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We perform numerical simulations to study the dynamics of the entry of hydrophobic spheres in a pool of water using ANSYS. To track the air-water interface during the translation of the sphere in the pool of water, we use the volume of fluid (VOF) model. The continuum surface force (CSF) method computes the surface tension force. To simulate the hydrophobic surface properties, we also include wall adhesion. We perform simulations with different diameters and impact speeds of the sphere. Our simulations capture the formation of different types of air cavities, pinch-offs of these cavities, and other finer details similar to the experiments performed at the same parameters. Finally, we compare the coefficient of drag among the different hydrophobic cases. We further perform simulations of hydrophilic spheres impacting the pool of water and compare the drag coefficient with the analogous hydrophobic cases. We conclude that the spheres with hydrophobic surfaces have a lower drag coefficient than their hydrophilic counterparts. This lower drag of the hydrophobic spheres is attributed to the formation of the air cavity by the hydrophobic surfaces while translating through the pool of water, which reduces the area of the sphere in contact with water. In contrast, no such air cavity forms in the case of hydrophilic spheres.

We report mechanical, optical and thermoelectric properties of recently fabricated Janus BiTeCl monolayer using density functional and semi-classical Boltzmann transport theory. Janus BiTeCl monolayer exhibits a direct bandgap, high carrier mobility (~10$^3$ cm$^2$V$^{-1}$s$^{-1}$) and high optical absorption in the UV-visible region. The mechanical behavior of the Janus BiTeCl monolayer is nearly isotropic having an ideal tensile strength ~ 15 GPa. The higher value of the Gruneisen parameter ($\gamma$), a low value of phonon group velocity (vg), and very little phonon scattering time ($\tau_p$) lead to low lattice thermal conductivity (1.46 W/mK) of Janus BiTeCl monolayer. The combined effect of thermal conductivity and electronic transport coefficients of Janus BiTeCl monolayer results in the figure of merit (ZT) in the range of 0.43-0.75 at 300-500 K. Our results suggest Janus BiTeCl monolayer be a potential candidate for optoelectronic and moderate temperature thermoelectric applications., Comment: 21 pages, 7 figures, Journal of Physics and Chemistry of Solids, 2022

Highly efficient, environmental friendly and renewable sources of energy are of great need today to combat with increasing energy demands and environmental pollution. In this work, we have investigated the novel 2D allotropes i.e., $\beta$-Te$_2$X (X = S, Se) using first-principles calculations and study their potential applications in light harvesting devices. Both the monolayers possess to have the high stability and semiconducting nature with an indirect band gap. The high carrier mobilities and excellent optical absorption of these monolayers make them potential candidates for solar conversion applications. We have proposed the type-II heterojunction solar cells and calculated their power conversion efficiencies (PCEs). The small conduction band offset and appropriate band gap of donor material in case of $\beta$-Te$_2$S(S-Side)/$\alpha$-Te$_2$S(Te-Side) heterojunction results in the PCE of ~ 21%. In addition to that, the band alignments of these monolayers properly engulf the redox potentials of the water. The overpotentials required to trigger the hydrogen reduction (HER) and water oxidation (OER) half reactions reveal that HER and OER preferred the acidic and neutral mediums, respectively. The calculated solar-to-hydrogen (STH) efficiencies of $\beta$-Te$_2$S ($\beta$-Te$_2$Se) monolayers come out to be ~ 13 % (~12 %), respectively, which implies their practical applications in water splitting. Thus, our work provides strong evidence regarding the potential applications of these materials in the field of light harvesting devices., Comment: 25 pages, 8 figures. Journal of Materials Chemistry C, 2023

In recent years, researchers have manifested their interest in the two-dimensional (2D) mono-elemental materials of group-VI elements because of their excellent optoelectronic, photovoltaic and thermoelectric properties. Despite the intensive recent research efforts, there is still a possibility of novel 2D allotropes of these elements due to their multivalency nature. Here, we have predicted a novel 2D allotrope of tellurium (ring-Te) using density functional theory. Its stability is confirmed by phonon and ab-initio molecular dynamics simulations. The ring-Te has an indirect band gap of 0.69 eV (1.16 eV) at PBE (HSE06) level of theories and undergoes an indirect-direct band gap transition under the tensile strain. The higher carrier mobility of holes (~103cm$^2$V$^{-1}$s$^{-1}$), good UV-visible light absorption ability and low exciton binding (~0.35 eV) of ring-Te gives rise to its potential applications in optoelectronic devices. Further, the electrical contact of ring-Te with topological Dirac semimetal (sq-Te) under the influence of electric field shows that the Schottky barriers and contact types can undergo transition from p-type to n-type Schottky contact and then to ohmic contact at higher electric field. Our study provides an insight into the physics of designing high-performance electrical coupled devices composed of 2D semiconductors and topological semimetals., Comment: 25 pages, 10 fifures, Nanoscale, 2023

