# Indian Institute of Technology Delhi

• New Delhi, New Delhi, India
Recent publications
A large amount of materials science knowledge is generated and stored as text published in peer-reviewed scientific literature. While recent developments in natural language processing, such as Bidirectional Encoder Representations from Transformers (BERT) models, provide promising information extraction tools, these models may yield suboptimal results when applied on materials domain since they are not trained in materials science specific notations and jargons. Here, we present a materials-aware language model, namely, MatSciBERT, trained on a large corpus of peer-reviewed materials science publications. We show that MatSciBERT outperforms SciBERT, a language model trained on science corpus, and establish state-of-the-art results on three downstream tasks, named entity recognition, relation classification, and abstract classification. We make the pre-trained weights of MatSciBERT publicly accessible for accelerated materials discovery and information extraction from materials science texts.
Autism spectrum is a brain development condition that impairs an individual’s capacity to communicate socially and manifests through strict routines and obsessive–compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting the timely formulation, revision, and implementation of treatment plans and goals. Additionally, the subjectivity of the clinician and a lack of data-driven decision-making affect treatment quality. We address these obstacles by applying two machine learning algorithms to recommend and personalize ABA treatment goals for 29 study participants with ASD. The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81–84%, with a normalized discounted cumulative gain of 79–81% (NDCG) compared to clinician-prepared ABA treatment recommendations. Additionally, we assess the two models’ treatment efficacy (TE) by measuring the percentage of recommended treatment goals mastered by the study participants. The proposed treatment recommendation and personalization strategy are generalizable to other intervention methods in addition to ABA and for other brain disorders. This study was registered as a clinical trial on November 5, 2020 with trial registration number CTRI/2020/11/028933.
Atomic force microscopy (AFM) is routinely used with indentation techniques to characterize the plastic deformation of materials. The accurate quantification of the features associated with the indent, which is used to quantify the hardness and indentation deformation mechanisms, depends on the sharpness of the AFM tip used for imaging. However, identifying the tip-sharpness of an atomic force microscope requires non-trivial measurements. Here, using machine learning, we develop a model to predict the tip sharpness of the AFM cantilever directly from the indent images. Further, we employ explainable machine learning models, such as integrated gradients and gradient shap, to interpret the features learned by the model. Altogether, we show that machine learning approaches can accelerate experiments by providing non-trivial information about the instrument performance, thereby enabling researchers to perform better quality experiments.
Wide Area Measurement Systems (WAMS) are well known for enhancing situational awareness of the grid using IEEE C37.118 phasors from Phasor Measurement Units (PMUs). Recently introduced IEC 61850-90-5 Routable Sample Values (R-SV) have proven promising to communicate timestamped synchrophasors to the control center, fulfilling the purpose. In wide area protection and control schemes, the communication of synchrophasors to the control center is also followed by sending back a supervisory decision signal (based on synchrophasor data) to the physical devices in the field. Development of WAMS testbeds for implementation and assessment of such schemes have often been addressed only from IEEE C37.118 PMU phasors’ perspective alone. This paper presents a comprehensive cyber–physical WAMS testbed capable of real-time communication of synchrophasors using both IEEE C37.118 PMU phasors and IEC 61850-90-5 R-SV. It also facilitates status and feedback signal communication between the control center and substations using IEC 61850-90-5 Routable GOOSE (R-GOOSE). The developed testbed integrates a real-time digital simulator (RTDS), industry-standard hardware devices such as PMUs, Phasor Data Concentrator (PDC), IEDs, Global Positioning System (GPS) synchronization clock, network components, and software components such as IEC 61850 emulator tools. It can be used for end-to-end implementation of a myriad of wide area monitoring protection and control (WAMPAC) applications. It can further facilitate vulnerability analysis of WAMS components, analyze the impact of cyber-attacks on critical applications, and then test and validate various security solutions for cyber resiliency of WAMPAC applications.
