Indian Institute of Technology Gandhinagar

We study IndIGO-D, a decihertz gravitational-wave mission concept, focusing on a specific configuration in which three spacecraft fly in formation to form an L-shaped interferometer in a heliocentric orbit. The two orthogonal arms share a common vertex, providing a space-based analogue of terrestrial Michelson detectors, while operating in an optimised configuration that yields ppm-level arm-length stability. Assuming 1000 km arm length, we analyse the orbital motion and antenna response, and assess sensitivity across the [0.1 - 10] Hz band bridging LISA and next-generation ground-based interferometers. Using fiducial sensitivity curves provided by the IndIGO-D collaboration, we compute horizon distances for different source classes. Intermediate-mass black-hole binaries with masses 10^{2} - 10^{3} \, M_\odot are detectable to redshifts z \sim 10^{3}, complementing the reach of LISA and terrestrial detectors. Binary neutron star systems are observable to a horizon distance of z \lesssim 0.3, allowing continuous multi-band coverage with Voyager-class interferometers from the decihertz regime to merger. A Bayesian parameter-estimation study of a GW170817-like binary shows that the sky localization area improves from \sim 21 \,\mathrm{deg}^2 at one month to 0.3 \,\mathrm{deg}^2 at six hours pre-merger! These sky areas are readily tiled by wide-field time-domain telescopes such as the Rubin Observatory, whose 9.6 \,\mathrm{deg}^2 field of view and r-band depth enable high-cadence, repeated coverage of GW170817-like kilonovae at this distance and beyond. IndIGO-D exploits the rapid evolution of binaries in the decihertz band to bridge the gap between millihertz and terrestrial observations, enabling early warnings on timescales from months to hours and enhancing the prospects for multi-band and multi-messenger discoveries.

Sitting in a packed Gyan Mancha auditorium in Kolkata as Harold Pinter’s work made a comeback in Bengali theater, I was transported to a similar evening two decades ago when soon after his Nobel Prize–winning (2005) The Homecoming was staged at the same venue in English. There have been very few Pinter productions in Bengali theater. Directed by Arindam Mukherjee and Madhurima Goswami and produced by Anya Theater, Caretaker (the definite article dropped from the English title to make it sound colloquial in Bangla) breaks that 20-year silence. The production has a stellar cast with two of the finest contemporary Bengali theater and film actors—Anirban Chakrabarti and Debesh Roy Chowdhuri—essaying Aston (changed to Shankar) and Davies (changed to Adhir) and Tathagata Chowdhury playing Mick, in this version Shankar’s younger brother Tegra.

Fulvic acid is a common humic contaminant found in soil-matrix forensic samples and is a highly effective PCR inhibitor. This work uses computational analysis to investigate its molecular interaction with Taq DNA polymerase and evaluate nanoparticle-based facilitation strategies. Molecular docking and molecular dynamics simulations proposed selective binding of fulvic acid with catalytic residues (ARG587, ASP610, PHE667, TYR671, ASP785, GLU786), affecting DNA binding, active-site geometry, and Mg²⁺ coordination. Tryptophan quenching assays confirmed a low-affinity (∼251.6 ± 2.2 µM), reversible interaction. Functionally, fulvic acid reduced the peak height by > 80 % within the DNA profile and affected critical loci-D18S51, Penta D, D22S1045, FGA, CSF1PO, and D21S11. Nano-based facilitators-bare and BSA-coated AuNPs-were compared with the commonly employed BSA; the latter showed a statistically significant (p < 0.0001) 25 % TPH improvement compared to the inhibited level but did not affect allelic balance. These findings establish the inhibitory mode for fulvic acid and confirm the effectiveness of BSA-coated AuNPs as a scalable solution to restore PCR efficiency to environmentally compromised forensic DNA samples over highly concentrated application of BSA as a facilitator.

