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"Kumar, Ranjan"
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In search of Indian English : history, politics and indigenisation
\"This book presents a historical account of the development of an acrolectal variety of the English language in colonial India. It highlights the phenomenon of Indianisation of the English language and its significance in the articulation of the Indian identity in pre-Independence India. This volume also discusses the socio-cultural milieu in which English became the first choice for writers and political leaders. Using examples primarily from the writings of Rammohan Roy, Bankimchandra, Krupabai Satthianadhan and Gandhi and from the speeches of Vivekananda, Tagore and Subhas Bose, this book argues that prose written in English in the nineteenth and the early twentieth century scripted a nationalist discourse through its appropriation of the coloniser's language. It also examines how these works, which absorbed elements of Indian culture and languages, paved the path for the emergence of Indian English as a distinct dialect of the English language. This book will be useful for teachers, scholars and students of English literature, linguistics, and cultural studies. It will also be of use to general readers interested in the history of the English language and the history of modern India\"-- Provided by publisher.
Book
Controlling period-doubling route to chaos phenomena of roll oscillations of a biased ship in regular sea waves
Dynamics, control, and stability of roll oscillations of a biased ship in regular sea waves are investigated. The ship under roll oscillation is modelled as the classical Helmholtz–Duffing oscillator with strongly nonlinear asymmetric restoring moment characteristics. The incremental harmonic balance method, amended with a pseudo-arc-length continuation approach, is employed to obtain the uncontrolled and controlled frequency responses. The primary and subharmonic responses of the uncontrolled system are examined through the period-doubling route to chaos path. The same ship roll model is then investigated under state feedback control with time delay. In the control scheme, a moving weight is actuated by the delay controller to generate the anti-rolling moment. The stability of periodic responses is studied using the semi-discretization method with extended Floquet theory. Bifurcation points on periodic response branches are identified from the eigenvalue study. The effect of gains and time delay in the feedback loop in controlling the primary and subharmonic responses is investigated. Several chaotic responses obtained through period-doubling route to chaos paths are successfully controlled. Stable, periodic, and steady-state solutions obtained from the IHB method are verified by numerical integration of the equation of motion as and when applicable. Solutions are aided with phase portrait, Poincaré map, time history, and Fourier spectrum for better clarity. It is shown that appropriate selections of control parameters can effectively reduce the roll amplitude to a great extent with the improved measure of stability.
Journal Article
Understanding data analytics and predictive modelling in the oil and gas industry
Covers aspects of data science and predictive analytics used in oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital well, value chain integration, crude basket forecasting and so forth.
BOOK
Advancements in predicting soil liquefaction susceptibility: a comprehensive analysis of ensemble and deep learning approaches
Liquefaction is a phenomenon that occurs when there is a loss of strength in wet and cohesionless soil due to higher pore water pressures, and as a result, the effective stress is reduced due to dynamic loading. A construction site should first investigate the site for liquefaction, and for analyzing liquefaction, the most accurate method should be selected, which provides the most accurate results. In this work, a detailed investigation is performed on the effectiveness of ensemble learning and deep learning (DL) models in assessing the liquefaction susceptibility of soil deposits from a large database consisting of cone penetration test (CPT) measurements and field liquefaction performance observations of historical earthquakes. The performance of the developed models is assessed via several comprehensive performance fitness error matrices (PFEMs), including precision, accuracy, recall, specificity, F1 score, MCC, BA, receiver operating characteristic (ROC) curve analysis, and area under the curve (AUC). Accuracy and validation loss curves were also plotted for all the proposed models. PFEMs are calculated, and a comparative study is performed for all the proposed methods. The BI-LSTM model has the highest accuracy, with 0.9791 in training and 0.8889 in testing, indicating strong predictive ability and good generalizability. LSTM follows closely with training and testing accuracies of 0.9433 and 0.8750, respectively, offering consistent performance. XGBoost also performs well, achieving 0.9194 in training and 0.8750 in testing, reflecting its robustness in handling complex patterns. In contrast, RF displays a significant discrepancy between the training (0.9373) and testing (0.8681) accuracies. Overall, BI-LSTM emerges as the most reliable model for assessing liquefaction potential, with LSTM, XGBoost and the RF also proving effective. Each model can offer unique strengths, with BI-LSTM and LSTM excelling at learning sequential dependencies, whereas XGBoost and RF provide powerful and often interpretable results from structured tabular data. This study advances the development of robust tools for assessing liquefaction hazards, thereby enhancing strategies for seismic risk mitigation.