An adjacent vertex distinguishing (AVD-) total coloring of a graph G is a total coloring such that any two adjacent vertices u and v have distinct sets of colors, that is, C(u)≠C(v), where C(v) is the set of colors of the edges incident to v and the color of v. The adjacent vertex distinguishing (AVD)-total chromatic number of a graph G, χa″(G) is the minimum integer k such that there exists an AVD-total coloring of G using k colors. It is known that χa″(G)≥Δ+1, where Δ is the maximum degree of the graph. The AVD-total coloring conjecture states that for any graph G, χa″(G)≤Δ+3. In this paper, we study AVD-total coloring in split graphs. We verify the AVD-total coloring conjecture for split graphs and classify certain classes of split graphs according to their AVD-total chromatic number.
This work proposes the command tracking problem for uncertain Euler–Lagrange (EL) systems with multiple partial loss of effectiveness (PLOE) actuator faults. Compared to existing fault-tolerant controllers for EL systems, the proposed adaptive controller accounts for parametric uncertainties in the system and multiple time-varying actuator fault parameters. The proposed method can also handle an infinite number of fault cases. The closed-loop fault-tolerant system is treated as a switched dynamical system, and a switched system stability is established using multiple Lyapunov functions. It is shown that all signals are bounded in each sub-interval and at the switching instances, and asymptotic tracking can be obtained only for a finite number of fault occurrences, whereas tracking error is bounded for the infinite case. Finally, a simulation example on a robotic manipulator is presented to show the effectiveness of the proposed method.
This article presents an experimental and computational study of a forced draft cookstove having separate primary and secondary air fans, while utilizing pellets as fuel. A two-dimensional axisymmetric computational fluid dynamics model of the developed cookstove has been created in ANSYS Fluent to analyze the fluid flow, temperature distribution and heat loss from the different parts of the cookstove. The simulation results showed that more than one fourth of the total heat produced by the burning of fuel was being lost to the ambient environment through the outermost wall of cookstove. Also, the temperature of the outer wall of the cookstove was found to be higher than the temperature of secondary air being preheated in the annulus chamber. Therefore, the developed model was further modified by using glass wool insulation which resulted in an increment of 5.7% in thermal efficiency, while the emissions of CO and PM2.5 were reduced by 7.1% and 25.9%, respectively. The performance of the developed models have also been compared with other pellet based forced draft models available globally, and the thermal efficiency of the Mimi Moto cookstove was found to be highest followed by FD 2.2 model.
The aim of the presented review is based on recent past research and ongoing practices in the area of Photovoltaic Thermal (PVT) technology and thereby to explore the challenges and suggest practical solutions leading to the development of an efficient working system. Current development in technologies and its engineering applications are based on further development in materials, process parameters and modelling and simulations based solutions. These issues well addressed in review process. Investigative review intends to explore progressive advances in materials and their applications in PV/T devices, process parameters and optimizations models to reach optimum working efficiencies. Integration of PV/T systems with buildings given focused attention. Synergistic correlation among three subdomains can lead to the dynamic working system based on optimized utilization of thermal and photosensitive spectrum of solar radiation. The study elucidates the inception of PVT technology as a non-concentrating type of system, incubating the idea of utilizing it as a process enhancing system. Besides these, it is further extended to the concentrated type of PVT systems using varying material intervention with an essential milestone of attaining an efficient built environment system by conceptualizing multifaceted utilization of PVT as Building Integrated Photovoltaic Thermal (BIPVT).
Increasing demand of pure and accessible water and improper disposal of waste into the existing water resources are the major challenges for sustainable development. Nanoscale technology is an effective approach that is increasingly being applied to water remediation. Compared to conventional water treatment processes, silver nanotechnology has been demonstrated to have advantages due to its anti-microbial and oligodynamic (biocidal) properties. This review is focused on environmentally friendly green syntheses of silver nanoparticles (AgNPs) and their applications for the disinfection and microbial control of wastewater. A bibliometric keyword analysis is conducted to unveil important keywords and topics in the utilisation of AgNPs for water treatment applications. The effectiveness of AgNPs, as both free nanoparticles (NPs) or as supported NPs (nanocomposites), to deal with noxious pollutants like complex dyes, heavy metals as well as emerging pollutants of concern is also discussed. This knowledge dataset will be helpful for researchers to identify and utilise the distinctive features of AgNPs and will hopefully stimulate the development of novel solutions to improve wastewater treatment. This review will also help researchers to prepare effective water management strategies using nano silver-based systems manufactured using green chemistry.
