Vapor Phase Selective Catalytic Oxidation of Cyclohexane over 13X Supported Metal Oxides in the Presence of Ozone

This article reports the application of ozone for the selective oxidation of cyclohexane over 13X molecular sieve supported various metal oxides at ambient temperatures. From the SEM, XRD and HR-TEM results, the impregnated metal oxides are highly dispersed on the support. The activity results reveal that Co/MS, Mo/MS, Cu/MS, and Ag/MS catalysts produce cyclohexanone/cyclohexanol as selective oxidation products, whereas Ce/MS, Mn/MS, and V/MS catalysts yield, predominantly, CO and CO₂. Among them, Co/MS catalyst exhibits better conversion of 12.2% with selectively of 58% to cyclohexanone/cyclohexanol, which is attributed to the simultaneous activation of ozone and cyclohexane (-C-H bond) at ambient conditions.     

Near optimal process plan selection for multiple jobs in networked based manufacturing using multi-objective evolutionary algorithms

The networked manufacturing offers several advantages in current competitive atmosphere by way of reducing the manufacturing cycle time and maintenance of the production flexibility, thereby achieving several feasible process plans. In this paper, we have addressed a Multi Objective Problem (MOP) which covers-minimize the makespan and to maximize the machine utilization while generating the feasible process plans for multiple jobs in the context of network based manufacturing system. A new multi-objective based Territory Defining Evolutionary Algorithm (TDEA) to resolve the above computationally challenge problem have been developed. In particular, with two powerful Multi-Objective Evolutionary Algorithms (MOEAs), viz. Non-dominated Sorting Genetic Algorithm (NSGA-II) and Controlled Elitist-NSGA-II (CE-NSGA-II) the performance of the proposed TDEA has been compared. An illustrative example along with three complex scenarios is presented to demonstrate the feasibility of the approach. The proposed algorithm is validated and the results are analyzed and compared.     

The development and application of a thermodynamic database for magnesium alloys

The available thermodynamic databases for magnesium alloys are discussed in this paper. Of particular interest are the features of a magnesium database developed by the authors with 19 elements: Mg-Al-Ca-Ce-Cu-Fe-KLa-Li-Mn-Na-Nd-Pr-Si-Sn-Sr-Y-Zn-Zr. Using this database, two applications are presented. One is the phase evolution in AZ61 magnesium alloy including the variations of phase fractions, alloying compositions, and partition coefficients of alloying elements as a function of temperature (or solid fraction). The other is to understand sodium-induced high-temperature embrittlement in the Al-Mg alloy, which is ascribed to the formation of a liquid phase due to the presence of sodium traces.     

Phytosterols in Seaweeds: An Overview on Biosynthesis to Biomedical Applications

Seaweed extracts are considered effective therapeutic alternatives to synthetic anticancer, antioxidant, and antimicrobial agents, owing to their availability, low cost, greater efficacy, eco-friendliness, and non-toxic nature. Since the bioactive constituents of seaweed, in particular, phytosterols, possess plenty of medicinal benefits over other conventional pharmaceutical agents, they have been extensively evaluated for many years. Fortunately, recent advances in phytosterol-based research have begun to unravel the evidence concerning these important processes and to endow the field with the understanding and identification of the potential contributions of seaweed-steroidal molecules that can be used as chemotherapeutic drugs. Despite the myriad of research interests in phytosterols, there is an immense need to fill the void with an up-to-date literature survey elucidating their biosynthesis, pharmacological effects, and other biomedical applications. Hence, in the present review, we summarize studies dealing with several types of seaweed to provide a comprehensive overview of the structural determination of several phytosterol molecules, their properties, biosynthetic pathways, and mechanisms of action, along with their health benefits, which could significantly contribute to the development of novel drugs and functional foods.     

Synthesis, characterization, and antimicrobial activity of nano-hydroxyapatite-zinc for bone tissue engineering applications

The bone implants used in tissue repair are susceptible to infections caused by staphylococci, specifically Staphylococcus aureus. Hence, the development of better biological materials that provide antimicrobial activity in bone tissue engineering is required. The nanoparticles of hydroxyapatite (nHAp) and nHAp dopped with Zn (nHAp-Zn) were prepared by the wet chemical method and the ion exchange method, respectively. They were characterized using SEM, AFM, FTIR and XRD. The antibacterial activity of nHAp and nHAp-Zn was determined with Gram-negative and Gram-positive bacterial strains. The results indicated that nHAp alone was acting as an inert matrix and when substituted with Zn, it showed better antibacterial activity. The nHAp-Zn was found to be non-toxic to osteoprogenitor cells. Thus, due to the antimicrobial property of nHAp-Zn nanoparticles, we suggest that they would have potential applications towards bone tissue engineering.     

