Double Time Window Targeting Technique: Real time DMRG dynamics in the PPP model

We present a generalized adaptive time-dependent density matrix renormalization group (DMRG) scheme, called the {\it double time window targeting} (DTWT) technique, which gives accurate results with nominal computational resources, within reasonable computational time. This procedure originates from the amalgamation of the features of pace keeping DMRG algorithm, first proposed by Luo {\it et. al}, [Phys.Rev. Lett. {\bf 91}, 049701 (2003)], and the time-step targeting (TST) algorithm by Feiguin and White [Phys. Rev. B {\bf 72}, 020404 (2005)]. Using the DTWT technique, we study the phenomena of spin-charge separation in conjugated polymers (materials for molecular electronics and spintronics), which have long-range electron-electron interactions and belong to the class of strongly correlated low-dimensional many-body systems. The issue of real time dynamics within the Pariser-Parr-Pople (PPP) model which includes long-range electron correlations has not been addressed in the literature so far. The present study on PPP chains has revealed that, (i) long-range electron correlations enable both the charge and spin degree of freedom of the electron, to propagate faster in the PPP model compared to Hubbard model, (ii) for standard parameters of the PPP model as applied to conjugated polymers, the charge velocity is almost twice that of the spin velocity and, (iii) the simplistic interpretation of long-range correlations by merely renormalizing the {\it U} value of the Hubbard model fails to explain the dynamics of doped holes/electrons in the PPP model.Comment: Final (published) version; 39 pages, 13 figures, 1 table; 2 new references adde

Infrastructure for mobile sensor network in the Singapore coastal zone

URL to conference page. Scroll down to 2010 conference, click on "Paper and session list," and search the PDF for Patrikalakis.Singapore is an island nation located at southern tip of the Malaysian Peninsula. She is at a strategic location along major shipping routes and therefore has one of the busiest harbors in the world. Having a safe and secure harbor environment is vital to maintain trade and growth in the country and region. To help build and maintain a safe harbor environment, the Center of Environmental Sensing and Modelling (CENSAM) under the Singapore-MIT Alliance for Research and Technology (SMART) is developing a mobile sensor network in the Singapore coastal zone

Isostructural phase transition in Tb2Ti2O7 under pressure and temperature: Insights from synchrotron X-ray diffraction

Tb2Ti2O7, a pyrochlore system, has garnered significant interest due to its intriguing structural and physical properties and their dependence on external physical parameters. In this study, utilizing high-brilliance synchrotron X-ray diffraction, we conducted a comprehensive investigation of structural evolution of Tb2Ti2O7 under external pressure and temperature. We have conclusively confirmed the occurrence of an isostructural phase transition beyond the pressure of 10 GPa. The transition exhibits a distinct signature in the variation of lattice parameters under pressure and leads to changes in mechanical properties. The underlying physics driving this transition can be understood in terms of localized rearrangement of atoms while retaining the overall cubic symmetry of the crystal. Notably, the observed transition remains almost independent of temperature. Our findings provide insights into the distinctive behaviour of the isostructural phase transition in Tb2Ti2O7

Natural Hazards Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

This article is about the state of ICON principles Goldman et al. (2021), https://doi. org/10.1029/2021EO153180 in natural hazards and a discussion on the opportunities and challenges of adopting them. Natural hazards pose risks to society, infrastructure, and the environment. Hazard interactions and their cascading phenomena in space and time can further intensify the impacts. Natural hazards’ risks are expected to increase in the future due to environmental, demographic, and socioeconomic changes. It is important to quantify and effectively communicate risks to inform the design and implementation of risk mitigation and adaptation strategies. Multihazard multisector risk management poses several nontrivial challenges, including: (a) integrated risk assessment, (b) Earth system data-model fusion, (c) uncertainty quantification and communication, and (d) crossing traditional disciplinary boundaries. Here, we review these challenges, highlight current research and operational endeavors, and underscore diverse research opportunities. We emphasize the need for integrated approaches, coordinated processes, open science, and networked efforts (ICON) for multihazard multisector risk management

GWAS identifies genetic loci underlying nitrogen responsiveness in the climate resilient C4 model Setaria italica (L.)

