Techno economic and environmental assessment of Flettner rotors for marine propulsion

Wind energy is a mature renewable energy source that offers significant potential for near-term (2020) and long-term (2050) greenhouse gas (GHG) emissions reductions. Similar to all sectors of the transportation industry, the marine industry is also focused towards reduction of environmental emissions. A direct consequence of this being is a renewed interest in utilising wind as supplementary energy source for propulsion on cargo/merchant ships. This research utilises a techno economic and environmental analysis approach to assess the possibility and benefits of harnessing wind energy, with an aim to establish the potential role of wind energy in reducing GHG emissions during conventional operation of marine vessels. The employed approach enables consistent assessment of different competing traditional propulsion systems when operated in conjunction with a novel environmental friendly technology, in this instance being the Flettner rotor technology. The assessment specifically focuses on quantifying the potential and relative reduction in fuel consumption and pollutant emissions that may be accrued while operating on typical Sea Lines of Communication. The results obtained indicate that the implementation of Flettner towers on commercial vessels could result in potential savings of up to 20% in terms of fuel consumption, and similar reductions in environmental emissions

A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments

Integrated supplier selection and order allocation is an important decision for both designing and operating supply chains. This decision is often influenced by the concerned stakeholders, suppliers, plant operators and customers in different tiers. As firms continue to seek competitive advantage through supply chain design and operations they aim to create optimized supply chains. This calls for on one hand consideration of multiple conflicting criteria and on the other hand consideration of uncertainties of demand and supply. Although there are studies on supplier selection using advanced mathematical models to cover a stochastic approach, multiple criteria decision making techniques and multiple stakeholder requirements separately, according to authors' knowledge there is no work that integrates these three aspects in a common framework. This paper proposes an integrated method for dealing with such problems using a combined Analytic Hierarchy Process-Quality Function Deployment (AHP-QFD) and chance constrained optimization algorithm approach that selects appropriate suppliers and allocates orders optimally between them. The effectiveness of the proposed decision support system has been demonstrated through application and validation in the bioenergy industry

Combinatorial Assortment Optimization

Assortment optimization refers to the problem of designing a slate of products to offer potential customers, such as stocking the shelves in a convenience store. The price of each product is fixed in advance, and a probabilistic choice function describes which product a customer will choose from any given subset. We introduce the combinatorial assortment problem, where each customer may select a bundle of products. We consider a model of consumer choice where the relative value of different bundles is described by a valuation function, while individual customers may differ in their absolute willingness to pay, and study the complexity of the resulting optimization problem. We show that any sub-polynomial approximation to the problem requires exponentially many demand queries when the valuation function is XOS, and that no FPTAS exists even for succinctly-representable submodular valuations. On the positive side, we show how to obtain constant approximations under a "well-priced" condition, where each product's price is sufficiently high. We also provide an exact algorithm for $k$-additive valuations, and show how to extend our results to a learning setting where the seller must infer the customers' preferences from their purchasing behavior

OPTIMIZATION OF CULTURE CONDITIONS OF STREPTOMYCES CARPATICUS (MTCC-11062) FOR THE PRODUCTION OF ANTIMICROBIAL COMPOUND

Objective: To improve the antimicrobial compound productivity of Streptomyces carpaticus (MTCC-11062) by optimizing its physical and chemical parameters Methods: Streptomyces carpaticus (MTCC-11062) was isolated from Visakhapatnam sea coast of Bay of Bengal and was screened for its antimicrobial activity by using agar well diffusion method. To improve the production of antimicrobial compound the medium composition and physical parameters were optimized and its productivity was studied against Bacillus cereus (MTCC 430) Escherichia coli (MTCC 443), Candida albicans (MTCC 227) obtained from MTCC, Chandigarh, India. Results: Optimum growth of mycelium and antimicrobial compound production occurred at pH 7.2, agitation 180 rpm and temperature 300C with glucose 10g/L, soyabean meal 2.5g/L, K2HPO4 2g/L, MgSO4 1g/L, NaCl 7.5g/L and trace salts. Conclusion: The optimization of cultural conditions proposed in this paper has effetely improved the antimicrobial compound productivity of Streptomyces carpaticus (MTCC-11062)

Techno economic and environmental assessment of wind assisted marine propulsion systems

