Efficiency of connected binary block designs when a single observation is unavailable

In this paper the problem of finding the design efficiency is considered when a single observation is unavailable in a connected binary block design. The explicit expression of efficiency is found for the resulting design when the original design is a balanced incomplete block design or a group divisible, singular or semiregular or regular with ₁>0, design. The efficiency does not depend on the position of the unavailable observation. For a regular group divisible design with ₁>0, the efficiency depends on the position of the unavailable observation. The bounds, both lower and upper, on the efficiency are given in this situation. The efficiencies of designs resulting from a balanced incomplete block design and a group divisible design are in fact high when a single observation is unavailable.The work of the first author is sponsored by the Air Force Office of Scientific Research under Grant AFOSR-90-0092.On leave from Indian Statistical Institute, Calcutta, India. The work of the third author was supported by a grant from the CMDS, Indian Institute of Management, Calcutta.     

Energy dependence of the fast fragment of the target nucleus in high energy hadron-nucleus interaction

This paper presents an extensive study on the dependence of the mean number of the fast fragment of the target nucleus n _g on the incident beam energy in proton-nucleus interaction in emulsion in the range 6·2E ₀400 GeV/c. It has been observed that n _g decreases in the range 6·2E ₀200 GeV/c, then increases and attains an approximately steady value up to 400 GeV/c. It is very difficult to explain this behaviour with the help of the existing nuclear production models.The author would like to thank Prof A. J. Herz (CERN), Prof. K. D. Tolostov (Dubna, U.S.S.R.), Prof. P. L. Jain (State University of New York, U.S.A.), Prof. G. Giacomelly (Italy) for kindly supplying the exposed emulsion plates.     

Novel Synthesis of Agaritine,a 4-Hydrazinobenzyl-Alcohol Derivative Occurring in Agaricaceae

The 4-hydrazinobenzyl alcohol ( 3 was prepared (58%)) by diiobutylaluminiumhydride reduction of methyl 4-hydrazinobenzoate ( 4 ), whereas LiA1H₄ or LiBh₄ reduction of 4 proceeded further to yield (via intermediate 3 ) (4-tolyl)hydrazine ( 5 ). The alcohol 3 was stable under O₂-free conditions and exhibited no tendency to eliminate H₂O, neither thermally nor with H⁺ catalysis. Oxidation of 3 with SeO₂ yielded 4-(hydroxymethyl)benzine-diazonium ion ( 8 ), identified by its azo coupling product 9 with 2-naphthol. Condensation of 3 with 1-benzyl 5-Hydrogen N-(benzyloxycarbonyl)-L-glutamate ( 10 ) in presence of dicyclohexylcarbodiimide afforded 81% of N²-(benzyloxycarbonyl)-L- glutamic acid 1-(benzyl-ester) 5-{2-[4-(hydroxymethyl)phenyl]hydrazide} ( 11 ) which upon controlled hydrogenolysis (quinoline-sulfur-poisoned Pd/C catalyst) gave 82% of L-Glutamic acid 5-{2-[4-(hydroxymethyl)phenyl] hydrazide} ( 1 ), i. e. agaritine, a metabolite of Agaricus bisporus. Without poisoning of the catalyst, hydrogenolysis of ( 11 ) yielded L-glutamic acid 5-[2-(4-tolyl)hydrazide] ( 12 ).     

Is Bitcoin’s Carbon Footprint Persistent? Multifractal Evidence and Policy Implications

The Bitcoin mining process is energy intensive, which can hamper the much-desired ecological balance. Given that the persistence of high levels of energy consumption of Bitcoin could have permanent policy implications, we examine the presence of long memory in the daily data of the Bitcoin Energy Consumption Index (BECI) (BECI upper bound, BECI lower bound, and BECI average) covering the period 25 February 2017 to 25 January 2022. Employing fractionally integrated GARCH (FIGARCH) and multifractal detrended fluctuation analysis (MFDFA) models to estimate the order of fractional integrating parameter and compute the Hurst exponent, which measures long memory, this study shows that distant series observations are strongly autocorrelated and long memory exists in most cases, although mean-reversion is observed at the first difference of the data series. Such evidence for the profound presence of long memory suggests the suitability of applying permanent policies regarding the use of alternate energy for mining; otherwise, transitory policy would quickly become obsolete. We also suggest the replacement of ‘proof-of-work’ with ‘proof-of-space’ or ‘proof-of-stake’, although with a trade-off (possible security breach) to reduce the carbon footprint, the implementation of direct tax on mining volume, or the mandatory use of carbon credits to restrict the environmental damage.     

Reactivity studies of carbon,phosphorus and sulfur‐based acyl sites with tertiary oximes in gemini surfactants

Kinetic studies of the reactions of tertiary oximes (monoisonitroso acetone; MINA and butane 2,3 dione monooxime; BDMO) with some carboxylate (p‐nitrophenyl acetate and p‐nitrophenyl benzoate), phosphate (p‐nitrophenyl diphenyl phosphate and bis (2,4‐dinitrophenyl) phosphate) and sulfonate (p‐nitrophenyl p‐toluene sulphonate) esters in gemini surfactants have been conducted. The observed first‐order rate constant versus surfactant profiles show micelle‐assisted bimolecular reactions involving interfacial ion exchange between bulk aqueous media and micellar pseudophase. Experimental results showed that MINA exhibited better nucleophilic activity towards ester cleavage than BDMO. Pseudophase model has been applied in order to determine micellar second‐order rate constants and binding constants. Copyright © 2013 John Wiley & Sons, Ltd.     

Hydrodynamic and thermal interactions of a cluster of solid particles in a pool of liquid of different Prandtl numbers using two-fluid model

The knowledge of thermal interaction between hot particles and liquid is essential for many engineering applications. The main focus of the present study is to understand the underlying phenomena of transient interaction between the hot particles and the liquid of varying Prandtl number under different parametric conditions. Analysis is carried out numerically using in-house multiphase code based on Eulerian two-fluid laminar model. The code is validated against existing results. The dispersion and penetration characteristics of the particles are observed to be a strong function of Prandtl number as well as volume fraction and particle diameter, with a stronger mushrooming observed for lower particle size or high Prandtl number liquid. The thermal interaction is observed to be between the particles and the narrow thermal envelope surrounding the particles. The particles cooling rate are observed to be several orders faster in a liquid with lower Prandtl number.     

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.