Kadambari Serial Cast
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The storyline of this serial tells about a straightforward and sweet girl who got hitched to an affluent personality who does not believe in destiny. Her heart got broken when she came to know that their fusion was only a betrayal by their families.
Kahaani Ghar Ghar Kii is yet another one of the most popular serials of soap opera queen Ekta Kapoor. Like Kyunki, it too became the flagship of Star Plus. Parvati Om Agarwal and Om Agarwal, played by Sakshi Tanwar and Kiran Karmarkar, respectively, are the most revered characters on television. The TV series came to an end in 2008 with1661 episodes and became one of the longest-running shows on Star Plus.
Besides the Hindi TV industry, Ekta Kapoor has also forayed into regional TV serials. The Kannad serial, Kadambari, is her longest-running show. The show aired on Udaya TV. It was based on the different struggles of different women.
Starting in 2009, Pavitra Rishta was produced by Ekta Kapoor and aired on Zee TV. It is one of the most popular serials of Ekta Kapoor. Not many know that the show was initially adopted from the Tamil serial Thirumathi Selvam on Sun TV. The show gave us two iconic characters, Archana and Manav, played by Ankita Lokhande and Sushant Singh Rajput / Hiten Tejwani. After the death of actor Sushant Singh Rajput, the serial was rebroadcasted on Zee TV and received good ratings. Owing to its popularity, Ekta has produced its new season, which has Ankita Lokhande and Shaheer Sheikh in key roles.
Kasautii Zindagii Kay was one of the longest-running Ekta Kapoor serials telecasted on Star Plus. The show enjoys classic cult status with iconic characters like Mr. Bajajaj, Komolika, Prerna, and Anurag. In 2018, the show came back with a new season with an all-new star cast. However, it could not beat the craze that the original version enjoyed.
Next on our list of Longest-running Ekta Kapoor serials is Kundali Bhagya. The TV series is a spin-off of Kumkum Bhagya. The show has grabbed eyeballs for its well-sketched characters, especially Dr. Preeta Karan Luthra, Dheeraj Dhoopar, and Manit Joura, essayed by Shraddha Arya, Dheeraj Dhoopar, and Manit Joura, respectively. Tune in to Zee TV at 9:30 to know the latest episodes. You can also watch it on Zee5.
Sony TV serial Kkusum revolved around its titular character, Kkusum, who faced many tribulations in her life. The show topped the TRP charts and received immense love for its characters and storyline. The soap opera concluded with its 1001st episode and bagged the title of one of the longest-running Ekta Kapoor serials. Owing to the love the original series received, the show is remade in Marathi as Tumchya Amchyatali Kusum by Ekta Kapoor and is telecasted on Sony Marathi.
Ekta Kapoor needs no introduction! The soap opera queen has been ruling the TV industry for the longest. Her serials like Kyunki Saas Bhi Kabhi Bahu Thi, Kumkum Bhagya, and Kahaani Ghar Ghar Kii have garnered immense love over the years.
