Abstract

In Himalayas, the high-elevational mountains have warmed more rapidly in recent decades than other areas of the globe. Himalayan timberline is climate dependent and sensitive to changes, thus can provide biological proof of global warming. The present analysis changes in timberline elevations over four decades and corresponding climatic parameters (temperature and precipitation) in the Western Himalayan region (Himachal Pradesh). Landsat-2 Multispectral Scanner (MSS) and Landsat-8 were used to evaluate the long-term (1976–2015) timberline dynamics. The climate data APHRODITE was used to calculate the annual mean temperature and annual precipitation along the spatially distinct timberline locations. The mean elevation of timberline position has shifted vertically 145 m with a rate of ~37 m per decade (@ 3.7 m per year) over the past four decades, however majority of timberline remained

Abstract

Systems of differential equations with polynomial right-hand sides are very common in applications. In particular, when restricted to the positive orthant, they appear naturally (according to the law of mass-action kinetics) in ecology, population dynamics, as models of biochemical interaction networks, and models of the spread of infectious diseases. On the other hand, their mathematical analysis is very challenging in general; in particular, it is very difficult to answer questions about the long-term dynamics of the variables (species) in the model, such as questions about persistence and extinction. Even if we restrict our attention to mass-action systems, these questions still remain challenging. On the other hand, if a polynomial dynamical system has a weakly reversible single linkage class (WR1 ) realization, then its long-term dynamics is known to be remarkably robust: all the variables are persistent (i.e., no species goes extinct), irrespective of the values of the parameters in the model. Here we describe an algorithm for finding WR1 realizations of polynomial dynamical systems, whenever such realizations exist.

Abstract

The leading order production of an SM singlet-like scalar has primarily been realized through the gluon fusion process by mixing with the SU(2)_L scalar doublet of the model. The dominant part of the physical state, i.e., the singlet component, does not have any role in its direct production. Focusing on such a state with a mass smaller than the SM-like Higgs scalar, we calculate the dominant next-to-leading (NLO) order corrections to its production cross-section. With these improved cross-sections, the present and future LHC limits may become somewhat more stringent

Abstract

Identification of cancer subtypes based on molecular knowledge is crucial for improving the patient diagnosis, prognosis, and treatment. In this work, we integrated copy number variations (CNVs) and transcriptomic data of Kidney Papillary Renal Cell Carcinoma (KIRP) using a network diffusion strategy to stratify cancers into clinically and biologically relevant subtypes. We constructed GeneNet, a KIRP specific gene expression network from RNA-seq data. The copy number variation data was projected onto GeneNet and propagated on the network for clustering. We identified robust subtypes that are biologically informative and significantly associated with patient survival, tumor stage and clinical subtypes of KIRP. We performed a Singular Value Decomposition (SVD) analysis of KIRP subtypes, which revealed the genes/silent players related to poor survival. A differential gene expression analysis between

Abstract

The liver plays a vital role in maintaining whole-body metabolic homeostasis, compound detoxification and has the unique ability to regenerate itself post-injury. Ageing leads to functional impairment of the liver and predisposes the liver to non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC). Mapping the molecular changes of the liver with ageing may help to understand the crosstalk of ageing with different liver diseases. A systems-level analysis of the ageing-induced liver changes and its crosstalk with liver-associated conditions is lacking. In the present study, we performed network-level analyses of the ageing liver using mouse transcriptomic data and a protein–protein interaction (PPI) network. A sample-wise analysis using network entropy measure was performed, which showed an increasing trend with ageing and helped to identify ageing genes based on local entropy changes. To

Abstract

The thermodynamics of the hydrophobic effect, as measured primarily through the temperature dependence of solubility, is reviewed, and then a class of models that incorporate the basic mechanism of hydrophobicity is described. These models predict a quantitative relation between the free energy of hydrophobic hydration and the strength of the solvent-mediated attraction between pairs of solute molecules. It is remarked that the free energy of attraction being just of the order of the thermal energy kT may be important for the effective operation of the hydrophobic effect in proteins. Deviations from pairwise additivity of hydrophobic forces are also briefly discussed.

Abstract

Fact-to-text generation can allow for the generation of high-quality, informative texts such as Wikipedia articles. Cross-lingual fact-to-text generation (XF2T) involves using facts available in a language, typically English, and generating texts in a different language based on these facts. This is particularly relevant for low and medium-resource languages, which have relatively structured informative content. This work explores the problem of XF2T for generating long text from given facts with a specific focus on generating factually grounded content. Unfortunately, previous work either focuses on cross-lingual facts to short text or monolingual graph to text generation. In this paper, we propose a novel solution to the multi-sentence XF2T task, which addresses these challenges by training multilingual Transformer-based models with coverage prompts and rebalanced beam search, and further improving the quality by defining task-specific reward functions and training on them using reinforcement learning. Keywords: XF2T, text generation, cross-lingual, NLG evaluation, low resource NLG

Abstract

Residential energy feedback refers to is about providing personalized information on household energy use to consumers to encourage energy savings. This paper conducts a review of field-based studies that have evaluated the impact of energy feedback on residential energy consumption. The review includes studies in real occupied homes that have deployed feedback intervention(s) and measured energy savings. Our study builds a taxonomy for energy feedback studies based on different characteristics of feedback such as frequency, type, presentation style, and methods of access. Energy savings from similar feedback types were found to differ depending on how the study was conducted. The reviewed studies deployed a range of feedback information including energy units, energy cost and tailored information conducted across diverse audiences (ethnicity, geographical positioning), varying experimental

Abstract

A system and method for generating a table-driven mutable blockchain are provided. The system includes one or more users 102A-102N associated with one or more nodes 104A-104N, a distributed file unit 106, a distributed ledger 108. The distributed file unit 106 stores a Lineage Table 200 and a global variable P 112, to keep count on the total number of blockchains in the system. The distributed ledger 108 stores one or more blockchains. The node 104A receives transaction details of transaction provided by user 102A through network 110, and defines transaction. The node 104 initializes linked list structure and creates a transaction of which a plurality of modifications is performed on it. The node 104 creates modified transaction in subsequent blockchains and then links these versions by adding sequence of elements in Linked List. The node 104 authenticates new transactions in main blockchain and the modified transactions in subsequent blockchains.

Abstract

One of the widely used peak reduction methods in smart grids is demand response, where one analyzes the shift in customers’ (agents’) usage patterns in response to the signal from the distribution company. Often, these signals are in the form of incentives offered to agents. This work studies the effect of incentives on the probabilities of accepting such offers in a real-world smart grid simulator, PowerTAC. We first show that there exists a function that depicts the probability of an agent reducing its load as a function of the discounts offered to them. We call it reduction probability (RP). RP function is further parametrized by the rate of reduction (RR), which can differ for each agent. We provide an optimal algorithm, MJS–EXPRESPONSE, that outputs the discounts to each agent by maximizing the expected reduction under a budget constraint. When RRs are unknown, we propose a Multi-Armed Bandit (MAB) based online algorithm, namely MJSUCB–EXPRESPONSE, to learn RRs. Experimentally we show that it exhibits sublinear regret. Finally, we showcase the efficacy of the proposed algorithm in mitigating demand peaks in a real-world smart grid system using the PowerTAC simulator as a test bed.