Natural fiber reinforced polymer composites are being widely used in aerospace and automotive applications. These are due to their low density and cost, better biodegradability and higher specific strength and modulus, which replace E-glass fiber reinforced polymer composites. Moreover, quasi-isotropic properties can be achieved with randomly orientated short natural fiber composites. These composite structures are subjected to dynamic loading and exposed to different temperatures and frequencies during their service. In this research, the influence of fiber parameters (fiber length and loading) on quasi-static flexural and dynamic mechanical properties of jute/polyester composites was studied. Dynamic mechanical properties were investigated at different frequencies (0.2, 0.5, 1.0, 2.0 and 5 Hz) and temperatures from 35 °C up to 190 °C. It has found that the composite sample with 5 mm length and 25 wt% (5JP25) exhibited the highest flexural strength of 94 MPa and flexural modulus of 7.45 GPa. However, the composite sample with 15 mm length and 25 wt% (15JP25) exhibited a higher average storage modulus and a lower average loss factor indicating the good fiber–matrix adhesion, compared with the other samples. The Cole-Cole plot of the composite sample with the fiber weight percentage of 25 wt%, indicated a perfect semi-circle. Good correlations were observed between the flexural and initial storage moduli values of pure polyester and composites.
Probabilistic load forecasting (PLF) is necessary for power system operations and control as it assists in proper scheduling and dispatch. Moreover, PLF adequately captures the uncertainty whether that uncertainty is related to load data or the forecasting model. And there are not many PLF models, and those which exist are very complex or difficult to interpret. This paper proposes a novel neuroevolution algorithm for handling the uncertainty associated with load forecasting. In this paper, a new modified evolutionary algorithm is proposed which is used to find the optimal hyperparameters of 1D-Convolutional neural network (CNN). The probabilistic forecasts are produced by minimizing the mean scaled interval score loss function at 50%, 90% and 95% prediction intervals. The proposed neuroevolution algorithm is tested on a global energy forecasting competition (GEFCom-2014) load dataset, and two different experiments are conducted considering load only and one with load and temperature. Strong conclusions are drawn from these experiments. Also, the proposed model is compared with other benchmark models, and it has been shown that it outperforms the other models.
This work presents the development of a unified gradient electromechanical theory for thin flexoelectric beams considering both direct and converse flexoelectric effects. The two-way coupled electromechanical theory is developed starting from 3D variationals formulation by considering an electric field-strain-based free energy function. The formulation incorporates mechanical as well as electrical size effects. The coupled 3D theory is specialized to isotropic materials, and a 1D beam theory for composite flexoelectric curved beams is derived using the classical Kirchhoff assumptions. The beam theory is solved using a novel C2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C^2$$\end{document} continuous finite element framework for different loading and boundary conditions. Our finite element results are verified with analytical solutions for a simply supported flexoelectric beam operating in both actuator and sensor modes. The results are also compared with existing literature for the special case of a passive micro-beam. Our computational framework is subsequently used to perform various parametric studies to analyze the effect of electrical and mechanical length scale parameters, geometric parameters like the radius of curvature, flexoelectric layer thickness etc., on the response of the beam. Also, contribution of converse flexoelectricity in the overall response of the flexoelectric beam is compared with that of the direct effect. Our simulation results predict that the converse effect is significant (≈\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx$$\end{document} 10–25% of the direct effect) for a wide range of thickness and length scale parameter values. It is also observed that the effective electromechanical coupling coefficient, calculated in terms of the voltage developed across the flexoelectric layer thickness, is higher in flexoelectric materials compared to piezoelectric materials at smaller length scales (thickness of the order of a few microns). Our simulation results also agree well with the trends observed in recent experiments [1].
