Seven out of 542 sitting Lok Sabha MPs (1.3%) and 199 out of 4,086 MLAs (5%) did not declare their PAN details while filing their election affidavits, according to an analysis by the Association for Democratic Reforms and National Election Watch. PAN and income details are among the entries in the prescribed affidavit format. While the analysis showed that among the candidates eventually elected, most did declare PAN details, it threw up additional details such as party-wise trends, state-wise trends, and discrepancies in PAN details between one election and the next.
Of the seven MPs who did not submit details, two were AIADMK candidates from Tamil Nadu, two were BJD candidates from Odisha, and the other three were from Mizoram (Congress), AIUDF (Assam), and NCP (Lakshadweep). Among the 199 MLAs, the Congress accounted for more than one-fourth (51) followed by the BJP for more than one-fifth (42).
State-wise, Kerala had the highest number of MLAs (33) who did not submit PAN details. Among those reelected, 11 MPs and 35 MLAs submitted PAN details that showed dissimilarities in the two affidavits.
How one driverless vehicle can ‘adjust’ with speed of another
In an age when the world is talking about driverless vehicles, one pertinent question is how the vehicles adjust with the surroundings. New research has provided a possible answer to this. “When the car in front of you speeds up, yours would accelerate, and when the car in front of you screeches to a halt, your car would stop, too,” the University of Delaware says in a post detailing the research.
Professor Andreas Malikopoulos of the university uses control theory to develop algorithms that will enable this technology of the future, the post says. He has published two recent papers on innovations in connected and automated vehicle technology.
Someday cars might “talk to each other” to coordinate traffic patterns, the university says, describing the findings of one of the papers, published in the journal Automatica. Malikopoulos and collaborators from Boston University recently developed a solution to control and minimise energy consumption in “connected” driverless vehicles crossing an urban intersection that lacked traffic signals. Using software to simulate their results, they found that their framework allowed these vehicles to conserve momentum and fuel while also improving travel time.
When a car moves from an area with a higher speed limit to another with a lower limit, can it automatically slow down? Malikopoulos and collaborators from the University of Virginia formulated a solution that “yields the optimal acceleration and deceleration in a speed reduction zone, avoiding rear-end crashes. Also, simulations suggest that the connected vehicles use 19% to 22% less fuel and get to their destinations 26% to 30% faster than human-driven vehicles”. The results of this research were published in IEEE Transactions on Intelligent Transportation Systems.
For full versions of the studies, visit: ieeexplore.ieee.org/document/8490221sciencedirect.com/science/article/pii/S0005109818301511