Experimental evidence on the origin of Ca-rich carbonated melts formed by interaction between sedimentary limestones and mantle-derived ultrabasic magmas

In this experimental study, we documented the formation of strongly ultrabasic and ultracalcic melts through the interaction of melilititic and basanitic melts with calcite. Three strongly to moderately SiO2-undersaturated volcanic rocks from the Bohemian Massif (central Europe) were mixed with 10, 30, and 50 wt% CaCO3 and melted at 1100, 1200, and 1300 °C at 2 kbar to evaluate the maximum amount of carbonate that can be assimilated by natural ultrabasic melts at shallow depths. Experiments revealed a surprisingly complete dissolution of the CaCO3, only rarely reaching carbonate saturation, with typical liquidus phases represented by olivine, spinel, melilite, and clinopyroxene. Only in the runs with the most SiO2-undersaturated compositions did abundant monticellite form instead of clinopyroxene. For all starting mixtures, strongly ultrabasic (SiO2 down to 15.6 wt%), lime-rich (CaO up to 43.6 wt%), ultracalcic (CaO/Al2O3 up to ~27) melt compositions were produced at 1200 and 1300 °C, with up to ~25 wt% dissolved CO2. When present, quenched olivine showed much higher forsterite content (Fo95–97) than olivine in the natural samples (Fo79–85). The two major results of this study are (1) silicate-carbonatite melt compositions do not necessarily imply the existence of carbonatitic components in the mantle, because they are also produced during limestone assimilation, and (2) Fo-rich olivines cannot be used to infer any primitive character of the melt nor high potential temperature (Tp).

Statistical and Computational Needs for Big Data Challenges

The traditional way of formatting information from transactional systems to make them available for “statistical processing” does not work in a situation where data is arriving in huge volumes from diverse sources, and where even the formats could be changing. Faced with this volume and diversification, it is essential to develop techniques to make best use of all of these stocks in order to extract the maximum amount of information and knowledge. Traditional analysis methods have been based largely on the assumption that statisticians can work with data within the confines of their own computing environment. But the growth of the amounts of data is changing that paradigm, especially which ride of the progress in computational data analysis. This chapter builds upon sources but also goes further in the examination to answer this question: What needs to be done in this area to deal with big data challenges?

Data Mining algorithms in search of effective conditions for conducting chemical reactions

В настоящее время накоплено значительное количество экспериментальных данных, фиксирующих процесс протекания химических реакций. Анализ этих данных комплексом алгоритмов Data Mining дает важную практическую информацию для поиска эффективных условий проведения реакций, при которых получается максимальное количество целевого продукта при минимальных затратах. В данной работе на примере работы с базой, содержащей данные о протекании реакции карбонилирования различных олефинов, показано, как разработанный нами программный комплекс позволяет извлечь полезные знания, способствующие повышению эффективности химических реакций. At present, a significant amount of experimental data has been accumulated, recording the process of the occurrence of chemical reactions. Analysis of these data by a set of Data Mining algorithms provides important practical information for finding effective conditions for carrying out reactions, at which the maximum amount of the target product is obtained at minimal cost. In this paper, using the example of working with a database containing data on the course of the carbonylation reaction of various olefins, it is shown how the software package developed by us allows us to extract useful knowledge that contributes to an increase in the efficiency of chemical reactions.

Evapotranspiration and soil moisture balance in relation to yield of pearl millet (Pennisetum gIaucum)

In this study. the agrometeorological data of Hisar and Bellary have been utilized to examine evapotranspirative demand of the pearl millet at different phases during the crop growing period.Water use efficiency of the crop has also been worked out and discussted. Cumulative soil moisture balance was computed to assess the crop stress situation, Attempt has also been made to assess the moisture availability to the crop during the growing period and to estimate the yield, The study revealed that the pearl millet used maximum amount of water during the flowering stage at both locations. The same variety of pearl millet was found to use water more efficiently at Hisar than at Bellary. The yield index based on soil moisture balance technique was found to give a good indication of the yield.

Modeling of systems with a closed loop of material resources circulation

The work is devoted to the problem of artificial realization of the unique property of living systems to self-recovery due to the existence in them of mechanisms of accumulation and closed circulation of material resources. The problem of developing a mathematical model that reproduces the processes of functioning of such systems is being solved. A functional diagram of these processes is built, in which the stages of active use of resources, their recovery and subsequent accumulation as a reserve are highlighted. The formal description of the system is made in the class of stochastic models with continuous time – in the form of a Markov process with a discrete set of states. Analytical expressions for the final probabilities of each of its states were found on the basis of the hypothesis of the Poisson character of event streams in the system. As an assessment of the system’s ability to self-recovery, the probability of its functioning with the maximum amount of resources capable of being processed by the system was calculated. Using a numerical example, a quantitative study of the dependence of this estimate on the main variable parameters of the closed-loop system was carried out: the number of recovery channels, the intensity of these channels, and the amount of resources accumulated in the reserve. The presence of an accumulated stock of resources in the system allows ensuring high indicators of its efficiency with a significant decrease in the requirements for the total intensity of resource recovery.

