Business Case Study on PricewaterhouseCoopers (PwC)Karthik Krishnan
A Case Study on PricewaterhouseCoopers (PwC) highlighting the leaders of it's Indian Management and Global as well. Also, Highlighting the Business Functions and it's leaders.
KPIT offers exposure to a highly attractive, rapidly expanding market (Automotive Electronics, Manufacturing and Energy & Utility), alongside an ability to capitalise as a low‐cost disruptor with compelling technology like ‘Revolo’. KPIT has one of the strongest industry positioning profiles in the Automotive Segments. Retain buy.
Business Case Study on PricewaterhouseCoopers (PwC)Karthik Krishnan
A Case Study on PricewaterhouseCoopers (PwC) highlighting the leaders of it's Indian Management and Global as well. Also, Highlighting the Business Functions and it's leaders.
KPIT offers exposure to a highly attractive, rapidly expanding market (Automotive Electronics, Manufacturing and Energy & Utility), alongside an ability to capitalise as a low‐cost disruptor with compelling technology like ‘Revolo’. KPIT has one of the strongest industry positioning profiles in the Automotive Segments. Retain buy.
Jai Saxena, from ERNST & YOUNG gives an analysts perspective on how to Build Businesses in a Meltdown.
Jai presents as part of the "Surviving and Winning in a Downturn" a one day conference by TiE Internet SiG, Delhi.
To catch all the action from the event, visit www.omshare.com (India's first Online Platform for taking Events and Conferences Global) - an Initiative by OMLogic
RBSA-RR-A Deep Dive Into The Hospital Industry in India.pdfRBSA Advisors
We are delighted to share our research on the Hospital segment in India, primarily focusing on the Performance of Key Hospital Groups including their future growth plans, and recent PE and M&A deals in this segment.
RBSA-RR-Specialty Chemicals-An opportunity for Make in India.pdfRBSA Advisors
These are interesting times for the Specialty Chemicals industry in India, in view of the current global market trends in this sector. The supply chain issues faced by manufacturers around the world, have brought greater momentum to the China +1 theme thereby creating incentives for Indian manufacturers to invest and grow.
RBSA-RR-Industry Valuation Multiples Series 6th Edition.pdfRBSA Advisors
We are delighted to share our “6th Edition of Industry wise Valuation Multiples” for Indian listed corporates. Our report coverage comprises of 13 largest industrial sectors in India.
RBSA-RR-Demystifying Life Insurance Industry in India (1).pdfRBSA Advisors
RBSA Advisors is delighted to share its recent research on the Life Insurance sector in India. Pandemic across the nation had impacted the country's overall financial system. The unprecedented nature of this crisis created difficult circumstances, including economic shutdowns. The year 2020 was a watershed year in the Insurance sector. Insurer were forced to rethink their business operations leading to enormous changes in the industry. Currently, life insurance industry is at crossroad.
Through this report we are demystifying the life insurance industry in India and sharing our views on the industry outlook.
SEBI streamlines the process of Buy-back of securities.pdfRBSA Advisors
With the intent to simplify the Buy-back process of securities, level the playing field for investors, and encourage ease of doing business, the Securities & Exchange Board of India (“SEBI”) has relaxed certain norms in the SEBI (Buy-back of Securities) Regulations, 2018 pursuant to SEBI (Buy-back of Securities) (Amendment) Regulations, 2023. Further, Operational Guidance on Buy-back was issued by SEBI on 8th March 2023. The amended regulation has come into force w.e.f 9th March, 2023.
RBSA-Budget-Finance Bill 2023-Key Proposals.pdfRBSA Advisors
Keeping a people-centric approach, various amendments in individual tax provisions and amendments providing benefits under the new tax-rate regime is a welcome move.
Through this report, we share our views on the prevailing framework/rules under the Income Tax Act, 1961 for determination of Fair Valuation in case of Shares/Securities.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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