Paper currency is used repeatedly in exchange for goods and services,1 and this is why the circulation of paper currency from one individual to another potentially spreads microorganisms. If these currencies are contaminated by pathogenic bacteria, the rate of infectious diseases will continue to rise. Various microorganisms have been isolated from money worldwide including developed countries. Bacillus sp., and Staphylococcus aureus have been identified as common contaminants isolated from paper currency.2 However, other organisms like, Micrococcus sp., Corynebacterium sp., Vibrio cholerae, Mycobacterium tuberculosis and members of the family Enterobacteriacea have been isolated from currency too. Pathogenic microbes like S. aureus, Escherichia coli, and Klebseilla, enterobacter have been isolated from the US coins and paper bills currencies.3 This study was aimed at isolating and identifying the level of contamination of the Indian currency notes by microbial pathogens and to identify the possible associated risk factors in the study area. From February to March 2012, a total of 30 currency notes consisting of five notes of each of INR.5 and INR.10 denominations, was collected from three sources (i.e., public transport conductors, fish vendors, and vegetable vendors) in Vellore city, Tamilnadu, India. The currency notes were collected with hands covered with sterile plastic gloves and were placed immediately into sterile polythene bags and labeled accordingly. The samples were transported immediately to the laboratory for analysis. All of the 30 notes studied were contaminated with bacteria. The culture from the collected Indian paper currency yielded 21 isolates representing eight different types of bacterial species viz E. coli, Proteus mirabilis, Vibrio sp., S. aureus, Pseuodomonas sp., Salmonella sp., Bacillus sp., and Klebsiella sp. We found common occurrence of some bacteria isolated from currency notes regardless of their sources; those included E. coli, Vibrio sp., S. aureus, and Pseuodomonas sp.; other isolates such as, P. mirabilis and Klebsiella sp. were found in a limited number of colonies. In the present study, isolation of Gram positive as well as Gram negative bacteria from Indian currency notes confirmed that currency might be playing an important role, as a vector, in the transmission of pathogenic bacteria in the community. The pathogenic or potentially pathogenic bacteria found on these Indian currency notes, namely E. coli, S. aureus, Bacillus sp., Klebsiellab sp., Salmonella sp., Pseudomonas sp., P. mirabilis and Bacillus sp. may cause a wide variety of diseases from food poisoning, wound and skin infections, respiratory and gastrointestinal Bacterial Contamination of Indian Currency Notes (Rupee)
Nine impact craters on Mercury bear the names of Persian-Tajik poets: Rudaki, Saadi, Nizami, Rumi, Navoi, Firdousi, Hafiz, Sanai, and Mahsati. We compile IAU-approved coordinates, diameters, quadrant designations, approval dates (1976--2025), and MESSENGER-derived geological characteristics for each crater, and place these data in the broader context of Solar System objects associated with Tajik-Persian civilisation, including lunar craters (Al-Biruni, Avicenna, Omar Khayyam, Abul Wafa, Nasireddin, Alfraganus, Azophi, Al-Khwarizmi), main-belt asteroids, and Enceladus surface features named from the One Thousand and One Nights. The approval timeline mirrors the successive stages of Mercury mapping: two craters were designated after Mariner 10 (1976), three more following Mariner 10 data analysis (1979, 1985), two after the first MESSENGER flybys (2008, 2010), two after MESSENGER orbital operations (2014), and one in 2025. Geological diversity is notable: the Sanai basin (490 km, ~3.8--3.9 Ga) is among the oldest large structures on Mercury; Firdousi shows lava-flooded floor morphology; Rumi hosts an explosive pyroclastic vent intersected by Palmer Rupes; Hafiz contains low-reflectan
Foreign currency exchange plays a vital role for trading of currency in the financial market. Due to its volatile nature, prediction of foreign currency exchange is a challenging task. This paper presents different machine learning techniques like Artificial Neural Network (ANN), Recurrent Neural Network (RNN) to develop prediction model between Nepalese Rupees against three major currencies Euro, Pound Sterling and US dollar. Recurrent Neural Network is a type of neural network that have feedback connections. In this paper, prediction model were based on different RNN architectures, feed forward ANN with back propagation algorithm and then compared the accuracy of each model. Different ANN architecture models like Feed forward neural network, Simple Recurrent Neural Network (SRNN), Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) were used. Input parameters were open, low, high and closing prices for each currency. From this study, we have found that LSTM networks provided better results than SRNN and GRU networks.
The multifractal spectra of daily foreign exchange rates for US dollar (USD), the British Pound (GBP), the Euro (Euro) and the Japanese Yen (Yen) with respect to the Indian Rupee are analysed for the period 6th January 1999 to 24th July 2018. We observe that the time series of logarithmic returns of all the four exchange rates exhibit features of multifractality. Next, we research the source of the observed multifractality. For this, we transform the return series in two ways: a) We randomly shuffle the original time series of logarithmic returns and b) We apply the process of phase randomisation on the unchanged series. Our results indicate in the case of the US dollar the source of multifractality is mainly the fat tail. For the British Pound and the Euro, we see the long-range correlations between the observations and the thick tails of the probability distribution give rise to the observed multifractal features, while in the case of the Japanese Yen, the origin of the multifractal nature of the return series is mostly due to the broad tail.
This study examines the impact of the foreign exchange rate, i.e., US Dollar to Indian Rupee (USD/INR) on the Indian Stock Market Index (Nifty 50) during the demonetization of high denomination Indian currencies. A daily rate of return of Foreign exchange rate (USD/INR) and the Indian Stock Market Index (Nifty 50) were considered for the study. The Dummy variable was used to measure the effect of demonetization during Nov/Dec 2016. The period of study was restricted to 243 days from 1st April 2016 to 31st March 2017. The study reveals that there was an upward trend observed in the Indian Stock Market and the Indian currency was strengthened with the decrease in the Foreign exchange rate (USD/INR).
We provide a methodology by which an epidemiologist may arrive at an optimal design for a survey whose goal is to estimate the disease burden in a population. For serosurveys with a given budget of $C$ rupees, a specified set of tests with costs, sensitivities, and specificities, we show the existence of optimal designs in four different contexts, including the well known c-optimal design. Usefulness of the results are illustrated via numerical examples. Our results are applicable to a wide range of epidemiological surveys under the assumptions that the estimate's Fisher-information matrix satisfies a uniform positive definite criterion.
A new quantum theory bridges two rival models of how impurities behave inside many-particle systems, resolving a problem that has challenged physicists for decades。 The findings could reshape experiments on ultracold atoms, semiconductors, and other exotic forms of quantum matter
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