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Studies conducted in Bangladesh so far did not unequivocally identify the modus operandi of local farmers to perceive and experience the climate variability at a national scale. Hence, this study aims to decipher local farmer’s perception on climate variability for the last 10 years, by constructing climate variability index (CVI). Additionally, this study demystified the socio-economic determinants for influencing farmer perception regarding climate variability as well as its impact on their livelihoods. The study was designed on a cross-sectional data through a country-wide primary survey of 16,053 households who were largely dependent on agriculture. A weighted index was constructed for mapping the regional climate variability using model-builder programming in ArcGIS. Also, a multivariable probit model was employed to identify the factors influencing farmers' perception and resulting impact of climate variability on their livelihoods. According to local farmer’s perception, the CVI mapping identified that Bangladesh experienced variegated climatic variability since last 10 years. However, local farmer’s perception varied with different socio-economic factors like gender, education, farmer’s category, credit, monthly income and access to media. Moreover, landless, small and medium farm holders were more aware of the local climate variability and eventually, they also experienced the higher influence of climate variability on their livelihoods. Since an effective mapping of regional climate variability is a sine qua non to devise region specific policies, this study will facilitate the government to determine its priorities, formulate efficacious strategies and thereby help to adapt with future climate-induced risks and vulnerabilities.

Statistical models provide a powerful and useful class of approximations for calculating reaction rates by bypassing the need for detailed, and often difficult, dynamical considerations. Such approaches invariably invoke specific assumptions about the extent of intramolecular vibrational energy flow in the system. However, the nature of the transition to the statistical regime as a function of the molecular parameters is far from being completely understood. Here, we use tools from nonlinear dynamics to study the transition to statisticality in a model unimolecular reaction by explicitly visulaizing the high dimensional classical phase space. We identify generic features in the phase space involving the intersection of two or more independent anharmonic resonances and show that the presence of correlated, but chaotic, intramolecular dynamics near such junctions leads to nonstatisticality. Interestingly, akin to the stability of asteroids in the Solar System, molecules can stay protected from dissociation at the junctions for several picoseconds due to the phenomenon of stable chaos., Comment: 12 pages, 6 figures (reduced size); Supplementary material can be accessed from the Journal site

Artificial Intelligence has enhanced the capability of many organizations’ decision-making at an extreme level. The demand for analyzing the structured and unstructured data that increases exponentially in recent days is really challenging task. To address this issue, we have developed MLAI software tool which could automate the process of typical data science problems. MLAI combines a number of efficient approaches to enhance the accuracy of several complex problems on predictive analysis for both structured and unstructured data. It creates a seamless interface for the user to interpret the data and also provides custom parameters for tuning the hyperparameters to address the specific business needs. The objective of the paper is to automate the classification problems through a software tool. It can intelligently analyze the whole dataset properly and also determines the features’ importances and also reduces the dimensions of the dataset. It incorporates the advanced visualization techniques using Plotly Express graphs. MLAI presents a development environment that automate various supervised machine learning techniques such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) on several datasets. The comparative analysis of results among aforesaid algorithms are presented and also observed that RF outperforms than rest of the classification algorithms.

We present the quantum and classical study of a four-well trapped Bose-Einstein condensate (BEC) modeled by the Bose-Hubbard Hamiltonian. The model used is fashioned as a minimal bipartite system consisting of a trimer coupled weakly to a monomer. Semiclassical insights into the fragmentation dynamics of the condensate is obtained by mapping out the Arnold web of the high dimensional classical phase space. A remarkable one-to-one correspondence between the quantum state space in occupation number representation and the classical Arnold web in action space is observed. The relevance of dynamical tunneling for the dynamics of Fock states near resonance junctions on the Arnold web is highlighted. We also predict the possibility of manipulating the transport within the trimer subsystem due to the weak coupling to the monomer., Comment: 14 pages 8 figures; Modified as per referee suggestions; Accepted in Physica D special issue on Chaos indicators

In this work we study the competition and correspondence between the classical and quantum routes to intramolecular vibrational energy redistribution (IVR) in a three degrees of freedom model effective Hamiltonian. Specifically, we focus on the classical and the quantum dynamics near the resonance junctions on the Arnold web that are formed by intersection of independent resonances. The regime of interest models the IVR dynamics from highly excited initial states near dissociation thresholds of molecular systems wherein both classical and purely quantum, involving dynamical tunneling, routes to IVR coexist. In the vicinity of a resonance junction classical chaos is inevitably present and hence one expects the quantum IVR pathways to have a strong classical component as well. We show that with increasing resonant coupling strengths the classical component of IVR leads to a transition from coherent dynamical tunneling to incoherent dynamical tunneling. Furthermore, we establish that the quantum IVR dynamics can be predicted based on the structures on the classical Arnold web. In addition, we investigate the nature of the highly excited eigenstates in order to identify the quantum signatures of the multiplicity-2 junctions. For the parameter regimes studies herein, by projecting the eigenstates onto the Arnold web, we find that eigenstates in the vicinity of the junctions are primarily delocalized due to dynamical tunneling., Comment: 17 pages, 9 figures (reduced size), Accepted in J. Phys. Chem. A (2018) for William P. Reinhardt Festschrift

