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CSIR Fourth Paradigm Institute

(Formerly CSIR Centre for Mathematical Modelling and Computer Simulation)

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विवरण.
श्रेणी:Publications.

by Raghavendra Prasad K, Kantha Rao Bhimala, G. K. Patra, Himesh S & Sheshakumar Goroshi 

The present study analyzed the actual evapotranspiration (ETa) and its components [transpiration (Et), bare soil evaporation (Eb), interception loss (Ei), and open water evaporation (Eo)] data to study the long-term (1980–2018) trends over different meteorological sub-divisions in India. Quantitatively, all India average (µ) annual ETa is 573 mm (standard deviation (σ) = 29 mm), where Et (µ = 456 mm; σ = 30 mm) plays a major role compared to other evaporation processes like Eb (µ = 56 mm; σ = 9 mm), Ei (µ = 34 mm; σ = 3 mm), and Eo (µ = 27 mm). The Mann–Kendall (MK) test reveals an increasing trend (1.33 mm/yr) in annual ETa due to the rising trend in Et (1.91 mm/yr) and Ei (0.16 mm/yr). The sub-division-wise analysis shows the increasing trend in ETa observed over irrigated regions located in the south, north-west, and foothills of the Himalayas during pre-monsoon (March–May) and monsoon season (June–September). The correlation analysis observed a complex relationship between ETa and climatic factors (rainfall (RF), soil moisture (SM), surface temperature (T), relative humidity (RH), surface net solar radiation (SSR), and wind speed (WS)) during monsoon season such that the water-limited areas have a positive correlation with SM, RH and RF, and negative correlation with WS, T, and SSR, whereas, in energy-limited areas (east India), the ETa showed a positive correlation with SSR and T and negative correlation with RF. The main climatic drivers for the increasing trend of ETa are SM and rainfall over dry regions and SSR and T over densely vegetated regions in India.

 Source: https://link.springer.com/article/10.1007/s00704-023-04436-x 

विवरण.
श्रेणी:Publications.

by Nikhila Yaladanda, Rajasekhar Mopuri, Hariprasad Vavilala, Kantha Rao Bhimala, Krushna Chandra Gouda, Madhusudhan Rao Kadiri, Suryanarayana Murty Upadhyayula & Srinivasa Rao Mutheneni

Abstract

The northeast region of India is highlighted as the most vulnerable region for malaria. This study attempts to explore the epidemiological profile and quantify the climate-induced influence on malaria cases in the context of tropical states, taking Meghalaya and Tripura as study areas. Monthly malaria cases and meteorological data from 2011 to 2018 and 2013 to 2019 were collected from the states of Meghalaya and Tripura, respectively. The nonlinear associations between individual and synergistic effect of meteorological factors and malaria cases were assessed, and climate-based malaria prediction models were developed using the generalized additive model (GAM) with Gaussian distribution. During the study period, a total of 216,943 and 125,926 cases were recorded in Meghalaya and Tripura, respectively, and majority of the cases occurred due to the infection of Plasmodium falciparum in both the states. The temperature and relative humidity in Meghalaya and temperature, rainfall, relative humidity, and soil moisture in Tripura showed a significant nonlinear effect on malaria; moreover, the synergistic effects of temperature and relative humidity (SI=2.37, RERI=0.58, AP=0.29) and temperature and rainfall (SI=6.09, RERI=2.25, AP=0.61) were found to be the key determinants of malaria transmission in Meghalaya and Tripura, respectively. The developed climate-based malaria prediction models are able to predict the malaria cases accurately in both Meghalaya (RMSE: 0.0889; R2: 0.944) and Tripura (RMSE: 0.0451; R2: 0.884). The study found that not only the individual climatic factors can significantly increase the risk of malaria transmission but also the synergistic effects of climatic factors can drive the malaria transmission multifold. This reminds the policymakers to pay attention to the control of malaria in situations with high temperature and relative humidity and high temperature and rainfall in Meghalaya and Tripura, respectively.

Source: https://doi.org/10.1007/s11356-023-26672-4 

 

विवरण.
श्रेणी:Publications.

by KK Chowdari, Surajit Deb Barma, Nagaraj Bhat, R Girisha, K.C. Gouda & Amai Mahesha

Abstract: 

Trend analysis of rainfall is often carried out in water resources management to understand its distribution over a given region. The cumulative seasonal and annual rainfall derived from monthly datasets spanning 102 years (1901–2002) for 11 districts of the semi-arid Karnataka, India, was used for the trend analysis. The two-step homogeneous test approach was carried out on all the time series. Then, lag-1 autocorrelation was conducted only on homogeneous time series. Only 78.18 % of the total time series data were detected as homogeneous, and 95.35% of time series data were found to have insignificant autocorrelation. Then, the Innovative Trend Analysis (ITA) method was applied to 43 homogeneous rainfall time series, as well as to 41 time series using the MK and SR tests, and to two time series using the mMK test. The MK and SR tests detected a significant trend in 14.63% of the time series, while the ITA method was able to detect a trend in 93.02% of the total time series data. The MK and SR tests revealed significant trends in winter and post-monsoon season precipitation for two districts, but only for one district in the case of summer and annual rainfall. No trend was identified for monsoon season precipitation. The mMK test showed a positive trend for the post-monsoon season in a district, while the ITA method revealed significant trends for all seasons in most districts. The sub-trend analysis revealed trends that traditional methods were unable to detect.

