886 research outputs found
Analysing a central implementation of an electronic lab notebook (eLabFTW) at the University of Innsbruck
No full textauthor: Rohit KarthikeyanMasterarbeit University of Innsbruck 202
Analysing a central implementation of an electronic lab notebook (eLabFTW) at the University of Innsbruck
No full textauthor: Rohit KarthikeyanMasterarbeit University of Innsbruck 202
Utilizing photoswitchable lipids to photoregulate facilitated ion transport across membranes
No full textAuthor Rohit YadavDissertation Johannes Kepler Universität Linz 2025Arbeit gesperr
Utilizing photoswitchable lipids to photoregulate facilitated ion transport across membranes
No full textAuthor Rohit YadavDissertation Johannes Kepler Universität Linz 2025Arbeit gesperr
Derandomizing Isolation Lemma for K3,3-free and K5-free Bipartite Graphs
No full textThe perfect matching problem has a randomized NC algorithm, using the celebrated Isolation Lemma of Mulmuley, Vazirani and Vazirani. The Isolation Lemma states that giving a random weight assignment to the edges of a graph ensures that it has a unique minimum weight perfect matching, with a good probability. We derandomize this lemma for K3,3-free and K5-free bipartite graphs. That is, we give a deterministic log-space construction of such a weight assignment for these graphs. Such a construction was known previously for planar bipartite graphs. Our result implies that the perfect matching problem for K3,3-free and K5-free bipartite graphs is in SPL. It also gives an alternate proof for an already known result – reachability for K3,3-free and K5-free graphs is in UL
COVID-19: Time Series Datasets India versus World
No full textThis dataset consists of COVID-19 time series data of India since March 24th, 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details.
.
The Authors can Refer to and CITE our latest Papers on COVID:
1. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming." Chaos, Solitons & Fractals (2020): 109945.
2. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries." Chaos, Solitons & Fractals 140 (2020): 110118.
3. Mousavi, Mohsen, et al. "COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features." IEEE Computational Intelligence Magazine 15.4 (2020): 34-50.
.
[Dataset is updated Once a Week
COVID-19: Time Series Datasets India versus World
No full textThis dataset consists of COVID-19 time series data of India since 24th March 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details.
.
The Authors can Refer to and CITE our latest Papers on COVID:
1. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming." Chaos, Solitons & Fractals (2020): 109945.
2. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries." Chaos, Solitons & Fractals 140 (2020): 110118.
3. Mousavi, Mohsen, et al. "COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features." IEEE Computational Intelligence Magazine 15.4 (2020): 34-50.
.
[Dataset is updated Once a Week
COVID-19: Time Series Datasets India versus World
No full textThis dataset consists of COVID-19 time series data of India since March 24th, 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details.
.
The Authors can Refer to and CITE our latest Papers on COVID:
1. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming." Chaos, Solitons & Fractals (2020): 109945.
2. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries." Chaos, Solitons & Fractals 140 (2020): 110118.
3. Mousavi, Mohsen, et al. "COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features." IEEE Computational Intelligence Magazine 15.4 (2020): 34-50.
.
[Dataset is updated Once a Week
COVID-19: Time Series Datasets India versus World
No full textThis dataset consists of COVID-19 time series data of India since 24th March 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details.
.
[Dataset is updated Twice a Week]
The Authors can Refer to and CITE our latest Papers on COVID:
1. Rohit Salgotra, Mostafa Gandomi, Amir H Gandomi. "Evolutionary Modelling of the COVID-19 Pandemic in Fifteen Most Affected Countries" Chaos, Solitons \& Fractals: (2020). https://doi.org/10.1016/j.chaos.2020.110118
2. Rohit Salgotra, Mostafa Gandomi, Amir H Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming" Chaos, Solitons \& Fractals: (2020). https://doi.org/10.1016/j.chaos.2020.10994
COVID-19: Time Series Datasets India versus World
No full textThis dataset consists of COVID-19 time series data of India since 24th March 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details.
.
The Authors can Refer to and CITE our latest Papers on COVID:
1. Rohit Salgotra, Mostafa Gandomi, Amir H Gandomi. "Evolutionary Modelling of the COVID-19 Pandemic in Fifteen Most Affected Countries" Chaos, Solitons \& Fractals: (2020). https://doi.org/10.1016/j.chaos.2020.110118
2. Rohit Salgotra, Mostafa Gandomi, Amir H Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming" Chaos, Solitons \& Fractals: (2020). https://doi.org/10.1016/j.chaos.2020.109945
.
[Dataset is updated Once a Week
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