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Across Europe there are a large number of rock deformation laboratories, each of which runs many experiments. Similarly there are a large number of theoretical rock physicists who develop constitutive and computational models both for rock deformation and changes in geophysical properties. Here we consider how to open up opportunities for sharing experimental data in a way that is integrated with multiple hypothesis testing. We present a prototype for a new forecasting model testing centre based on e-infrastructures for capturing and sharing data and models to accelerate the Rock Physicist (RP) research. This proposal is triggered by our work on data assimilation in the NERC EFFORT (Earthquake and Failure Forecasting in Real Time) project, using data provided by the NERC CREEP 2 experimental project as a test case. EFFORT is a multi-disciplinary collaboration between Geoscientists, Rock Physicists and Computer Scientist. Brittle failure of the crust is likely to play a key role in controlling the timing of a range of geophysical hazards, such as volcanic eruptions, yet the predictability of brittle failure is unknown. Our aim is to provide a facility for developing and testing models to forecast brittle failure in experimental and natural data. Model testing is performed in real-time, verifiably prospective mode, in order to avoid selection biases that are possible in retrospective analyses. The project will ultimately quantify the predictability of brittle failure, and how this predictability scales from simple, controlled laboratory conditions to the complex, uncontrolled real world. Experimental data are collected from controlled laboratory experiments which includes data from the UCL Laboratory and from Creep2 project which will undertake experiments in a deep-sea laboratory. We illustrate the properties of the prototype testing centre by streaming and analysing realistically noisy synthetic data, as an aid to generating and improving testing methodologies in
Many attempts for deterministic forecasting of eruptions and landslides have been performed using the material Failure Forecast Method (FFM). This method consists in adjusting an empirical power law on precursory patterns of seismicity or deformation. Until now, most of the studies have presented hindsight forecasts based on complete time series of precursors and do not evaluate the ability of the method for carrying out real-time forecasting with partial precursory sequences. In this study, we present a rigorous approach of the FFM designed for real-time applications on volcano-seismic precursors. We use a Bayesian approach based on the FFM theory and an automatic classification of seismic events. The probability distributions of the data deduced from the performance of this classification are used as input. As output, it provides the probability of the forecast time at each observation time before the eruption. The spread of the a posteriori probability density function of the prediction time and its stability with respect to the observation time are used as criteria to evaluate the reliability of the forecast. We test the method on precursory accelerations of long-period seismicity prior to vulcanian explosions at Volcán de Colima (Mexico). For explosions preceded by a single phase of seismic acceleration, we obtain accurate and reliable forecasts using approximately 80% of the whole precursory sequence. It is, however, more difficult to apply the method to multiple acceleration patterns.
Accelerating rates of geophysical signals are observed before a range of material failure phenomena. They provide insights into the physical processes controlling failure and the basis for failure forecasts. However, examples of accelerating seismicity before landslides are rare, and their behavior and forecasting potential are largely unknown. Here I use a Bayesian methodology to apply a novel gamma point process model to investigate a sequence of quasiperiodic repeating earthquakes preceding a large landslide at Nuugaatsiaq in Greenland in June 2017. The evolution in earthquake rate is best explained by an inverse power law increase with time toward failure, as predicted by material failure theory. However, the commonly accepted power law exponent value of 1.0 is inconsistent with the data. Instead, the mean posterior value of 0.71 indicates a particularly rapid acceleration toward failure and suggests that only relatively short warning times may be possible for similar landslides in future.
Accurate prediction of catastrophic brittle failure in rocks and in the Earth presents a significant challenge on theoretical and practical grounds. The governing equations are not known precisely, but are known to produce highly non-linear behavior similar to those of near-critical dynamical systems, with a large and irreducible stochastic component due to material heterogeneity. In a laboratory setting mechanical, hydraulic and rock physical properties are known to change in systematic ways prior to catastrophic failure, often with significant non-Gaussian fluctuations about the mean signal at a given time, for example in the rate of remotely-sensed acoustic emissions. The effectiveness of such signals in real-time forecasting has never been tested before in a controlled laboratory setting, and previous work has often been qualitative in nature, and subject to retrospective selection bias, though it has often been invoked as a basis in forecasting natural hazard events such as volcanoes and earthquakes. Here we describe a collaborative experiment in real-time data assimilation to explore the limits of predictability of rock failure in a best-case scenario. Data are streamed from a remote rock deformation laboratory to a user-friendly portal, where several proposed physical/stochastic models can be analysed in parallel in real time, using a variety of statistical fitting techniques, including least squares regression, maximum likelihood fitting, Markov-chain Monte-Carlo and Bayesian analysis. The results are posted and regularly updated on the web site prior to catastrophic failure, to ensure a true and and verifiable prospective test of forecasting power. Preliminary tests on synthetic data with known non-Gaussian statistics shows how forecasting power is likely to evolve in the live experiments. In general the predicted failure time does converge on the real failure time, illustrating the bias associated with the 'benefit of hindsight' in retrospective analyses 153554b96e
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