Towards finding the best-fit distribution for OSN data

The Journal of Supercomputing - Currently, all online social networks (OSNs) are considered to follow a power-law distribution. In this paper, the degree distribution for multiple OSNs has been...     

A simulation study of reduction kinetics for sponge iron production in a rotary hearth furnace

ABSTRACT

A mathematical model has been developed by coupling genetic algorithm (GA) with heat and material balance equations to estimate rate parameters and solid-phase evolution related to the reduction of iron ore-coal composite pellets in a multi-layer bed Rotary hearth Furnace (RHF). The present process involves treating iron ore-coal composite pellets in a crucible over the hearth in RHF. The various solid phases evolved at the end of the process are estimated experimentally, and are used in conjunction with the model to estimate rate parameters. The predicted apparent activation energy for the wustite reduction step is found to be lower than those of the reduction of higher oxides. The thermal efficiency is found to decrease significantly with an increase in the carbon content of the pellet. Thermal efficiency was also found to increase mildly up to three layers. Multilayer bed remains as a potential design parameter to increase thermal efficiency.     

Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.

     

Profile of Triacylglycerols,Phenols, and Vitamin E of Manjari Medika Grape Seed Oil and Cake: Introducing a Novel Indian Variety

The aim of the study is to investigate the biochemical composition of grapeseed oil and cake from an unexplored Indian grape‐juice cultivar, Manjari Medika (MM). The composition of oil and residual seed cake is evaluated using various chromatographic and spectroscopic techniques. The findings demonstrate that the vitamin E content of MM‐seed oil (1.15–1.35 g kg^?1) is distinctively higher than the Codex standard, suggesting its superior quality as an edible oil. The predominant triacylglycerols include trilinolein (LLL, 43%), dilinoleoyl‐stearylglycerol (LSL, 19%), and dilinoleoyl‐palmitoylglycerol (LLP, 11%), which are earlier recognized as natural antioxidants. The seed‐cake is rich in polyphenols including acylated anthocyanins (e.g., pelargonidin‐3‐O‐coumaroyl glucoside) and certain other flavonoids (e.g., catechin). The profile of phytonutrients in MM seed‐oil and cake is significantly superior to its seeded female parent and two other widely cultivated wine‐grape varieties. In brief, the studied by‐products of this new grape‐juice cultivar can be an important source of high‐value ingredients for use in food supplements, nutraceuticals, and functional foods. Practical applications: This study reports the phytochemical profile of the seed‐oil and seed cake derived from a newly developed grape variety, Manjari Medika. High contents of selective antioxidants: lipids, vitamin E, and phenols in the seed‐oil and cake with health benefits suggest their potential for use in nutraceutical and functional foods. These byproducts can be utilised as ingredients of functional foods and nutraceuticals (e.g., grape seed oil capsule) and also as raw materials in food supply chains (e.g., for production of grape cookies or cake). MM can also be utilized as a colorant in the food industry.     

Attribute Allocation and Retrieval Scheme for Large-Scale Sensor Networks

Wireless sensor network is an emerging technology that enables remote monitoring of large geographical regions. In this paper, we address the problem of distributing attributes over such a large-scale sensor network so that the cost of data retrieval is minimized. The proposed scheme is a data-centric storage scheme where the attributes are distributed over the network depending on the correlations between them. The problem addressed here is similar to the Allocation Problem of distributed databases. In this paper, we have defined the Allocation Problem in the context of sensor networks and have proposed a scheme for finding a good distribution of attributes to the sensor network. We also propose an architecture for query processing given such a distribution of attributes. We analytically determine the conditions under which the proposed architecture is beneficial and present simulation results to demonstrate the same. To the best of our knowledge, this is the first attempt to determine an allocation of attributes over a sensor network based on the correlations between attributes.     

An application of chemometric techniques to analyze the effects of the wave function modifications on the intermolecular stretching frequencies of the hydrogen-bonded complexes

Factorial design and principal component models are used to determine how ab initio H-bond stretching frequencies depend on characteristics of the molecular orbital wave functions of acetylene–HX, ethylene–HX and cyclopropane–HX π-type hydrogen complexes with X=F, Cl, CN, NC and CCH. The results obtained for the three sets of complexes show that factorial design and principal component analyses complement each other. Factorial design calculations clearly show that these frequencies are affected mostly by inclusion of electron correlation on the calculation level. On average, their values are increased by about 25 cm⁻¹ due to a change from the Hartree–Fock (HF) to Möller–Plesset 2 (MP2) level. Valence, diffuse and polarization main effects as well as valence–diffuse, diffuse–correlation and polarization–correlation interaction effects are also important to better describe a factorial model to the H-bond stretching frequencies of these hydrogen complexes. This simplified model has been successful in reproducing the complete ab initio results, which correspond to two hundred and forty calculations. Principal component analyses applied only to hydrogen-bonded complexes whose experimental frequencies are known, has revealed that the six-dimensional original space can be accurately represented by a bidimensional space defined by two principal components. Its graphical representation reveals that the experimental intermolecular stretching frequencies are in closest agreement with the MP2/6–31+G and MP2/6–311+G ab initio results.     

A control-theoretic approach to distributed discrete-valued decision-making in networks of sensing agents

We address the problem of global sensor fusion for the purpose of distributed decision-making, from a control-theoretic perspective. In particular, we introduce a quasi-linear stochastic distributed protocol, using which a network of sensing agents can reach agreement in order to take a collective action. Using control-theoretic methods, we design the parameters of our protocol - which include weights in the local update rules used by the agents and a finite stopping time - to achieve agreement in a fair and rapid manner. We show analytically that the developed protocol achieves fair agreement with certainty in the noise-free case and achieves fair agreement with high probability even in the presence of communication noise and assuming very little information storage capability for the agents. Our development is illustrated throughout with a canonical example motivated by autonomous vehicle control.