Motor adaptation driven by sensory prediction errors (SPEs) is often regarded as an automatic, implicit process that operates independent of reward. However, in most past work, reward and performance outcomes (success / failure) have been intrinsically confounded, making it unclear whether explicitly delivered reward per se influences SPE-driven learning. Here, we used an error-clamp paradigm to dissociate reward from both error and task outcome, enabling us to directly test whether explicit reward modulates implicit adaptation. Participants performed reaching movements under clamped visual feedback that produced a constant SPE, while being instructed to ignore the cursor. In two experiments, reward was delivered when the unseen hand successfully intersected the reach target. In an eight-target task, reward did not alter the overall magnitude or time course of adaptation. However, trial-level analyses revealed that rewarded movements were followed by smaller trial-to-trial updates, reduced variability, and a cumulative suppression of adaptive adjustments. Consistent with this result, individuals who experienced reward more frequently exhibited less asymptotic learning. In our second experiment using a simplified, two-target task, these trial-level effects accumulated to produce robust reductions in both asymptotic adaptation and aftereffects in the rewarded groups. Across both experiments, longer streaks of rewarded trials predicted progressively weaker SPE-driven updating. Collectively, these findings demonstrate that implicit adaptation is not insulated from reward signals. Instead, explicit reward appears to attenuate sensitivity to SPEs, stabilizing motor output particularly when the task structure allows consistent action-reward associations. We conclude that motivational signals can gate the expression of error-driven motor adaptation.

We investigate the automatic generation of Wikipedia articles as an alternative to its manual creation. We propose a framework for creating a Wikipedia article for a named entity which not only looks similar to other Wikipedia articles in its category but also aggregates ihe diverse aspects related to that named entity from the Web. In particular, a semi-supervised method is used for determining the headings and identifying the content for each heading in the Wikipedia article generated. Evaluations show that articles created by our system for categories like actors are more reliable and informative compared to those generated by previous approaches of Wikipedia article automation.

The Stanford University Unstructured design is a tool for solving multi-disciplinary problems governed by the Partial Differential Equations on general, unstructured grids. The Overset algorithm ‘Overset Parallel Engine for AeRodynamic Applications’ developed at Institute of High Performance Computing, Singapore allows dynamic overset grid capabilities with any number of bodies using parallel processing. This paper presents the coupling of OPERA with SU2 for complex multi- body aerospace problems. The modified software suite is tested for the flow past a three dimensional sphere. The results compared with analytical results support the potential and working of the algorithm compared to a non-overlapping grid. The same problem is analyzed for moving overlapping grid. The velocity contours at different time instance are shown and are found to be continuous across the overlapping grid.

Carbon Capture and Storage (CCS) is crucial for reducing emissions in hard-to-decarbonize sectors such as cement, steel, and power generation. For India, heavily reliant on coal, CCS provides a means to lower emissions while maintaining energy security. While global CCS deployment has gained momentum through policy support and funding, high costs remain a significant challenge. In India, basalt formations, especially in the Deccan Traps, present a promising and cost-effective option for CO2 storage through mineralization. India's CCS policy is still in development, with short-term incentives like carbon credits and potential long-term goals, such as carbon taxes, being considered. To scale CCS, India requires robust funding mechanisms, technological innovation, and large-scale CCS clusters. International collaboration and investment will be essential in addressing financial and technological barriers. With the right infrastructure, research, and policy framework, CCS can become a key component of India's strategy to meet its net-zero target by 2070.

We examine the feasibility of using synthetic medical data generated by GANs in the classroom, to teach data science in health informatics. We present an end-to-end methodology to retain instructional utility, while preserving privacy to a level, which meets regulatory requirements: (1) a GAN is trained by a certified medical-data security-aware agent, inside a secure environment; (2) the final GAN model is used outside of the secure environment by external users (instructors or researchers) to generate synthetic data. This second step facilitates data handling for external users, by avoiding de-identification, which may require special user training, be costly, and/or cause loss of data fidelity. We benchmark our proposed GAN versus various baseline methods using a novel set of metrics. At equal levels of privacy and utility, GANs provide small footprint models, meeting the desired specifications of our application domain. Data, code, and a challenge that we organized for educational purposes are available.