Modeling of electrotransport properties of Li-intercalated graphene film

In this work, within the framework of density functional theory combined with the method of nonequilibrium Green’s functions the density of states, transmission spectrum, current-voltage characteristics, and differential conductivity of Li-intercalated graphene (LiC6) have been determined. It is shown that in the energy range of -1.3÷-1.05 eV the quasiparticle transport through the nanostructure is disable. The features of IV- and dI/dV-characteristics of LiC6 in the form of decreasing of resistance in the range of -0.4÷0.4 V were revealed, and in the interval of 0.4÷1.4 V formation of negative differential resistance area, related to scattering of quasiparticles. It is established, that LiC6 nanodevice has 12÷13 ballistic channels and has the maximum amount of conductance 12÷13G0 , where Go is the conductance quantum.

Strategy on Disaster Recovery Management based on Graph Theory Concepts

The COVID-19 pandemic has asserted major baseline facts from disaster anthropology during the last three decades. Resilience could be based on the solution to the question: "What is the maximum amount of destruction, if any, that the graph (a network) can sustain while ensuring that at least one of each technology type remains and that the remaining induced subgraph is properly colored?" The concept of a graph's Chromatic Core Subgraph is a solution to the stated problem. In this paper, the pandemic graphs and certain sequential graphs are developed. For these graphs, the Chromatic core subgraph is obtained. The results of the pandemic graphs' Chromatic core subgraph are used to develop a disaster recovery strategy for the COVID-19 pandemic.

Budget-cut: introduction to a budget based cutting-plane algorithm for capacity expansion models

We present an algorithm to solve capacity extension problems that frequently occur in energy system optimization models. Such models describe a system where certain components can be installed to reduce future costs and achieve carbon reduction goals; however, the choice of these components requires the solution of a computationally expensive combinatorial problem. In our proposed algorithm, we solve a sequence of linear programs that serve to tighten a budget—the maximum amount we are willing to spend towards reducing overall costs. Our proposal finds application in the general setting where optional investment decisions provide an enhanced portfolio over the original setting that maintains feasibility. We present computational results on two model classes, and demonstrate computational savings up to 96% on certain instances.

Integrated Information Theory, Intrinsicality, and Overlapping Conscious Systems

Integrated information theory (IIT) identifies consciousness with having a maximum amount of integrated information. But a thing's having the maximum amount of anything cannot be intrinsic to it, for that depends on how that thing compares to certain other things. IIT's consciousness, then, is not intrinsic. A mereological argument elaborates this consequence: IIT implies that one physical system can be conscious while a physical duplicate of it is not conscious. Thus, by a common and reasonable conception of intrinsicality, IIT's consciousness is not intrinsic. It is then argued that to avoid the implication that consciousness is not intrinsic, IIT must abandon its exclusion postulate, which prohibits overlapping conscious systems. Indeed, theories of consciousness that attribute consciousness to physical systems should embrace the view that some conscious systems overlap. A discussion of the admittedly counterintuitive nature of this solution, along with some medical and neuroscientific realities that would seem to support it, is included.

Solving transportation problem using modified ASM method

Transportation problems are related to activities aimed at minimizing the cost of distributing goods from a source to a destination. One of the methods used to solve transportation problems is the ASM Method as a method capable of producing optimal direct solutions without having to determine the initial basic feasible solution first. Determination of the allocation of goods in the ASM Method uses a reduced cost of 0 by calculating the maximum amount in the allocation of goods. Then the ASM method is modified so that the iteration used is simpler in obtaining the optimal direct solution without calculating the maximum number of row and column elements. The method is called Modified ASM Method. This method also provides more optimal results than the ASM method. This research aimed to solve transportation problems using the Modified ASM method to produce optimal solutions directly. The research procedure identifies and forms a model of transportation problems (variable decisions, objective functions and constraint functions), identifies types of transportation problems (balanced or unbalanced), and obtains direct solutions by solving transportation problems using the Modified ASM method. This research shows that the Modified ASM method successfully solves the problem of balanced and unbalanced transportation by producing optimal solutions in a simpler way than the ASM method.