Accurate prediction of rainfall is a complex problem because of the large number of controlling factors, complex interrelationships between them and the multiscaling behaviour of the process. Because ...of the multiscaling behaviour of the problem, the use of hybrid modelling involving decomposition techniques is preferable over strenuous physical models and standalone driven techniques for accurate rainfall predictions. This study proposes a novel hybrid modelling framework integrating Long Short Term Memory (LSTM) and Multivariate Empirical Mode Decomposition (MEMD) aided with Time Dependent Intrinsic Cross-Correlation (TDICC) analysis algorithm for monthly rainfall predictions. The application of the proposed model is demonstrated for monthly rainfall prediction of 2005–2015 period at All India spatial domain considering El-Niño Southern Oscillation(ENSO), Indian Ocean Dipole(IOD) and five antecedent values of rainfall are the input variables. The proposed framework first uses MEMD to obtain a set of orthogonal components namely Intrinsic Mode Functions (IMFs) identifying and aligning the common scales embedded in the multiple input variables considered. Subsequently, scale specific rainfall information are predicted by incorporating them on relevant IMFs and their significant lags identified through TDIC and TDICC analyses. Final aggregation of predicted rainfall components from different scales gives the monthly rainfall of a generic time step ahead. The efficacy of the proposed MEMD-TDICC-LSTM framework is compared with five other hybrid models such as MEMD-TDIC-ANN, MEMD-TDICC-ANN, MEMD-ACO-ANN, MEMD-ACO-LSTM and MEMD-TDIC-LSTM, which used Time Dependent Intrinsic Correlation (TDIC) and Ant Colony Optimization (ACO) for predictor selection and Artificial Neural Network (ANN) as modelling tool. The study has used different graphical representations and ten different statistical performance evaluation measures for the prediction of validation period, it is observed that the proposed model could achieve a predictive skill of 0.98, Nash-SutcliffeEfficiency (NSE) of 0.95 and Index of Agreement (IA) of 0.91 which is better than all the five remaining models. The capability of MEMD-TDICC-LSTM model to predict the extremes is also confirmed by using the bar graph for the drought year rainfall of 2009 and is observed that the model is successful in capturing the extremes with an annual rainfall 975.16 mm closer to observed value 927.3 mm. MEMD algorithm facilitates the inclusion of multiple large scale climatic oscillations as inputs and their multi time scale decomposition, TDICC helps to fix the relevant inputs at different time scales and the LSTM functions as the robust modelling tool. Further, the framework using this specific combination resulted in substantial reduction in modelling complexity and faster execution, as the approach considers only the most relevant and significant inputs in the process.
•Novel MEMD-TDICC-LSTM framework is proposed for prediction of monthly rainfall over India.•MEMD performs robust decomposition of multiple climatic inputs and LSTM performs efficient prediction.•Presented the novel TDICC based approach for hydro-climatic teleconnection and feature selection (FS).•The proposed framework is superior to ANN hybrids and ACO-LSTM hybrids in the prediction of mean and extreme rainfall.
Extreme temperature events are one of the most serious threats resulting from climatic change across the globe. Quantifying the intensity, duration, and frequency of temperature extremes is of huge ...societal and scientific interest. Recent evidence in climate change challenges the stationarity assumption conventionally followed while performing temperature-duration-frequency (TDF) analysis. India has distinct climate zones and topography, distributing climate threats unevenly. In this study, stationary (S) and non-stationary (NS) TDF analysis for India and its seven temperature homogenous areas is performed using a gridded (1° × 1°) daily maximum temperature dataset from 1951 to 2019. Time is employed as a covariate to incorporate linear, quadratic, and exponential trends in the location and/or scale parameters of the generalized extreme value distribution to demonstrate the impact of non-stationarity in developing TDF curves. According to the findings, NS TDF models provide a better fit to the dataset when compared to the S TDF model. More than 55% of the grid points have NS Model-1, viz., location parameter linearly varying with time, as the best-fit model. In contrast to their stationary counterparts, NS temperature return levels were consistently higher across all return periods. Furthermore, temperature homogenous zones in the North-West, North Central, and Interior Peninsula are more susceptible to temperature rises beyond 45°C. While envisioning long-term solutions in a changing climate scenario, considering non-stationarity significantly improved the accuracy of TDF curves. This will indeed support more robust predictions, which will ultimately aid in the mitigation of future extreme temperature events.
