The k-essence scalar field in the context of Supernova Ia observations

We study a framework where the hypothesis of a minimum length in space-time is complemented with the notion of reference frame invariance. It turns out natural to interpret the action of the obtained reference frame transformations in the context of doubly special relativity. As a consequence of this formalism we find interesting connections between the minimum length properties and the modified velocity-energy relation for ultra-relativistic particles. For example, we can predict the ratio between the minimum lengths in space and time using the results from OPERA on superluminal neutrinos.     

Radiation effect on optimum design analysis of a constructal T-shaped fin with variable thermal conductivity

A constructal T-shaped fin with temperature dependent thermal conductivity of fin the material exposed to both convective and radiative environments is analyzed by Adomian decomposition method. This method provides a closed form of analytical solution for analyzing the temperature distribution, performance and optimum design. A comparative study has been executed among the present and published works. Unlike the published work, dependent parameters on the performance and optimization analysis are highlighted.     

H2-release from alcohols,diols, and compounds with amino functionality promoted by titanium(II) sandwich complex, [Cp2Ti]: a theoretical approach

Structural Chemistry - The role of Ti(II) sandwich complex, [Cp2Ti], generated by the combination of [Cp2TiCl2] and two equivalents of nBuLi, in situ, in toluene has been investigated theoretically...     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

Mirror-Image Oligonucleotides: History and Emerging Applications

As chiral molecules, naturally occurring d -oligonucleotides have enantiomers, l -DNA and l -RNA, which are comprised of l -(deoxy)ribose sugars. These mirror-image oligonucleotides have the same physical and chemical properties as that of their native d -counterparts, yet are highly orthogonal to the stereospecific environment of biology. Consequently, l -oligonucleotides are resistant to nuclease degradation and many of the off-target interactions that plague traditional d -oligonucleotide-based technologies; thus making them ideal for biomedical applications. Despite a flurry of interest during the early 1990s, the inability of d - and l -oligonucleotides to form contiguous Watson–Crick base pairs with each other has ultimately led to the perception that l -oligonucleotides have only limited utility. Recently, however, scientists have begun to uncover novel strategies to harness the bio-orthogonality of l -oligonucleotides, while overcoming (and even exploiting) their inability to Watson–Crick base pair with the natural polymer. Herein, a brief history of l -oligonucleotide research is presented and emerging l -oligonucleotide-based technologies, as well as their applications in research and therapy, are presented.     

Novel nonlinear wave equation: Regulated rogue waves and accelerated soliton solutions

A new exactly solvable ($1+1$)-dimensional complex nonlinear wave equation exhibiting rich analytic properties has been introduced. A rogue wave (RW), localized in space–time like Peregrine RW solution, though richer due to the presence of free parameters is discovered. This freedom allows to regulate amplitude and width of the RW as needed. The proposed equation allows also an intriguing topology changing accelerated dark soliton solution in spite of constant coefficients in the equation.