Indian Institute of Technology Gandhinagar

Reliable and continuous water level and streamflow records are essential for accurate hydrological modeling and informed water resource management, yet observations often suffer from substantial data gaps. Existing data gap-filling methods use observations from upstream or nearby stations with local climate records. However, their applicability to long-term, large-scale hydrological networks over diverse climate zones with extended data gaps remains limited. Here, we develop a robust framework that integrates geomorphological, meteorological, and hydrological parameters with the Quantile Regression Forests to fill the gaps in daily streamflow observations at 343 stations across Peninsular India from 1961 to 2021. To reconstruct the streamflow at ungauged locations, we employed transfer learning through k-means clustering to group hydromorphologically similar catchments. Our data gap-fill method performs well with Nash-Sutcliffe Efficiency (NSE) of more than 0.8 at 72% and 90% of stations for water level and streamflow, respectively. We found that 50 and 100-year return period events are highly sensitive to data gaps, often leading to significant under- or overestimation by more than 40%. Overall, the gap-filled streamflow and water-level data sets for the 1961–2021 period provide a robust foundation for hydrological modeling, climate change impact assessments, and water resource planning across Peninsular India.

In this paper, we introduce the notion of (strictly) semimonotone matrices of exact order k, where 0≤𝑘≤𝑛, and explore their properties. We fully characterize the 3×3 (strictly) semimonotone matrices of exact order 2, and show that the class of 3×3 semimonotone matrices of exact order 2 forms a subclass of inverse 𝐙-matrices. We further investigate 𝑛×𝑛 (strictly) semimonotone matrices of exact order 2, with emphasis on their identification and construction, and establish that every 𝑛×𝑛 semimonotone 𝐙-matrices of exact order 2 is invertible. Additionally, we show that when n−k=1, the class of (strictly) semimonotone matrices of exact order k is a subclass of 𝐙-matrices.

By many estimates, the world has just crossed the point where more than half the global population is urban, a trend driven by rapid urbanization in developing countries. Urban centres offer economies of scale in terms of productive enterprise and public investment. Cities are social melting pots, centres of innovation, and drivers of social change. However, cities are also marked by social differentiation, poverty, conflict, and environmental degradation. These are all issues that not only matter to cities but also lie at the heart of development. As such, the time is right to consider afresh the relationship between cities and development. This volume presents a significant new collection of multidisciplinary papers focused on urbanization and its implications for development. It raises four questions: What is so special about the urban context? Why is urbanization and urban growth important to development at the present conjuncture? What are the strengths and limitations of our current state of knowledge about urbanization and development from the policy perspective? How can a multidisciplinary perspective on the urban context add value to development research and policy?.

High specific stiffness materials are used to design the space payload components. These components should sustain the extreme environmental conditions throughout their life cycle, without failure. Space missions need lightweight materials which are mechanically strong with high thermal and electric conductivities. Carbon fiber reinforced polymer (CFRP) offers considerable mass saving and high strength, which is widely used for space payload components. However, it has limitations to replace the traditional space-qualified materials due to its low conductivity. Carbon Nanotubes (CNTs) are efficient with greater electrical and thermal conductivities. For CNTs to be seen as effective reinforcements for attaining high strength and conductivity of polymer composites, they need to meet the criteria of being well-dispersed by the solution mixing method. The quality of the CNT nanocomposite relies upon several parameters like the type of CNTs, purity, aspect ratio, amount of loading, alignment and interfacial adhesion between the nanotube and polymer. The performance of the CNT-CFRP composite depends on the successful execution of the processing technique. It has been intended in this review paper to highlight the enhancement of the mechanical, thermal and electrical properties of the composite, and the challenges in achieving it. An attempt has been made to optimize the process parameters to fabricate space payload components which can be excellent alternatives to the existing high-density materials. Moreover, this review research is the need of the hour for prominent space agencies such as ISRO and NASA for their future inter-planetary missions, where payload weight needs to be kept light without making any compromise on the performance index.

Residential customers play an important role in total power consumption with a highly varying demand profile depending upon climatic conditions. With recent technological advancements, it is possible to deploy Renewable Energy Sources (RES) at customer end to support their power demand. To take advantage of this opportunity, it is important to understand the load profile of residential users. Also, deployment of RES at customer end will lead to a more decentralized power grid adding to the complexity of the system. Modelling and analysis of the resulting system is therefore necessary for optimal electric energy utilization. Bottom up approaches estimates the demand profile of customers which is then utilized to obtain consumption patterns at distribution and generation levels. Demand profile of residential users could be estimated using a technique known as Non-intrusive Load Monitoring (NILM). NILM disaggregates the aggregate power consumption measured at utility entry point to identify the operational states of individual appliance. In this work, we propose an NILM technique which uses Neural Network (NN) along with Hidden Markov Model (HMM) to detect the operating state of an appliance. The appliance level consumption obtained using this approach for a community can be then utilized for developing bottom up models for energy system simulation.