratory system, caused by Rhinoviruses and Coronaviruses plz read more by below image:

CORONA - STOP!!! KORONA !!!!!ROKNA !!!!

#COVID19 : CORONA VIRUS                                                                                                                   An image of a page from a zoology book authored by  Dr Ramesh Gupta is massively viral on social media. Several users have shared it claiming that the book speaks of a cure for COVID-19.  “#WHO you have not gone through the book of 12th standard   written by Dr Ramesh Chand Gupta where he has mentioned 

 about coronavirus so this is not a recent disease and he mentioned the cure too…who else is confused here lets reads as 

#JantaCurfewMarch22 

#pmoindia

#NarendraModi

#coronavirus

Conclusion

Conclusion:

Thus an expert from a zoology textbook covering the basics of the common cold was taken out of context. The claims made rest on the misconception that there is only one coronavirus and that coronavirus only causes the common cold. In fact, there are many coronaviruses that infect different animals. Of those, from the mid-1960s till date, seven coronaviruses have been shown to infect humans including SARS, MERS and the novel COVID-19. These viruses cause a wide range of illnesses from common cold to acute respiratory distress syndrome. Hence these viral tweets are misleading people on the treatment available for coronavirus and fuel conspiracy theories that the current coronavirus pandemic is not new.

Note: The number of positive cases of the novel coronavirus in India has exceeded 600. This has caused the government to impose a complete restriction on movement apart from essential services. Globally, more than 4 lakh confirmed cases and over 19,000 deaths have been reported. There is a growing sense of panic among citizens, causing them to fall for a variety of online misinformation – misleading images and videos rousing fear or medical misinformation promoting pseudoscience and invalid treatments. While your intentions may be pure, misinformation, spread especially during a global pandemic, can take lives. We request our readers to practice caution and not forward unverified messages on WhatsApp and other social media platforms.

One Solution from my side The RAMA BANNA FOR ALL SUCH VIRUSES IS PARIJATA TREE’S FLOWER OR LEAVES IS A GOOD ANTIBIOTIC [6]

References

[1] https://www.altnews.in/no-dr-ramesh-guptas-zoology-book-does-not-mention-a-cure-for-novel-coronavirus/

[2] https://link.springer.com/chapter/10.1007/978-1-4684-1280-2_37     

[3] https://tamil.samayam.com/education/news/school-text-books-having-mention-of-coronavirus-and-prevention-check-pics-here/articleshow/74840464.cms

[4] https://www.who.int/emergencies/diseases/novel-coronavirus-2019

[5] https://byjus.com/biology/common-cold/

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BellmanFord Algorithm

How Bellman Ford's algorithm works

Bellman Ford algorithm works by overestimating the length of the path from the starting vertex to all other vertices. Then it iteratively relaxes those estimates by finding new paths that are shorter than the previously overestimated paths

Steps

1) This step initializes distances from source to all vertices as infinite and distance to source itself as 0. Create an array dist[] of size |V| with all values as infinite except dist[src] where src is source vertex.

2) This step calculates shortest distances. Do following |V|-1 times where |V| is the number of vertices in given graph.
…..a) Do following for each edge u-v
………………If dist[v] > dist[u] + weight of edge uv, then update dist[v]
………………….dist[v] = dist[u] + weight of edge uv

3) This step reports if there is a negative weight cycle in graph. Do following for each edge u-v
……If dist[v] > dist[u] + weight of edge uv, then “Graph contains negative weight cycle”
The idea of step 3 is, step 2 guarantees shortest distances if graph doesn’t contain negative weight cycle. If we iterate through all edges one more time and get a shorter path for any vertex, then there is a negative weight cycle

How does this work? Like other Dynamic Programming Problems, the algorithm calculate shortest paths in bottom-up manner. It first calculates the shortest distances which have at-most one edge in the path. Then, it calculates shortest paths with at-most 2 edges, and so on. After the i-th iteration of outer loop, the shortest paths with at most i edges are calculated. There can be maximum |V| – 1 edges in any simple path, that is why the outer loop runs |v| – 1 times. The idea is, assuming that there is no negative weight cycle, if we have calculated shortest paths with at most i edges, then an iteration over all edges guarantees to give shortest path with at-most (i+1) edges (Proof is simple, you can refer this or MIT Video Lecture)

Example
Let us understand the algorithm with following example graph. The images are taken from this source.

Let the given source vertex be 0. Initialize all distances as infinite, except the distance to source itself. Total number of vertices in the graph is 5, so all edges must be processed 4 times.

Let all edges are processed in following order: (B, E), (D, B), (B, D), (A, B), (A, C), (D, C), (B, C), (E, D). We get following distances when all edges are processed first time. The first row in shows initial distances. The second row shows distances when edges (B, E), (D, B), (B, D) and (A, B) are processed. The third row shows distances when (A, C) is processed. The fourth row shows when (D, C), (B, C) and (E, D) are processed.

The first iteration guarantees to give all shortest paths which are at most 1 edge long. We get following distances when all edges are processed second time (The last row shows final values).

The second iteration guarantees to give all shortest paths which are at most 2 edges long. The algorithm processes all edges 2 more times. The distances are minimized after the second iteration, so third and fourth iterations don’t update the distances.