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    Monsoonal Rainfall Time Series (1901–2002) Analysis of Uttarakhand, India
    (2021-01-01) ;
    Gupta, Pankaj Kumar
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    Rawat, Ajay
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    Bajaj, Ankit
    Uttarakhand is mostly a hilly state, which is located at the foothills of the Himalayan mountain ranges. Monsoon is an important phenomenon, which controls the regional climate of Uttarakhand. Thus, an effort has been made to investigate the trends of monsoonal rainfall using descriptive statistical analysis, rainfall variability index (RVI) followed by frequency and trends analysis. District-wise RVI has been developed to identify the numbers of normal, dry, very dry, wet, and very wet years. Gumbel’s extreme-value distribution and Mann–Kendall test have been used for frequency and trends analysis, respectively. The results show that lesser Himalayan district at low altitude has reported with highest magnitude of monsoonal rainfall. Further, the number of wet and very wet monsoonal years has decreased during 1951–2000 as compared to 1901–2000 in the study area. Even so, interestingly, for all districts of both regions, the number of dry and very dry years surpasses the number of wet and very wet years for the last 20 years of study. The climate of Uttarakhand is significantly controlled by monsoon and analysis of monsoon rainfall for a period of 101 years (1901–2002) reveals a decreasing trend in southwest monsoon rainfall. The outcomes of this study may help water management practices and agricultural planning in Uttarakhand.
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    Evidence for Higgs boson decay to a pair of muons
    (2021-01-01)
    Sirunyan, A. M.
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    Tumasyan, A.
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    Adam, W.
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    Bergauer, T.
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    Dragicevic, M.
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    Erö, J.
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    Escalante Del Valle, A.
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    Frühwirth, R.
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    Jeitler, M.
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    Krammer, N.
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    Lechner, L.
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    Liko, D.
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    Mikulec, I.
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    Pitters, F. M.
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    Rad, N.
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    Schieck, J.
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    Schöfbeck, R.
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    Spanring, M.
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    Templ, S.
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    Waltenberger, W.
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    Wulz, C. E.
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    Zarucki, M.
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    Chekhovsky, V.
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    Litomin, A.
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    Makarenko, V.
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    Suarez Gonzalez, J.
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    Darwish, M. R.
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    De Wolf, E. A.
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    Di Croce, D.
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    Janssen, X.
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    Kello, T.
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    Lelek, A.
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    Pieters, M.
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    Rejeb Sfar, H.
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    Van Haevermaet, H.
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    Van Mechelen, P.
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    Van Putte, S.
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    Van Remortel, N.
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    Blekman, F.
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    Bols, E. S.
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    Chhibra, S. S.
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    D’Hondt, J.
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    De Clercq, J.
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    Lontkovskyi, D.
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    Lowette, S.
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    Marchesini, I.
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    Moortgat, S.
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    Morton, A.
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    Müller, D.
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    Python, Q.
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    Tavernier, S.
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    Van Doninck, W.
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    Van Mulders, P.
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    Beghin, D.
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    Bilin, B.
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    Clerbaux, B.
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    De Lentdecker, G.
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    Dorney, B.
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    Favart, L.
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    Grebenyuk, A.
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    Kalsi, A. K.
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    Makarenko, I.
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    Moureaux, L.
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    Pétré, L.
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    Popov, A.
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    Postiau, N.
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    Starling, E.
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    Thomas, L.
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    Vander Velde, C.
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    Vanlaer, P.
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    Vannerom, D.
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    Wezenbeek, L.
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    Cornelis, T.
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    Dobur, D.
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    Gruchala, M.
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    Khvastunov, I.
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    Niedziela, M.
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    Roskas, C.
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    Skovpen, K.
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    Tytgat, M.
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    Verbeke, W.
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    Vermassen, B.
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    Vit, M.
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    Bruno, G.
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    Bury, F.
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    Caputo, C.
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    David, P.
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    Delaere, C.
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    Delcourt, M.
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    Donertas, I. S.
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    Giammanco, A.
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    Lemaitre, V.
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    Mondal, K.
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    Prisciandaro, J.
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    Taliercio, A.
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    Teklishyn, M.
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    Vischia, P.
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    Wertz, S.
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    Wuyckens, S.
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    Alves, G. A.
    Evidence for Higgs boson decay to a pair of muons is presented. This result combines searches in four exclusive categories targeting the production of the Higgs boson via gluon fusion, via vector boson fusion, in association with a vector boson, and in association with a top quark-antiquark pair. The analysis is performed using proton-proton collision data at s = 13 TeV, corresponding to an integrated luminosity of 137 fb−1, recorded by the CMS experiment at the CERN LHC. An excess of events over the back- ground expectation is observed in data with a significance of 3.0 standard deviations, where the expectation for the standard model (SM) Higgs boson with mass of 125.38 GeV is 2.5. The combination of this result with that from data recorded at s = 7 and 8 TeV, corresponding to integrated luminosities of 5.1 and 19.7 fb−1, respectively, increases both the expected and observed significances by 1%. The measured signal strength, relative to the SM prediction, is 1.19−0.39+0.40(stat)−0.14+0.15(syst). This result constitutes the first evidence for the decay of the Higgs boson to second generation fermions and is the most precise measurement of the Higgs boson coupling to muons reported to date. [Figure not available: see fulltext.].