Introduction: N responsiveness is the capacity to perceive and induce morpho-physiological adaptation to external and internal Nitrogen (N). Crop productivity is propelled by N fertilizer and requires the breeding/selection of cultivars with intrinsically high N responsiveness. This trait has many advantages in being more meaningful in commercial/environmental context, facilitating in-season N management and not being inversely correlated with N availability over processes regulating NUE. Current lack of its understanding at the physio-genetic basis is an impediment to select for cultivars with a predictably high N response. Objectives: To dissect physio-genetic basis of N responsiveness in 142 diverse population of foxtail millet, Setaria italica (L.) by employing contrasting N fertilizer nutrition regimes. Methods: We phenotyped S. italica accessions for major yield related traits under low (N10, N25) and optimal (N100) growth conditions and genotyped them to subsequently perform a genome-wide association study to identify genetic loci associated with nitrogen responsiveness trait. Groups of accessions showing contrasting trait performance and allelic forms of specific linked genetic loci (showing haplotypes) were further accessed for N dependent transcript abundances of their proximal genes. Results: Our study show that N dependent yield rise in S. italica is driven by grain number whose responsiveness to N availability is genetically underlined. We identify 22 unique SNP loci strongly associated with this trait out of which six exhibit haplotypes and consistent allelic variation between lines with contrasting N dependent grain number response and panicle architectures. Furthermore, differential transcript abundances of specific genes proximally linked to these SNPs in same lines is indicative of their N dependence in a genotype specific manner. Conclusion: The study demonstrates the value/ potential of N responsiveness as a selection trait and identifies key genetic components underlying the trait in S. italica. This has major implications for improving crop N sustainability and food security

Major depressive disorder and schizophrenia are associated with a disturbed experience of temporal memory

Background Disturbances in ‘psychological time’ are frequently reported in major depressive disorder (MDD) and schizophrenia. If one accepts the suggestion that the experience of the dimensions of time, past-present-future, are not inseparable then a disturbance in episodic memory is implicated. Episodic memory allows us to make sense of the world and our place within it by constructing a temporal context and temporal flow between events. These temporal representations are disordered in schizophrenia, but whether this is reflected in MDD is not known. Temporal-order memory deficits can be explained by two hypotheses. The prefrontal-organisational hypothesis suggests that deficits result from a breakdown in processes involved in encoding, retrieval, monitoring and decision-making. Whereas the hippocampal-mnemonic theory suggests that item-encoding, and inter-item associative encoding contribute to temporal-order memory. Methods New learning, recency judgments and executive function were investigated in 14 MDD patients, 15 schizophrenia patients and 10 healthy volunteers (HVs). Results Relative to HVs, both MDD and schizophrenia made more temporal errors despite achieving 100% learning. Deficits in executive function and item-recognition were present in both psychiatric groups, but executive function correlated to temporal errors in MDD only, and item-recognition to new learning in schizophrenia only. Limitations MDD and schizophrenia patients were taking medication Conclusions Temporal-ordering deficits are evident in both MDD and schizophrenia, and whilst the disruption of organisational and mnemonic processes appears to be ubiquitous, preliminary evidence from the correlational analysis suggests prefrontal problems are implicated in MDD temporal-order deficits, whereas hippocampal are more associated to temporal-order memory deficits in schizophrenia

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation

Cosmic-ray soil water monitoring: the development, status & potential of the COSMOS-India network

Soil moisture (SM) plays a central role in the hydrological cycle and surface energy balance and represents an important control on a range of land surface processes. Knowledge of the spatial and temporal dynamics of SM is important for applications ranging from numerical weather and climate predictions, the calibration and validation of remotely sensed data products, as well as water resources, flood and drought forecasting, agronomy and predictions of greenhouse gas fluxes. Since 2015, the Centre for Ecology and Ecology has been working in partnership with several Indian Research Institutes to develop COSMOS-India, a new network of SM monitoring stations that employ cosmic-ray soil moisture sensors (CRS) to deliver high temporal frequency, near-real time observations of SM at field scale. CRS provide continuous observations of near-surface (top 0.1 to 0.2 m) soil volumetric water content (VWC; m3 m-3) that are representative of a large footprint area (approximately 200 m in radius). To date, seven COSMOS-India sites have been installed and are operational at a range of locations that are characterised by differences in climate, soil type and land management. In this presentation, the development, current status and future potential of the COSMOS-India network will be discussed. Key results from the COSMOS-India network will be presented and analysed