When the ground state of a localized ion is a non-Kramers doublet, such localized ions may carry multipolar moments. For example,Pr3+ions in a cubic environment would possess quadrupolar and ...octupolar, but no magnetic dipole, moments. When such multipolar moments are placed in a metallic host, unusual interactions between these local moments and conduction electrons arise, in contrast to the familiar magnetic dipole interactions in the classic Kondo problem. In this work, we consider the interaction between a single quadrupolar-octupolar local moment and conduction electrons withp-orbital symmetry as a concrete model for the multipolar Kondo problem. We show that this model can be written most naturally in the spin-orbital entangled basis of conduction electrons. Using this basis, the perturbative renormalization-group (RG) fixed points are readily identified. There are two kinds of fixed points, one for the two-channel Kondo and the other for a novel fixed point. We investigate the nature of the novel fixed point nonperturbatively using non-Abelian bosonization, current algebra, and conformal field theory approaches. It is shown that the novel fixed point leads to a, previously unidentified, non-Fermi liquid state with entangled spin and orbital degrees of freedom, which shows resistivityρ∼TΔand diverging specific heat coefficientC/T∼T−1+2Δ, withΔ=1/5. Our results open up the possibility of myriads of non-Fermi liquid states, depending on the choices of multipolar moments and conduction electron orbitals, which would be relevant for many rare-earth metallic systems.
Broken symmetries in solids involving higher order multipolar degrees of freedom are historically referred to as "hidden orders" due to the formidable task of detecting them with conventional probes. ...In this work, we theoretically propose that magnetostriction provides a powerful and novel tool to directly detect higher-order multipolar symmetry breaking-such as the elusive octupolar order-by examining scaling behaviour of length change with respect to an applied magnetic field h. Employing a symmetry-based Landau theory, we focus on the family of Pr-based cage compounds with strongly correlated f-electrons, Pr(Ti,V,Ir)
(Al,Zn)
, whose low energy degrees of freedom are purely higher-order multipoles: quadrupoles Formula: see text and octupole Formula: see text. We demonstrate that a magnetic field along the 111 direction induces a distinct linear-in-h length change below the octupolar ordering temperature. The resulting "magnetostriction coefficient" is directly proportional to the octupolar order parameter, thus providing clear access to such subtle order parameters.
ABSTRACT
In recent times, trend analysis and change point detection in hydroclimatic variables receiving significant attention due to climate change and its socioeconomic consequences. In this study, ...long‐term trends of rainfall in four subdivisions of southern India namely Kerala, Tamil Nadu, North Interior (NI) Karnataka and Telangana regions are analysed using linear regression, nonparametric Mann–Kendall (MK) test and Sen's slope estimator methods. Trend analysis of annual rainfall time series shows an increasing trend in three subdivisions – Tamil Nadu, NI Karnataka and Telangana, and a decreasing trend in Kerala subdivision. Further the sequential change in trend of annual and seasonal rainfalls in the four subdivisions is conducted using sequential MK (SQMK) method. The SQMK analysis shows an early divergence of progressive and retrograde modes of post‐monsoon rainfall of Kerala and winter rainfall of Telangana subdivisions. Further it is observed that among different seasonal rainfalls, the post‐monsoon rainfall of Kerala subdivision shows a statistically significant increase in the recent past. Then the trend analysis based on discrete wavelet transform (DWT) in conjunction with SQMK method is performed on the post‐monsoon rainfall time series of Kerala subdivision, and the results show that there is a dominancy of short‐term periodicity of less than a decade in the subdivision.
Abstract This study presents a novel decentralized active control algorithm for vibration mitigation of structures subjected to random seismic excitations. In recent years, the applicability of ...decentralization technique has gained momentum in the field of control theory to overcome the complexity and computational burden of centralized system. Advancement in instrumentation scheme, i.e., sensors, actuators and computing units in the feedback loop is the reason behind the growing interest of decentralized control concept. In this case, the controller framework is designed through suboptimal H 2 -based proportional control algorithm. The objective of this optimal strategy is to reduce structural energy and control force simultaneously. Further, the concept of decentralized switched state-feedback control law is incorporated, and the detailed formulations are presented in digital domain for easy implementation. A set of communication channels are considered to exchange the state information between the neighbouring subcontrollers. The performance of the designed control strategy is numerically illustrated for an 8-storey building model with multiple magneto-rheological dampers. Comparative assessment with passive case and standard centralized scheme confirms better adaptiveness of the proposed control scheme.