Journal Article
Handbook of Himalayan ecosystems and sustainability
\"Volume 1: Handbook on Spatio-Temporal Monitoring of Forests and Climate is aimed to describe the recent progress and developments of geospatial technologies (Remote Sensing and GIS) for assessing, monitoring and managing fragile Himalayan ecosystems and its sustainability under climate change. It is a collective research contribution from renowned researchers and academicians working in the Hindu Kush Himalayan (HKH) mountain range. The Himalayas ecosystems have been facing substantial transformation due to severe environmental conditions, land transformation, forest degradation and fragmentation. The authors utilized satellite datasets and algorithms to discuss the intricacy of Land use Land cover change, forest and agricultural ecosystems, canopy height estimation, above-ground biomass, wildfires, carbon sequestration, and landscape restoration. Furthermore, the potential impacts of climate change on ecosystems, biodiversity and future food and nutritional security are also addressed including the impact on the livelihood of people of the Himalayas. This comprehensive Handbook explains the advanced geospatial technologies for mapping and management of natural resources of the Himalayas\"-- Provided by publisher.
Book
Biofertilizers function as key player in sustainable agriculture by improving soil fertility, plant tolerance and crop productivity
Current soil management strategies are mainly dependent on inorganic chemical-based fertilizers, which caused a serious threat to human health and environment. The exploitation of beneficial microbes as a biofertilizer has become paramount importance in agriculture sector for their potential role in food safety and sustainable crop production. The eco-friendly approaches inspire a wide range of application of plant growth promoting rhizobacteria (PGPRs), endo- and ectomycorrhizal fungi, cyanobacteria and many other useful microscopic organisms led to improved nutrient uptake, plant growth and plant tolerance to abiotic and biotic stress. The present review highlighted biofertilizers mediated crops functional traits such as plant growth and productivity, nutrient profile, plant defense and protection with special emphasis to its function to trigger various growth- and defense-related genes in signaling network of cellular pathways to cause cellular response and thereby crop improvement. The knowledge gained from the literature appraised herein will help us to understand the physiological bases of biofertlizers towards sustainable agriculture in reducing problems associated with the use of chemicals fertilizers.
Journal Article
Exploring the unfolding pathways of protein families using Elastic Network Model
We explore how a protein’s native structure determines its unfolding process. We examine how the local structural features, like shear, and the global structural properties, like the number of soft modes, change during unfolding. Simulations are performed using a Gaussian Network Model (GNM) with bond breaking for both thermal and force-induced unfolding scenarios. We find that unfolding starts in areas of high shear in the native structure and progressively spreads to the low shear regions. Interestingly, analysis of single domain protein families (Chymotrypsin inhibitor and Barnase) reveal that proteins with distinct unfolding pathways exhibit divergent behavior of the number of soft modes during unfolding. This suggests that the number of soft modes might be a valuable tool for understanding thermal unfolding pathways. Additionally, we found a strong link between a protein’s overall structural similarity (TM-score) and its unfolding pathways, highlighting the importance of the native structure in determining how a protein unfolds.