A robust understanding of structure–property relations of electrospun fibers is vital for device design. However, these relationships are inherently complex and hard to model using data from limited trial and error experiments. Machine learning has emerged as an efficient approach to model multidimensional relationships but fundamentally require diverse data to learn these relationships from. In this study, we present a novel Electrospun Fiber Experimental Attributes Dataset (FEAD) by collating experimental data from literature, developing new features, and complementing with our own experiments. Fiber diameter, a key parameter for controlling electrical and thermal properties of electrospun polyvinylidene fluoride (PVDF) polymer, is modeled using a large number of solution and electrospinning process experimental parameters using a multi-model machine learning approach. This is complemented with a model-agnostic interpretable game-theoretic approach to identify the relative and absolute relationships between the variables. Experimental attributes such as feed, polymer concentration, Flory-Huggins Chi parameter, and relative energy difference were found to be most impactful for modeling fiber diameter. This study overcomes several limitations in existing literature such as non-availability of meta datasets, application of latest machine learning techniques, and state-of-the-art approaches for interpreting these “black box” models, thus bridging the gap between experimental and computational studies. This improved ability to generalize structure–property relationships across any PVDF-polymer solvent system presents a promising ability to reduce expensive lab testing required for developing PVDF fibers of desired mechanical and electrical properties.
In this work, our motivation is to design a new collocation method based on Müntz–Legendre polynomial involving operational matrices to solve variable‐order stochastic fractional integro‐differential equation. We first prove the existence and uniqueness result for the solution of considered problem. The operational matrices are used to convert the variable‐order stochastic fractional integro‐differential equation into non‐linear system of algebraic equations. Further, the accuracy and efficiency of the proposed method are investigated through numerical experiments. Finally, we show the advantage of the variable‐order model over the constant order model through a real‐world example of mathematical finance.
The dry air intrusion over India during summer monsoon break phases is well known. It has been argued that this dry air originates over the desert regions of West Asia. Here, we show that a reservoir of saturation deficit air exists over the western and northern Arabian Sea during the summer monsoon season. The monsoon low-level jet (LLJ) that transports the moisture to continental India in the active phase of monsoon, transports the dry air to northern and central India during the break phase. The LLJ undergoes a weakening and broadening before the monsoon break phase in response to increased barotropic instability. The broadening of LLJ leads to an intensification of zonal flow in the poleward flanks and a weakening at the core. The development of a positive meridional SST gradient over the northern Arabian Sea favours an increase in the low-level zonal flow in the north, which advects the moist deficit air across northwest India. The dry air intrusion results in enhanced static stability over northern and central India and strong suppression of convection. Further, the enhanced static stability weakens zonal flow from the northern Arabian Sea region and leads to the demise of the dry air intrusion. Thus, internal mechanisms are responsible for the dry air intrusion over India and its termination during the break phase of the summer monsoon. An index for the dry air intrusion is constructed based on the saturation deficit transport. This dry air intrusion index is used to identify the dry air intrusion events during monsoon breaks during the 1981–2014 period. The statistics show that there were 34 (4) monsoon breaks with (without) dry air intrusion during 1981–2014 period. We also note that the dry air intrusion and the monsoon breaks are happening simultaneously, suggesting that it is difficult to establish a cause-effect relationship.
The photovoltaic (PV) power generation and cooling demand of the air conditioner are increased along with an increase in solar irradiation. Therefore, considering such fact, in this paper, PV power is integrated with the air conditioner to support the grid. With recent developments in power electronics, the air conditioning systems are operated in variable speed using variable frequency drive (VFD) technology. In this paper, taking the advantage of the VFD technology, PV power is directly injected into the DC bus of VFD using an isolated DC‐DC converter. In this methodology, due to the high‐frequency DC‐DC conversion, high power DC‐AC (50 Hz) stage is eliminated, and seamless power is transferred from PV generation to the load without interrupting the main operation of the air conditioner. Thus, the reliability of the system is enhanced with the reduction in overall cost, conversion losses and bulkiness. With the PV power support, the peak amplitude of the grid current is reduced and consequently the power consumption, reactive power intake from the grid, as well as the harmonics component of the grid current, are reduced. This scheme is used in rural or suburban areas where the solar profile is significant and air conditioner is extensively used.
