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.     

Nucleophilic dephosphorylation of p-nitrophenyl diphenyl phosphate in cationic micellar media

The kinetics of nucleophilic dephosphorylation of p-nitrophenyl diphenyl phosphate by hydroxamate ions (R'(C=O)N(RO-)) have been investigated in aqueous cationic micellar media at pH 9.12 and 27 degrees C. The pseudo-first-order rate constant-surfactant profiles show micelle-assisted bimolecular reactions involving interfacial ion exchange between bulk aqueous media and micellar pseudophase. N-Substituted hydroxamate ion shows higher reactivity over the unsubstituted hydroxamate ions in cationic micellar media. The kinetic data are discussed in terms of the pseudophase ion exchange model.     

Studies on vinyl photopolymerization using a macromolecular C.T. Complex between poly(N-vinyl carbazole) and bromine as the photoinitiator

Photopolymerization of methyl methacrylate (MMA) was studied at 40°C using a macromolecular C.T. Complex between poly(N-vinyl carbazole) and bromine, expressed in brief as (PNVC–Br₂) complex, as the photoinitiator. Initiator exponent was 0.40 for [PNVC–Br₂] ≤ 2.5 × 10^?3 mol L^?1 and practically zero for [PNVC–Br₂] > 2.5 × 10^?3 mol L^?1. Monomer exponent in different diluent systems such as benzene, carbon tetrachloride, and acetone was close to 1.0. Low initiator exponent (<0.5) is explained on the basis of an initiator-dependent termination mechanism, in addition to the usual bimolecular termination. Analysis of kinetic data indicates that the initiator-dependent termination is primarily due to degradative initiator transfer and that due to primary radicals is considered inconsequential in view of monomer exponent being close to unity. The non-ideal termination process assumes over-whelming prominence at high [PNVC–Br₂].     

Two (E)‐2‐arylidene‐1,4‐di‐p‐tosyl‐1,2,3,4‐tetrahydroquinoxaline compounds: supramolecular frameworks built with C—H...O and C—H...π(arene) hydrogen bonds and π–π stacking interactions

The pyrazine ring in two N‐substituted quinoxaline derivatives, namely (E)‐2‐(2‐methoxybenzylidene)‐1,4‐di‐p‐tosyl‐1,2,3,4‐tetrahydroquinoxaline, C₃₀H₂₈N₂S₂O₅, (II), and (E)‐methyl 2‐[(1,4‐di‐p‐tosyl‐1,2,3,4‐tetrahydroquinoxalin‐2‐ylidene)methyl]benzoate, C₃₁H₂₈N₂S₂O₆, (III), assumes a half‐chair conformation and is shielded by the terminal tosyl groups. In the molecular packing of the compounds, intermolecular C—H...O hydrogen bonds between centrosymmetrically related molecules generate dimeric rings, viz. R₂²(22) in (II) and R₂²(26) in (III), which are further connected through C—H...π(arene) hydrogen bonds and π–π stacking interactions into novel supramolecular frameworks.     

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.     

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.