Highly efficient and sustainable resources of energy are of great demand today to combat with environmental pollution and the energy crisis. In this work, we have examined the novel 2D Janus AsTeX (X = Cl, Br and I) monolayers using first-principles calculations and explore their potential energy conversion applications. We have demonstrated the thermal, energetic, dynamic and mechanical stability of AsTeX (X = Cl, Br, and I) monolayers. Janus AsTeX (X = Cl, Br and I) monolayers are indirect bandgap semiconductors with high carrier mobilities and excellent visible light optical absorption. Our findings demonstrate that the Janus AsTeCl and AsTeBr monolayers exhibits low lattice thermal conductivity and excellent electronic transport properties obtained using semi-classical Boltzmann transport theory including various scattering mechanism. Additionally, the redox potential of water is adequately engulfed by the band alignments of the AsTeCl and AsTeBr monolayers. The water splitting process under illumination can proceeds spontaneously on Janus AsTeBr monolayer, while a minimal low external potential (0.26-0.29 eV) is required to trigger water splitting process on Janus AsTeCl monolayer. A more than 10% STH efficiency of these monolayers indicate their potential practical applications in the commercial production of hydrogen. Thus, our study demonstrates that these monolayers can show potential applications in energy conversion fields., Comment: 31 pages, 10 figures, Journal of Materials Chemistry A, 2023

Earthquake prediction has been a challenging research area for many decades, where the future occurrence of this highly uncertain calamity is predicted. In this paper, several parametric and non-parametric features were calculated, where the non-parametric features were calculated using the parametric features. $8$ seismic features were calculated using Gutenberg-Richter law, the total recurrence, and the seismic energy release. Additionally, criterions such as Maximum Relevance and Maximum Redundancy were applied to choose the pertinent features. These features along with others were used as input for an Extreme Learning Machine (ELM) Regression Model. Magnitude and time data of $5$ decades from the Assam-Guwahati region were used to create this model for magnitude prediction. The Testing Accuracy and Testing Speed were computed taking the Root Mean Squared Error (RMSE) as the parameter for evaluating the mode. As confirmed by the results, ELM shows better scalability with much faster training and testing speed (up to a thousand times faster) than traditional Support Vector Machines. The testing RMSE came out to be around $0.097$. To further test the model's robustness -- magnitude-time data from California was used to calculate the seismic indicators which were then fed into an ELM and then tested on the Assam-Guwahati region. The model proves to be robust and can be implemented in early warning systems as it continues to be a major part of Disaster Response and management., Comment: 11 pages, 13 figures, 2 tables

The convolutional layers of standard convolutional neural networks (CNNs) are equivariant to translation. However, the convolution and fully-connected layers are not equivariant or invariant to other affine geometric transformations. Recently, a new class of CNNs is proposed in which the conventional layers of CNNs are replaced with equivariant convolution, pooling, and batch-normalization layers. The final classification layer in equivariant neural networks is invariant to different affine geometric transformations such as rotation, reflection and translation, and the scalar value is obtained by either eliminating the spatial dimensions of filter responses using convolution and down-sampling throughout the network or average is taken over the filter responses. In this work, we propose to integrate the orthogonal moments which gives the high-order statistics of the function as an effective means for encoding global invariance with respect to rotation, reflection and translation in fully-connected layers. As a result, the intermediate layers of the network become equivariant while the classification layer becomes invariant. The most widely used Zernike, pseudo-Zernike and orthogonal Fourier-Mellin moments are considered for this purpose. The effectiveness of the proposed work is evaluated by integrating the invariant transition and fully-connected layer in the architecture of group-equivariant CNNs (G-CNNs) on rotated MNIST and CIFAR10 datasets., Comment: International Joint Conference on Neural Networks (IJCNN), 2022

The Arcanum mission is a proposed L-class spacecraft that highlights the revolutionary approach which can now be taken to future space mission design. Using the case of the SpaceX Starship vehicle and in particular the high mass and volume characteristics of this launcher, the feasible large size of future missions, even with high delta-V transfer requirements, are analysed. A demonstrator vehicle, designed to support a large and capable science platform with multiple components, is detailed, clearly showing the range and depth of science goals that will be answerable thanks to the current revolution in super heavy-lift launch vehicles., Comment: First presented at the 2021 Reinventing Space Conference held in London, 28-30 June 2021 and later published in the Journal of the British Interplanetary Society. 14 pages, 5 figures