A concern regarding the deterioration in power quality (PQ) has escalated with the high level of integration of renewable energy sources to the utility, primarily in the scenario of a weak distribution grid. This paper presents a modified complex variable filter (MCVF)‐based control to enhance the power quality performance of wind–solar photovoltaic (PV) and battery‐based microgrid under the weak grid and dynamic load conditions. An MCVF attenuates the harmonics and DC bias infected voltage and current and extracts the fundamental components from distorted current and voltage. The control scheme for the voltage source converter (VSC) is presented to meet the active power demand of load/grid and attenuates harmonics to control the power quality issues at the point of common coupling. To achieve the round, the clock operation of the microgrid, a battery is interfaced to support the local loads under off‐grid mode. Therefore, the presented VSC controller supplies the continuous power to the local loads and maintains the total harmonic distortion value of grid current within the PQ Standard IEEE‐519‐2014. The simulated and test results are presented to validate the VSC controller in different operating conditions.
Purpose Primary objective of this study was to retrospectively evaluate the potential of a range of qualitative and quantitative multiparametric features assessed on T2, post-contrast T1, DWI, DCE-MRI, and susceptibility-weighted-imaging (SWI) in differentiating evenly sampled cohort of primary-central-nervous-system-lymphoma (PCNSL) vs glioblastoma (GB) with pathological validation. Methods The study included MRI-data of histopathologically confirmed ninety-five GB and PCNSL patients scanned at 3.0 T MRI. A total of six qualitative features (three from T2 and post-contrast T1, three from SWI: thin-linear-uninterrupted-intra-tumoral-vasculature, broken-intra-tumoral-microvasculature, hemorrhage) were analyzed by three independent radiologists. Ten quantitative features from DWI and DCE-MRI were computed using in-house-developed algorithms. For qualitative features, Cohen’s Kappa-interrater-variability-analysis was performed. Z-test and independent t-tests were performed to find significant qualitative and quantitative features respectively. Logistic-regression (LR) classifiers were implemented for evaluating performance of individual and various combinations of features in differentiating PCNSL vs GB. Performance evaluation was done via ROC-analysis. Pathological validation was performed to verify disintegration of vessel walls in GB and rim of viable neoplastic lymphoid cells with angiocentric-pattern in PCNSL. Results Three qualitative SWI features and four quantitative DCE-MRI features (rCBVcorr, Kep, Ve, and necrosis-volume-percentage) were significantly different (p < 0.05) between PCNSL and GB. Best diagnostic performance was observed with LR classifier using SWI features (AUC-0.99). The inclusion of quantitative features with SWI feature did not improve the differentiation accuracy. Conclusions The combination of three qualitative SWI features using LR provided the highest accuracy in differentiating PCNSL and GB. Thin-linear-uninterrupted-intra-tumoral-vasculature in PCNSL and broken-intra-tumoral-microvasculature with hemorrhage in GB are the major contributors to the differentiation.
The present numerical study characterizes nucleate boiling heat transfer in ethanol from a single nucleation site on a horizontal base plate in the presence of a fluid-immersed solid copper torus. The torus lies above the base plate, with its axis centered along the nucleation site, and it is either kept stationary or subjected to forced oscillations in the vertical direction. A sharp-interface dual grid Level Set method (SI-DGLSM)-based in-house solver is used here, in which a Level Set-based Immersed Boundary Method (LSIBM) is used to impose the boundary condition at the moving solid-fluid interfaces. For nucleate boiling in the presence of a stationary torus, enhancement in Nusselt number is observed due to thinning of the thermal boundary layer under the torus and thickening of the thermal boundary layer near the bubble. In the presence of torus oscillations, a lock-on regime is observed at optimal actuation parameters, for which the frequency of bubble departure synchronizes with the actuation frequency of the torus. A significant increase in Nusselt number is observed within the lock-on regime due to active pumping of superheated liquid towards the nucleating bubble during bubble growth. Analysis of heat flux partitioning shows that the enhancement in Nusselt number may be mainly attributed to higher sensible heat flux in the presence of torus oscillations, especially near the lock-on regime. This work demonstrates a novel way for enhancing heat transfer in the single-bubble nucleate boiling regime via externally actuated solid objects immersed in the liquid.
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• Kusuma School of Biological Sciences
• Centre for Biomedical Engineering
• Department of Mechanical Engineering
• Department of Chemical Engineering
• SeNSE - Center for Sensors, iNstrumentation and cyber-physical Systems Engineering
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