Conversion of Methanol to Aromatic-Rich Gasoline over High-Efficiency Bifunctional Catalysts: Green Synthesis of GaZSM-5 Zeolites via Dry-Gel Conversion Strategy

Russian Journal of Applied Chemistry - Nanoscale ZSM-5 (AlZ5-D) and Ga-substituted ZSM-5 zeolites ((Al,Ga)Z5-D and GaZ5-D) were synthesized by a green dry-gel conversion strategy. For comparison,...     

Changes of microflora in high pressure treated Indian white prawn (Fenneropenaeus indicus)

ABSTRACT

This study was to investigate the piezotolerance and diversity indices of microflora of Indian white prawn (Fenneropenaeus indicus) after high pressure (HP)-treatment. Indian white prawns subjected to HP-treatments and its effect was studied up to species level and compared with unpressurized samples. The bacterium was identified by using bacterial identification schemes, biochemical tests and API kits (bioMérieux, Marcyl’Etoile, France). Diversity analysis was performed using PRIMER (Plymouth Routines in Multivariate Research) software v 5.2.2. The significant elimination of microflora was found to be proportional with the pressure level. In the case of spore formers, mere destruction was noticed after HP-treatment. Arthrobacter spp., Listeria grayi and Corynebacterium spp. were the most piezotolerant bacteria in HP-treated samples. Diversity indices revealed a significant reduction of microflora of Indian white prawn. The apparent reduction of microflora with pressure level was clearly evident from the diversity indices; moreover a diminished piezotolerance of Gram negative spoilage bacteria was also observed.     

Characteristics investigation on heat transfer growth of sonochemically synthesized ZnO-DW based nanofluids inside square heat exchanger

Journal of Thermal Analysis and Calorimetry - In recent decades, the growth of heat transfer using nanomaterials in the conventional base fluid has caught the attention of researchers...     

Local linear convergence analysis of Primal–Dual splitting methods

In this paper, we study the local linear convergence properties of a versatile class of Primal–Dual splitting methods for minimizing composite non-smooth convex optimization problems. Under the assumption that the non-smooth components of the problem are partly smooth relative to smooth manifolds, we present a unified local convergence analysis framework for these methods. More precisely, in our framework, we first show that (i) the sequences generated by Primal–Dual splitting methods identify a pair of primal and dual smooth manifolds in a finite number of iterations, and then (ii) enter a local linear convergence regime, which is characterized based on the structure of the underlying active smooth manifolds. We also show how our results for Primal–Dual splitting can be specialized to cover existing ones on Forward–Backward splitting and Douglas–Rachford splitting/ADMM (alternating direction methods of multipliers). Moreover, based on these obtained local convergence analysis result, several practical acceleration techniques are discussed. To exemplify the usefulness of the obtained result, we consider several concrete numerical experiments arising from fields including signal/image processing, inverse problems and machine learning. The demonstration not only verifies the local linear convergence behaviour of Primal–Dual splitting methods, but also the insights on how to accelerate them in practice.