Source: https://link.springer.com/article/10.1007/s00704-023-04400-9 

विवरण.
श्रेणी:Publications.

by Ashish, Gokul Saha and Shyam S Rai

Summary

We investigate the 3-D shear velocity (Vs) structure of the crust beneath the Kumaon Garhwal Himalaya using joint inversion of interpolated receiver functions from 57 seismic stations, and Rayleigh wave group velocity dispersion data in the period 2 to 100 s with significantly improved horizontal resolution of about 25 km. The velocity image reveals several important features. In the shallow crust, the Main Himalayan Thrust (MHT) is characterised as a flat-ramp-flat structure, inferred from the presence of low Vs of 3.1–3.4 km/s representing wet sediments dragged along the MHT and lying above the crystalline Indian crust of Vs ∼ 3.6 km/s. The MHT is at a depth of about 8 km beneath the southern edge of the Himalaya, dipping at 3○ to the north. At the front of the High Himalaya, the dip increases significantly to about 35○–40○ representing the ramp and reaching a depth of 24 km. Farther north beneath the High Himalaya, the MHT continues as a nearly flat structure. The middle crust (20–30 km) has reduced Vs (3.3–3.5 km/s) below the northern part of the Lesser Himalaya, possibly due to the presence of fluid released by metamorphism of the subducting Indian crust along with the presence of mica produced as a consequence of deformation. The thickness of the crust is ∼50 km beneath the sub and Lesser Himalaya and increases abruptly in the front of the High Himalaya to 60 km and remains so till the southern part of Tethys Himalaya. The observed thick crust with lower seismic velocity (and rigidity) beneath the High Himalaya could be responsible for its high topography. We report almost 6–8 km thinning of the crust in the eastern segment of Garhwal Himalaya adjoining Nepal.

Source: https://doi.org/10.1093/gji/ggad044 

विवरण.
श्रेणी:Publications.

by G.K. Patra, Kantha Rao Bhimala, Ashapurna Marndi, Saikat Chowdhury, Jarjish Rahaman, Sutanu Nandi, Ram Rup Sarkar, K.C. Gouda, K.V. Ramesh, Rajesh P. Barnwal, Siddhartha Raj, Anil Saini

Abstract
Deep learning (DL) is a very powerful computational tool for various applications in scientific and industrial research which can be real-time implemented for societal benefits. Several factors impact the development of optimized DL models for better prediction including the amount of quality sample data, domain-specific knowledge, and the architecture of the model for extraction of the useful features/patterns from the data. The present chapter demonstrates the state-of-the-art DL methodologies used by the researchers from different laboratories under the Council of Scientific and Industrial Research (CSIR), India to solve important research activities across several sectors like Medical, Healthcare, Agriculture, Energy, etc. The Convolutional Neural Network (CNN) techniques are utilized for Tumor diagnosis, classifying molecular subtypes of glioma tissues, and predicting driver gene mutations in glioma. Similarly, the Long short-term memory (LSTM) model is applied for the assessment of crop production, and transfer learning is used for the classification of tea leaves. Further, the ensemble LSTM methodology is implemented for short-term prediction of wind speed to enhance the renewable energy sectors. Finally, the multivariate LSTM models were developed by integrating the weather parameters for the prediction of covid-19 spread over different states in India which is an input for policy planning and supply chain management during the pandemic time. All the use cases are being validated and the results are quite satisfying and provide confidence for the real-time application of DL for scientific and industrial research and societal benefit to the common people.

Source: https://doi.org/10.1016/bs.host.2022.12.002

 

 

आलेख और अधिक ...

  1. Impact of comorbidity on patients with COVID-19 in India: A nationwide analysis
  2. Extreme rainfall event analysis over the state of Himachal Pradesh in India
  3. Relationship of height and intensity of low-level jet stream with Indian summer monsoon rainfall
  4. Exacerbation of Indian Summer Monsoon Breaks by the Indirect Effect of Regional Dust Aerosols

Vision: 

To synergize the Transdisciplinary Pan-CSIR expertise and build a unified platform that embodies a rich set of big data enabling technologies and services with optimized performance to facilitate data intensive scientific discovery in the country.

Mission:

To Pioneer data driven interdisciplinary research in diverse fields through state-of-the-art data science ecosystem and impactful industrial partnerships for the betterment of Society.

Mandate: 

To develop cutting edge data science products as a horizontal across the CSIR Themes and position as an Institute of Excellence in Bigdata and Artificial Intelligence.

Student Programme for Advancement in Research Knowledge (SPARK)

SPARK is intended to provide a unique opportunity to bright and motivated students of reputed Universities to carry out their major project/thesis work and advance their research knowledge in mathematical modelling and simulation of complex systems. The programme is intended to increase the interaction between scientists and faculty members of academic institutes along with their students towards a long term research collaboration. Click here to apply for SPARK.

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Patents available for licensing/commercial exploitation

  1. Method and device for categorizing a stream control transmission protocol (SCTP) receiver terminal as a malicious SCTP receiver terminal (No: US 10129294 B2). 
  1. Method and device for detecting a malicious SCTP receiver terminal (No. WO2015118553A1).
  3. METHOD AND DEVICE FOR ROBUST MULTIPATH TRANSMISSION CONTROL PROTOCOL (MPTCP) AGAINST DATA SEQUENCE SIGNAL MANIPULATION ATTACK ((Indian patent 2024))