Abstract

Spectroscopy is the study of how matter interacts with electromagnetic radiation. The spectra of any molecule are highly information-rich, yet the inverse relation of spectra to the corresponding molecular structure is still an unsolved problem. Nuclear magnetic resonance (NMR) spectroscopy is one such critical technique in the scientists’ toolkit to characterize molecules. In this work, a novel machine learning framework is proposed that attempts to solve this inverse problem by navigating the chemical space to find the correct structure given an NMR spectra. The proposed framework uses a combination of online Monte Carlo tree search (MCTS) and a set of graph convolution networks to build a molecule iteratively. Our method can predict the structure of the molecule ∼80% of the time in its top 3 guesses for molecules with <10 heavy atoms. We believe that the proposed framework is a significant step in solving the

Abstract

Ketones are the key functional group that recurs in chemistry and biology, and accessing them through simple and economic ways is highly desirable. Herein, we report the synthesis of unsymmetrical ketones from abundant toluene and alkyl esters, where volatile alcohols are the sole byproduct. This protocol applies to a repertoire of substrates bearing electron‐donating, electron‐withdrawing, and neutral substituents. Most importantly, the organometallic ferrocenyl ester underwent aroylation with ease. This method is the first example to furnish diketones from methyl arenes and diesters. Furthermore, cyclic imide was synthesized by this protocol utilizing KN(SiMe3)2 as a ′nitrogen′ source. Density functional theory studies provide insight into deprotonation of toluene by K+‐π interaction by increasing its acidity, and this being the rate‐determining step.

Abstract

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding afnity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding afnities better than the state-of-the-art scoring functions are important. For the frst time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding afnities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding afnities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding afnities

Abstract

A unique B–N coordinated phenanthroimidazole-based zinc salen was synthesized. The zinc salen thus synthesized acts as a photocatalyst for the cycloaddition of carbon dioxide with terminal epoxides under ambient conditions. DFT study of the cycloaddition of carbon dioxide with terminal epoxide indicates the preference of the reaction pathway when photocatalyzed by zinc salen. We anticipate that this strategy will help to design new photocatalysts for CO2 fixation.

Abstract

Nucleocytoplasmic shuttling of viral elements, supported by several host factors, is essential for the replication of the human immunodeficiency virus (HIV). HIV‐1 uses a nuclear RNA export pathway mediated by viral protein Rev to transport its Rev response element (RRE)‐containing partially spliced and unspliced transcripts aided by the host nuclear RNA export protein CRM1. The factor(s) interacting with the CRM1‐Rev complex are potential antiretroviral target(s) and could serve as a retroviral model system to study nuclear export machinery adapted by these viruses. We earlier reported that cellular Staufen‐2 interacts with Rev, facilitating viral‐RNA export. Here, we identified the formation of a complex between Staufen‐2, CRM1 and Rev. Molecular docking and simulations mapped the interacting residues in the RNA‐binding Domain 4 of Staufen‐2 as R336 and R337, which were experimentally verified to be …

Abstract

Among primates, the prehensile nature of the hand is vital for greater adaptability and a secure grip over the substrate/branches, particularly for arm-swinging motion or brachiation. Though various brachiation mechanisms that are mechanically equivalent to underactuated pendulum models are reported in the literature, not much attention has been given to the hand design that facilitates both locomotion and within-hand manipulation. In this paper, we propose a new robotic gripper design, equipped with shape conformable active gripping surfaces that can act as an active or passive joint and adapt to substrates with different shapes and sizes. A floating base serial chain, named GraspMaM, equipped with two such grippers, increases the versatility by performing a range of locomotion and manipulation modes without using dedicated systems. The unique gripper design allows the robot to estimate the passive joint state while arm-swinging and exhibits a dual relationship between manipulation and locomotion. We report the design details of the multimodal gripper and how it can be adapted for the brachiation motion assuming it as an articulated suspended pendulum model. Further, the system parameters of the physical prototype are estimated, and experimental results for the brachiation mode are discussed to validate and show the effectiveness of the proposed design.

Abstract

Underwater gliders are common robotic platforms that usually have at least three degrees of freedom (DoF), namely surge, heave, and pitch. The heave motion generated using variable buoyancy can be vectored along surge direction using wings. These gliders are usually designed to be operated for specific applications and the mission dictates the diving requirements, usually quantified using factors such as dive-cycle, dive-in-depth, and range. Several parameters influence diving performance, of which, dimension and position of the wing play a significant role. The objective of this work is to study the effect of wing parameters such as the position of the wing (along the hull) and its dimensions, on the diving performance of the glider and thereby determine their optimal values for a given mission requirement. The wing can be accordingly designed and attached to the hull of the glider. The design and dynamics of RoBuoy, a novel underwater glider, is used for this study. Conveniently RoBuoy’s design allows modular wing positioning and dimensions, and hence the results of the study can be tested on this platform. A detailed study of the influence of the chosen wing parameters on the dynamics of the glider along with a Genetic Algorithm (GA) based approach to optimizing the parameters is presented in this paper.

Abstract

In this paper, the Multi-Swarm Cooperative Information-driven search and Divide and Conquer mitigation control (MSCIDC) approach is proposed for faster detection and mitigation of forest fire by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of Unmanned Aerial Vehicles (UAVs) that fly together to search and quench the fire effectively. The multiswarm cooperative information-driven search uses a multi-level search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire location. The search level is selected based on the thermal sensor information about the potential fire area. The dynamicity of swarms, aided by global regulative repulsion and merging between swarms, reduces the detection and mitigation time compared to the existing methods. The local attraction among the members of the swarm helps the non-detector members to reach the fire location faster, and divide-and-conquer mitigation control ensures a non-overlapping fire sector allocation for all members quenching the fire. The performance of MSCIDC has been compared with different multi-UAV methods using a simulated environment of pine forest. The performance clearly shows that MSCIDC mitigates fire much faster than the multi-UAV methods. The Monte-Carlo simulation results indicate that the proposed method reduces the average forest area burnt by 65% and mission time by 60% compared to the best result case of the multi-UAV approaches, guaranteeing a faster and successful mission. Index Terms—Swarm search, forest firefighting, cooperative information-driven search, divide and conquer mitigation control

Abstract

This paper presents detailed mathematical modeling, controller design, and flight test results for the autonomous mission of a nonconventional fixed-wing biplane micro air vehicle (MAV) called Skylark having span and chord length within 150 mm. Although numerous fixed-wing MAV designs are reported in the open literature, an MAV’s reliable autonomous flight is still a challenge. The primary difficulties in performing the autonomous mission of fixed-wing MAV are system integration within the weight and power budget, center of gravity (CG) management, and flight controller design for fast dynamics. In this paper, the key challenges are addressed by using the higher payload capacity of Skylark, suitable selection and design of avionics components, and design of controller after detailed development of the mathematical model, including the additional effect of propeller wash and motor countertorque. An …

Abstract

In this paper, we solve an optimal consensus control problem of maximizing the state-dependent communi- cation connectivity during a multi-agent consensus dynamics. A proportional-derivative type consensus controller is leveraged to drive agents into a symmetric formation. The asymptotic stability of the closed-loop system dynamics is established using Lyapunov theory, which helps us to deduce an intuitive time-varying gain profile based on a sufficient condition for convergence. Further, a Model Predictive Control approach is adopted to minimize a quadratic cost over a finite prediction horizon by adjusting the controller gains, such that the optimal connectivity is attained on the way with less control efforts, while handling constraints to agents’ states, inputs, turn-rates and disturbances injected into agent velocities. Simulation results with time-varying controller gains demonstrate the impact of our proposed technique