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    Development And Performance Evaluation Of Novel High-Density Clean Packer And Completion Fluid For Hpht Petroleum Reservoirs
    (2023-01-01)
    Singh, R.
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    Rao, G. R.
    The era of easily producible oil and gas has ended for quite some time, and new oil and gas fields are slowly being discovered in low to high combinations high pressure and high temperature (HPHT) wells. Generally, HPHT wells are classified under a pressure range of 10,000 to 30,000 psia and a temperature range of 350 °F to 500 °F. High density fluid systems have the ability to control the well high pressure for wellbore stability with high density, which contains fewer solids and maintains rheological stability at the HPHT reservoir for minimizing the formation damage and satisfy environmental requirements. This has demanded that new and novel packer and completion fluid designs be developed for low to high combinations HPHT wells. In this paper, developed fluid has shown with high specific gravity and optimum rheology. The calculated apparent viscosity (AV) was found low value which is desired for packer and completion fluid. Our firm belief is that the fluid can be designed optimally for a particular set of requirements specific to aid the completion process with efficient cost, less and high efficiency. From the industrial application point of view, it needs to be free from weight enhancing solid particles.
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    A Novel Iterative Field Search Approach to Minimum Zone Circle for Roundness Error Estimation
    (2023-08-01)
    Singh, Deep
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    Roundness is one of the common attributes in the manufacturing industry. Roundness is the most prominent of the extant fundamental forms, as the majority of the fabricated components are round or cylindrical. The examination of roundness error associated with such features is critical since inadequate evaluation might result in the rejection of excellent parts. The performance of any equipment depends on the mating parts. Out-of-roundness components cause the inefficiency of such equipment’s system performance. As a result, roundness error assessment is critical for macro-sized specimens as well as for micro- and nano-sized components. The present article proposes a novel approach to Minimum Zone Circle (MZC) to evaluate the roundness error. An iterative field search methodology is used in the suggested approach. The proposed methodology evaluates the roundness error based on the generated discretized points within the search space and continues the same by decreasing the search space with an increase in the number of iterations to attain the Minimum Zone Error (MZE). The proposed algorithm has been tested with ten CMM (coordinate measuring machine) datasets and ten form datasets available in the literature studies and found to be excellent in comparison to the existing techniques. Further, the proposed methodology was also implemented to estimate the MZE of centerless ground specimens at multiple cross sections, and the roundness error obtained was lesser as compared to the LSC, MCC, and MIC. The number of generated discretized points is flexible and can be varied to reduce the number of iterations and computations. The recommended method is quite effective in assessing the roundness error when compared to the existing techniques. It also works well on data that was both evenly and unevenly spaced. The results suggest that altering the search field area for higher computing efficiency is straightforward, resilient, and versatile.
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    InnoGuideGPT: Integrating conversational interface and command interpretation for navigation robots
    (2023-10-25)
    Sundar, Rahul
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    Gadgil, Shreyash
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    Satya Sai, Tankala
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    Reddy, Sathi Sai Krishna
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    Gautam, B.
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    Mittal, Ishita
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    Guduguntla, Jyotsna Sree
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    Pujala, Shanmukesh
    Integrating natural language understanding, voice command interpretation and natural language generation for realtime inference is a challenging problem. However, developing a proof of concept is now possible in just a few lines of code which was otherwise unimaginable a few years ago. Thanks to the democratization of LLMs and the availability of high-quality pre-trained models through APIs. It is now possible to quickly build effective use cases by just integrating multiple models without even having to pre-train/fine-tune the models on custom data. This is due to their zero-shot learning ability. Although, there are many recent works in this regard oriented towards software [1, 5], edge implementations and their applications to Robotics are yet to be explored in their entirety [6]. Recently, Koubaa [2] proposed RoboGPT, where a ChatGPT was prompt-tuned for command interpretation and subsequently determining the robot's actions. In this work, we explore the possibility of integrating a real-time conversational voice interface within a navigation robot. This is achieved using an underlying LLM interface to respond to user queries based on a prior context and additionally interpret voice commands for the robot to navigate. As a proof of concept, we evaluate the effectiveness of the planned workflow using a robot simulation. For simulating a Robot's environment, there exist various tools but in this work, Gazebo [3] has been adopted to simulate a differential drive robot moving in an indoor environment and Rviz is used for visualization. We utilized the TurtleBot3 [4] software packages to implement motion planning and navigation algorithms (see figure 2). For the speech recognition, we use OpenAI's Whisper API, while for text to speech we use the freely available google text-to-speech python package. We then use OpenAI's GPT-3.5-turbo API within the Langchain framework for building a context aware conversational interface and an additional command interpretation module under the hood to guide the navigation bot. It is remarkable how there is a significant reduction in development time for proof of concepts through the availability of high-quality models in addition to high-quality outcomes. Once the workflow is validated on Gazebo, the workflow is then implemented onto a NVIDIA Jetson Nano which will be used to send and process the cloud based LLM/speech API requests and responses (see figure 3.