We theoretically investigate the mechanism to generate large intrinsic spin Hall effect in iridates or more broadly in 5d transition metal oxides with strong spin-orbit coupling. We demonstrate such ...a possibility by taking the example of orthorhombic perovskite iridate with nonsymmorphic lattice symmetry, SrIrO
, which is a three-dimensional semimetal with nodal line spectrum. It is shown that large intrinsic spin Hall effect arises in this system via the spin-Berry curvature originating from the nearly degenerate electronic spectra surrounding the nodal line. This effect exists even when the nodal line is gently gapped out, due to the persistent nearly degenerate electronic structure. The magnitude of the spin Hall conductivity is shown to be comparable to the best known example such as doped topological insulators and the biggest in any transition metal oxides. To gain further insight, we compute the intrinsic spin Hall conductivity in both bulk and thin film systems. We find that the geometric confinement in thin films leads to significant modifications of the electronic states, leading to even bigger spin Hall conductivity in certain cases. We compare our findings with the recent experimental report on the discovery of large spin Hall effect in SrIrO
thin films.
In this paper, the Hilbert–Huang transform (HHT) approach is used for the multiscale characterization of All India Summer Monsoon Rainfall (AISMR) time series and monsoon rainfall time series from ...five homogeneous regions in India. The study employs the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for multiscale decomposition of monsoon rainfall in India and uses the Normalized Hilbert Transform and Direct Quadrature (NHT-DQ) scheme for the time–frequency characterization. The cross-correlation analysis between orthogonal modes of All India monthly monsoon rainfall time series and that of five climate indices such as Quasi Biennial Oscillation (QBO), El Niño Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multi Decadal Oscillation (AMO), and Equatorial Indian Ocean Oscillation (EQUINOO) in the time domain showed that the links of different climate indices with monsoon rainfall are expressed well only for few low-frequency modes and for the trend component. Furthermore, this paper investigated the hydro-climatic teleconnection of ISMR in multiple time scales using the HHT-based running correlation analysis technique called time-dependent intrinsic correlation (TDIC). The results showed that both the strength and nature of association between different climate indices and ISMR vary with time scale. Stemming from this finding, a methodology employing Multivariate extension of EMD and Stepwise Linear Regression (MEMD-SLR) is proposed for prediction of monsoon rainfall in India. The proposed MEMD-SLR method clearly exhibited superior performance over the IMD operational forecast, M5 Model Tree (MT), and multiple linear regression methods in ISMR predictions and displayed excellent predictive skill during 1989–2012 including the four extreme events that have occurred during this period.
Background
PB-22 (1-pentyl-8-quinolinyl ester-1H-indole-3-carboxylic acid) and 5F-PB-22 (1-(5-fluoropentyl)-8-quinolinyl ester-1H-indole-3-carboxylic acid) are new synthetic cannabinoids with a ...quinoline substructure and the first marketed substances with an ester bond linkage. No human metabolism data are currently available, making it difficult to document PB-22 and 5F-PB-22 intake from urine analysis, and complicating assessment of the drugs’ pharmacodynamic and toxicological properties.
Methods
We incubated 10 μmol/l PB-22 and 5F-PB-22 with pooled cryopreserved human hepatocytes up to 3 h and analyzed samples on a TripleTOF 5600+ high-resolution mass spectrometer. Data were acquired via TOF scan, followed by information-dependent acquisition triggered product ion scans with mass defect filtering (MDF). The accurate mass full scan MS and MS/MS metabolite datasets were analyzed with multiple data processing techniques, including MDF, neutral loss and product ion filtering.
Results
The predominant metabolic pathway for PB-22 and 5F-PB-22 was ester hydrolysis yielding a wide variety of (5-fluoro)pentylindole-3-carboxylic acid metabolites. Twenty metabolites for PB-22 and 22 metabolites for 5F-PB-22 were identified, with the majority generated by oxidation with or without glucuronidation. For 5F-PB-22, oxidative defluorination occurred forming PB-22 metabolites. Both compounds underwent epoxide formation followed by internal hydrolysis and also produced a cysteine conjugate.
Conclusion
Human hepatic metabolic profiles were generated for PB-22 and 5F-PB-22. Pentylindole-3-carboxylic acid, hydroxypentyl-PB-22 and PB-22 pentanoic acid for PB-22, and 5′-fluoropentylindole-3-carboxylic acid, PB-22 pentanoic acid and the hydroxy-5F-PB-22 metabolite with oxidation at the quinoline system for 5F-PB-22 are likely the best targets to incorporate into analytical methods for urine to document PB-22 and 5F-PB-22 intake.
>Figure
Metabolism of synthetic cannabinoids PB-22 and 5F-PB-22 by human hepatocyte incubation and high-resolution mass spectrometry
PDF