Intramolecular vibrational energy redistribution (IVR) impacts the dynamics of reactions in a profound way. Theoretical and experimental studies are increasingly indicating that accounting for the finite rate of energy flow is critical for uncovering the correct reaction mechanisms and calculating accurate rates. This requires an explicit understanding of the influence and interplay of the various anharmonic (Fermi) resonances that lead to the coupling of the vibrational modes. In this regard, the local random matrix theory (LRMT) and the related Bose-statistics triangle rule (BSTR) model have emerged as a powerful and predictive quantum theories for IVR. In this Perspective we highlight the close correspondence between LRMT and the classical phase space perspective on IVR, primarily using model Hamiltonians with three degrees of freedom. Our purpose for this is threefold. First, this clearly brings out the extent to which IVR pathways are essentially classical, and hence crucial towards attempts to control IVR. Second, given that LRMT and BSTR are designed to be applicable for large molecules, the exquisite correspondence observed even for small molecules allows for insights into the quantum ergodicity transition. Third, we showcase the power of modern nonlinear dynamics methods in analysing high dimensional phase spaces, thereby extending the deep insights into IVR that were earlier gained for systems with effectively two degrees of freedom. We begin with a brief overview of recent examples where IVR plays an important role and conclude by mentioning the outstanding problems and the potential connections to issues of interest in other fields., Comment: Accepted Phys. Chem. Chem. Phys Perspective article, 36 pages, 15 figures (reduced size)

Robotic grippers have been designed for grasping a wide variety of objects. This paper presents a novel design of a flexible gripper suitable for gripping circular objects having variable curvatures and different textures. Two rubber belts form the gripper arms, which are used for gripping objects. These rubber belts are attached to DC motors. The motors are fitted in the gripper base. Magnets are attached on the other ends along with an interlocking mechanism. The primary actuation in the gripper is brought about by the Shape Memory Alloy (SMA) wire fitted along the inner side of the rubber belts. On energizing the SMA, the rubber belts bend along with the pre-programmed SMA and the magnets on the two belts come closer. Subsequently, locking is achieved. This forms a loop around the gripping object. The grip of the rubber belts around the object is further tightened by winding them around the shafts of the motors to which they are attached. This helps the gripper to firmly grasp objects of variable diameters. Gripping has been successfully tested on pipes, metal poles, trees with thin and thick stems.

Polyphenol contributes significantly to the quality of tea. Non-invasive analysis of polyphenol in tea leaves is thus of prime importance. In this work, near infrared reflectance (NIR) and partial least squares (PLS) has been used to determine the total polyphenol content in tea leaves. During sample acquisition the number of variable is quite high for each spectra and whole range of spectra may not play an important role for building the calibration model of PLS algorithm. Thus, to determine the spectral region, Grey wolf optimizer (GWO) was used. Partial least squares (PLS) algorithm was used to generate the fitness function for GWO to estimate the total polyphenol content using the spectral region, found by optimization technique. During model calibration, for training and testing, leave-one-sample out cross-validation (LOSOCV) was used. The optimum range was obtained to be from 1043.5 nm to 1166 nm. The adequacy of the model developed was evaluated by root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (R). The RMSECV value found using GWO was 0.1364. The RMSEP and correlation coefficients (R) in the prediction set is 0.3244 and 0.91, respectively.

Multilevel inverters are popular for their ability to enhance voltage level, reduce harmonic distortion and eliminate the need of output filters. They are mainly comprised of different combinations of dc voltage sources and power electronic switches and the number of voltage levels are dependent on the switching scheme. This paper proposes a new topology of single phase multilevel inverter. The new topology uses a combination of series connection of isolated dc sources and the developed H-bridge. This paper gives a detailed analysis of the proposed topology using both symmetrical and asymmetrical dc sources. Furthermore, the proposed topology has been compared with the conventional cascaded multilevel inverter and some new topologies published in recent literatures. Finally, the operation of the proposed topology has been verified with computer simulation using MATLAB/Simulink.