Journal Article
Electronically Tunable Memristor Emulator Implemented Using a Single Active Element and Its Application in Adaptive Learning
In recent times, much-coveted memristor emulators have found their use in a variety of applications such as neuromorphic computing, analog computations, signal processing, etc. Thus, a 100 MHz flux-controlled memristor emulator is proposed in this research brief. The proposed memristor emulator is designed using a single differential voltage current conveyor (DVCC), three PMOS transistors, and one capacitor. Among three PMOS transistors, two transistors are used to implement an active resistor, and one transistor is used as the multiplier required for the necessary memristive behaviors. Through simple adjustment of the switch, the proposed emulator can be operated in incremental as well as decremental configurations. The simulations are performed using a 180 nm technology node to validate the proposed design and are experimentally verified using AD844AN and CD4007 ICs. The memristor states of the proposed emulator are perfectly retained even in the absence of external stimuli, thereby ascertaining the non-volatility behavior. The robustness of the design is further analyzed using the PVT and Monte Carlo simulations, which suggest that the circuit operation is not hindered by the mismatch and process variations. A simple neuromorphic adaptive learning circuit based on the proposed memristor is also designed as an application.
Journal Article
Machine learning-driven nonlinear analysis of inclusion effects in aluminium alloys
The impact of inclusions on the properties of aluminum alloys is comprehensively analyzed in this study using machine learning. The analysis indicates that inclusion size is the primary factor influencing mechanical performance, contributing a significant amount to the degradation of tensile strength in comparison to density’s 35% influence, as quantified by SHAP value analysis. Nonlinear regression modeling identifies critical thresholds, resulting in an 8 MPa/µm strength reduction for inclusions below 5 μm and a stabilization at 275 MPa for sizes exceeding 10 μm. Cluster analysis effectively separates material samples into high-strength (325 ± 10 MPa) and low-strength (285 ± 15 MPa) groups. A comparative model evaluation confirms Random Forest’s superior predictive capability, with an 18 MPa RMSE compared to Gradient Boosting’s 22 MPa. The research quantifies substantial property improvements that can be achieved through inclusion control. The strength is increased by 25 MPa when the size is reduced from 10 μm to 5 μm. However, the fatigue Life analysis demonstrates severe degradation beyond 10 μm, with a decline to 0.5 × 10
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cycles compared to 1.3 × 10⁶ cycles at 5 μm in comparison. Corrosion behavior is characterized by exponential dependence, with rates increasing from 0.02 mm/yr at 5 μm to 0.055 mm/yr at 15 μm. A robust framework for comprehending inclusion-property relationships and offering actionable quality control parameters for industrial applications, particularly in aerospace and automotive sectors where precise material performance is critical, is provided by the study’s machine learning approach, which combines predictive modeling with advanced visualization techniques.
Journal Article
COVID-19 pandemic: Insights into structure, function, and hACE2 receptor recognition by SARS-CoV-2
Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a newly emerging, highly transmissible, and pathogenic coronavirus in humans that has caused global public health emergencies and economic crises. To date, millions of infections and thousands of deaths have been reported worldwide, and the numbers continue to rise. Currently, there is no specific drug or vaccine against this deadly virus; therefore, there is a pressing need to understand the mechanism(s) through which this virus enters the host cell. Viral entry into the host cell is a multistep process in which SARS-CoV-2 utilizes the receptor-binding domain (RBD) of the spike (S) glycoprotein to recognize angiotensin-converting enzyme 2 (ACE2) receptors on the human cells; this initiates host-cell entry by promoting viral-host cell membrane fusion through large-scale conformational changes in the S protein. Receptor recognition and fusion are critical and essential steps of viral infections and are key determinants of the viral host range and cross-species transmission. In this review, we summarize the current knowledge on the origin and evolution of SARS-CoV-2 and the roles of key viral factors. We discuss the structure of RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 and its significance in drug discovery and explain the receptor recognition mechanisms of coronaviruses. Further, we provide a comparative analysis of the SARS-CoV and SARS-CoV-2 S proteins and their receptor-binding specificity and discuss the differences in their antigenicity based on biophysical and structural characteristics.
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