In recent years, the increase in marine fuel prices coupled with stricter regulations on pollutant emissions set by the International Maritime Organization have promoted the research in new propulsion technologies and the utilisation of cleaner fuels. This paper describes a novel methodology to enable quantifying and evaluating the environmental and economic benefits that new technologies and fuels could allow in the marine sector. The proposed techno economic and environmental analysis approach enables consistent assessment of different traditional propulsion systems (diesel engine and gas turbine) when operated in conjunction with a novel environmental friendly technology, such as a vertical axis wind turbine. The techno-economic and environmental assessment is focused on the potential reduction in fuel consumption and pollutant emissions that may be accrued while operating on typical Sea Lines Of Communication (Mediterranean, North Sea, Atlantic). The study demonstrates the benefits of the installation of two vertical axis wind turbines on the deck of a ship in conjunction with conventional power plants. The analysis indicates that the performance of the wind turbines and the corresponding benefits strongly depend on the routes and environment in which they operate (therefore favourable wind conditions) allowing fuel savings from 14% (in the gas turbine case) to 16% (in the diesel engine case). The study also indicates that possible benefits may diminish for weak wind conditions. The results reported in this paper establish the economic benefits of installing vertical axis wind turbines in conjunction with conventional technology (Diesel and Gas Turbine Power plants) when installed on a ship travelling through the Atlantic Ocean. The primary purpose of this study is to introduce a methodology to demonstrate the application, performance and economic benefits of the technology at a preliminary design phase and further form a foundation for more elaborate analysis on the subject in the future

SCREENING AND MOLECULAR DOCKING STUDIES OF NEW NATURAL AGONISTS AGAINST PEROXISOME PROLIFERATOR-ACTIVATED RECEPTOR-ALPHA TARGETED TO TREAT OBESITY

ABSTRACTObjective: Obesity was considered as a serious health concern apart from the age group in today's population globally. The percentage of obese peoplein the world's population is increasing at a faster rate, and health issues arising due to obesity are gradually increasing. Our present insilico study wasaimed to screen out natural molecules against the peroxisome proliferator-activated receptor (PPAR), especially alpha aids in triggering the obesity.Methods: Several targets for treating obesity were identified, and one among such promising target was PPAR. Using the insilico applications such asnatural database was screened and the molecules were further evaluated based on their docking score parameter with the receptor.Results: The docking methodology suggested that two molecules zinc02091671 and zinc02137525 were found to reproduce the similar type ofinteractions such as that of the known inhibitor and crystal ligand.Conclusion: The reported two molecules were found to be promising agonists based on the computational studies and can be advanced the in vitrobased evaluation.Keywords: Obesity, Peroxisome proliferator-activated receptor, e-pharmacophore, QikProp, Docking

Dynamic Pricing with a Prior on Market Response

We study a problem of dynamic pricing faced by a vendor with limited inventory, uncertain about demand, aiming to maximize expected discounted revenue over an infinite time horizon. The vendor learns from purchase data, so his strategy must take into account the impact of price on both revenue and future observations. We focus on a model in which customers arrive according to a Poisson process of uncertain rate, each with an independent, identically distributed reservation price. Upon arrival, a customer purchases a unit of inventory if and only if his reservation price equals or exceeds the vendor’s prevailing price.Institute for Operations Research and the Management Sciences (MSOM society)National Science Foundation (U.S.) (grant IIS- 0428868

Quantifying signals with power-law correlations: A comparative study of detrended fluctuation analysis and detrended moving average techniques

Detrended fluctuation analysis (DFA) and detrended moving average (DMA) are two scaling analysis methods designed to quantify correlations in noisy non-stationary signals. We systematically study the performance of different variants of the DMA method when applied to artificially generated long-range power-law correlated signals with an {\it a-priori} known scaling exponent $\alpha_{0}$ and compare them with the DFA method. We find that the scaling results obtained from different variants of the DMA method strongly depend on the type of the moving average filter. Further, we investigate the optimal scaling regime where the DFA and DMA methods accurately quantify the scaling exponent $\alpha_{0}$, and how this regime depends on the correlations in the signal. Finally, we develop a three-dimensional representation to determine how the stability of the scaling curves obtained from the DFA and DMA methods depends on the scale of analysis, the order of detrending, and the order of the moving average we use, as well as on the type of correlations in the signal.Comment: 15 pages, 16 figure

Assortment optimisation under a general discrete choice model: A tight analysis of revenue-ordered assortments

The assortment problem in revenue management is the problem of deciding which subset of products to offer to consumers in order to maximise revenue. A simple and natural strategy is to select the best assortment out of all those that are constructed by fixing a threshold revenue $\pi$ and then choosing all products with revenue at least $\pi$. This is known as the revenue-ordered assortments strategy. In this paper we study the approximation guarantees provided by revenue-ordered assortments when customers are rational in the following sense: the probability of selecting a specific product from the set being offered cannot increase if the set is enlarged. This rationality assumption, known as regularity, is satisfied by almost all discrete choice models considered in the revenue management and choice theory literature, and in particular by random utility models. The bounds we obtain are tight and improve on recent results in that direction, such as for the Mixed Multinomial Logit model by Rusmevichientong et al. (2014). An appealing feature of our analysis is its simplicity, as it relies only on the regularity condition. We also draw a connection between assortment optimisation and two pricing problems called unit demand envy-free pricing and Stackelberg minimum spanning tree: These problems can be restated as assortment problems under discrete choice models satisfying the regularity condition, and moreover revenue-ordered assortments correspond then to the well-studied uniform pricing heuristic. When specialised to that setting, the general bounds we establish for revenue-ordered assortments match and unify the best known results on uniform pricing.Comment: Minor changes following referees' comment