Differential Response to Single and Combined Salt and Heat Stresses: Impact on Accumulation of Proteins and Metabolites in Dead Pericarps of Brassica juncea

Dead organs enclosing embryos, such as seed coats and pericarps, are emerging as important maternally-derived components of the dispersal unit that affect seed performance and fate. In the face of climate change and increased incidents of heatwaves, we sought to investigate the effect of salinity (S), short episodes of high temperature (HS), and combination of S + HS (SHS), at the reproductive phase, on the properties of dead pericarps of Brassica juncea. Proteome and metabolome analyses revealed multiple proteins and metabolites stored in dead pericarps whose levels and composition were altered under single and combined stress conditions. The protein profile of SHS showed a higher correlation with salt than with HS indicating the dominant effect of salt over heat stress. On the other hand, the analysis of metabolites showed that the profile of SHS has better correlation with HS than with salt. The integration of metabolic and proteomic data showed that changes in TCA cycle intermediates and certain amino acids (e.g., proline) under salt treatments (S and SHS) are highly correlated with changes in proteins involved in their biosynthetic pathways. Thus, accumulation of proteins and metabolites in dead pericarps is differently affected by single and combination of salt and heat stresses. Salinity appears to dominate plant response to combined stresses at the protein level, while heat appears to be the major factor affecting metabolite accumulation in dead pericarps.     

A blockchain-based approach for a multi-echelon sustainable supply chain

Blockchain technology is destined to revolutionise supply chain processes. At the same time, governmental and regulatory policies are forcing firms to adjust their supply chains in response to environmental concerns. The objective of this study is therefore to develop a distributed ledger-based blockchain approach for monitoring supply chain performance and optimising both emission levels and operational costs in a synchronised fashion, producing a better outcome for the supply chain. We propose the blockchain approach for different production allocation problems within a multi-echelon supply chain (MESC) under a carbon taxation policy. As such, we couple recent advances in digitalisation of operations with increasingly stringent regulatory environmental policies. Specifically, with lead time considerations under emission rate constraints (imposed by a carbon taxation policy), we simultaneously consider the production, distribution and inventory control decisions in a production allocation-based MESC problem. The problem is then formulated as a Mixed Integer Non-Linear Programming (MINLP) model. We show that the distributed ledger-based blockchain approach minimises both total cost and carbon emissions. We then validate the feasibility of the proposed approach by comparing the results with a non-dominated sorting genetic algorithm (NSGA-II). The findings provide support for policymakers and supply chain executives alike.     

Biodegradable polymeric injectable implants for long‐term delivery of contraceptive drugs

Development of injectable, long‐lasting, contraceptive drug delivery formulations, and implants are highly desired to avoid unplanned pregnancies while improving patient compliance and reducing adverse side effects and treatment costs. The present study reports on the fabrication and characterization of two levonorgestrel (LNG) microsphere injectable formulations. Poly(?‐caprolactone) (PCL) with 12.5% and 24% (w/w) LNG were fabricated into microspheres, measuring 300 ± 125 µm, via the oil‐in‐water (o/w) emulsion solvent evaporation technique. Formulations showed sustained drug release up to 120 days. FTIR, XRD, DSC, and TGA confirmed the absence of LNG chemical interaction with PCL as well as its molecular level distribution. The in vitro release of LNG was calculated to be Fickian diffusion controlled and properly characterized. The inclusion of multiple elevated release temperatures allowed for the application of the Arrhenius model to calculate drug release constants and representative sampling intervals, demonstrating the use of elevated temperatures for accelerated‐time drug release studies. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46068.     

Integrated process planning and scheduling in networked manufacturing systems for I4.0: a review and framework proposal

Wireless Networks - Integrated process planning and scheduling in networked manufacturing systems plays a crucial role nowadays and in the forthcoming context of Industry 4.0 to enable effective...     

Exploring optimization and caching for efficient collection operations

Many large programs operate on collection types. Extensive libraries are available in many programming languages, such as the C++ Standard Template Library, which make programming with collections convenient. Extending programming languages to provide collection queries as first class constructs in the language would not only allow programmers to write queries explicitly in their programs but it would also allow compilers to leverage the wealth of experience available from the database domain to optimize such queries. This paper describes an approach to reduce the run time of programs involving explicit collection queries by performing run time query optimization that is effective for single runs of a program. In addition, it also leverages a cache to store previously computed results. The proposed approach relies on histograms built from the data at run time to estimate the selectivity of joins and predicates in order to construct query plans. Information from earlier executions of the same query during run time is leveraged during the construction of the query plans, even when the data has changed between these executions. An effective cache policy is also determined for caching the results of join (sub) queries. The cache is maintained incrementally, when the underlying collections change, and use of the cache space is optimized by a cache replacement policy. Our approach has been implemented within the Java Query Language (JQL) framework using AspectJ. Our approach demonstrated that its run time query optimization in integration with caching sub query result significantly improves the run time of programs with explicit queries over equivalent programs performing collection operations by iterating over those collections. This paper evaluates our approach using synthetic as well as real world Robocode programs by comparing it to JQL as a benchmark. Experimental results show that our approach performs better than the JQL approach with respect to the program run time.