A coming resurgence of super heavy-lift launch vehicles has precipitated an immense interest in the future of crewed spaceflight and even future colonisation efforts. While it is true that a bright future awaits this sector, driven by commercial ventures and the reignited interest of old space-faring nations, and the joining of new ones, little of this attention has been reserved for the science-centric applications of these launchers. The Arcanum mission is a proposal to use these vehicles to deliver an L-class observatory into a highly eccentric orbit around Neptune, with a wide-ranging suite of science goals and instrumentation tackling Solar System science, planetary science, Kuiper Belt Objects and exoplanet systems., Comment: Global Space Exploration Conference 2021, St. Petersburg, Russian Federation, 14 - 18 June 2021. Published by the International Astronautical Federation with permission

Recently, neural networks have been successfully employed to improve upon state-of-the-art performance in ad-hoc retrieval tasks via machine-learned ranking functions. While neural retrieval models grow in complexity and impact, little is understood about their correspondence with well-studied IR principles. Recent work on interpretability in machine learning has provided tools and techniques to understand neural models in general, yet there has been little progress towards explaining ranking models. We investigate whether one can explain the behavior of neural ranking models in terms of their congruence with well understood principles of document ranking by using established theories from axiomatic IR. Axiomatic analysis of information retrieval models has formalized a set of constraints on ranking decisions that reasonable retrieval models should fulfill. We operationalize this axiomatic thinking to reproduce rankings based on combinations of elementary constraints. This allows us to investigate to what extent the ranking decisions of neural rankers can be explained in terms of retrieval axioms, and which axioms apply in which situations. Our experimental study considers a comprehensive set of axioms over several representative neural rankers. While the existing axioms can already explain the particularly confident ranking decisions rather well, future work should extend the axiom set to also cover the other still "unexplainable" neural IR rank decisions., Comment: 10 pages, 2 figures. Published in the proceedings of ICTIR 2021

Probing complex language models has recently revealed several insights into linguistic and semantic patterns found in the learned representations. In this article, we probe BERT specifically to understand and measure the relational knowledge it captures in its parametric memory. While probing for linguistic understanding is commonly applied to all layers of BERT as well as fine-tuned models, this has not been done for factual knowledge. We utilize existing knowledge base completion tasks (LAMA) to probe every layer of pre-trained as well as fine-tuned BERT models(ranking, question answering, NER). Our findings show that knowledge is not just contained in BERT's final layers. Intermediate layers contribute a significant amount (17-60%) to the total knowledge found. Probing intermediate layers also reveals how different types of knowledge emerge at varying rates. When BERT is fine-tuned, relational knowledge is forgotten. The extent of forgetting is impacted by the fine-tuning objective and the training data. We found that ranking models forget the least and retain more knowledge in their final layer compared to masked language modeling and question-answering. However, masked language modeling performed the best at acquiring new knowledge from the training data. When it comes to learning facts, we found that capacity and fact density are key factors. We hope this initial work will spur further research into understanding the parametric memory of language models and the effect of training objectives on factual knowledge. The code to repeat the experiments is publicly available on GitHub., Comment: Extended journal version of arXiv:2010.09313. To be published

Sedimentation in active fluids has come into focus due to the ubiquity of swimming micro-organisms in natural and industrial processes. Here, we investigate sedimentation dynamics of passive particles in a fluid as a function of bacteria E. coli concentration. Results show that the presence of swimming bacteria significantly reduces the speed of the sedimentation front even in the dilute regime, in which the sedimentation speed is expected to be independent of particle concentration. Furthermore, bacteria increase the dispersion of the passive particles, which determines the width of the sedimentation front. For short times, particle sedimentation speed has a linear dependence on bacterial concentration. Mean square displacement data shows, however, that bacterial activity decays over long experimental (sedimentation) times. An advection-diffusion equation coupled to bacteria population dynamics seems to capture concentration profiles relatively well. A single parameter, the ratio of single particle speed to the bacteria flow speed can be used to predict front sedimentation speed., Comment: Soft Matter, (2021). arXiv admin note: substantial text overlap with arXiv:1710.04068

Complex machine learning models are deployed in several critical domains including healthcare and autonomous vehicles nowadays, albeit as functional black boxes. Consequently, there has been a recent surge in interpreting decisions of such complex models in order to explain their actions to humans. Models that correspond to human interpretation of a task are more desirable in certain contexts and can help attribute liability, build trust, expose biases and in turn build better models. It is, therefore, crucial to understand how and which models conform to human understanding of tasks. In this paper, we present a large-scale crowdsourcing study that reveals and quantifies the dissonance between human and machine understanding, through the lens of an image classification task. In particular, we seek to answer the following questions: Which (well-performing) complex ML models are closer to humans in their use of features to make accurate predictions? How does task difficulty affect the feature selection capability of machines in comparison to humans? Are humans consistently better at selecting features that make image recognition more accurate? Our findings have important implications on human-machine collaboration, considering that a long term goal in the field of artificial intelligence is to make machines capable of learning and reasoning like humans., Comment: 23 pages, 5 figures