Thermodynamics of SmMnO3 and SmMn2O5 phases determined by the E.M.F. method

Streszczenie angielskie: Using solid oxide galvanic cells of the type: MnO + Sm2O3 + SmMnO3 / O-2/ Ni + NiO and Mn3O4 + SmMnO3 + SmMn2O5 / O-2 / air the equilibrium oxygen pressure for three-phase equilibria described by the following reactions of formation of ternary phases: MnO + 1/2Sm2O3 + 1/4O2 = SmMnO3 1/3Mn3O4 + SmMnO3 + 1/3O2 = SmMn2O5 was determined in the temperature range from 1173 to 1450 K. From the obtained experimental data the corresponding Gibbs free energy change for above reactions of phases formation was derived: ΔG0f,SmMnO3(+/ - 250J) = -131321(+/ - 2000) + 48.02(+/ - 0:35)T / K ΔG0f,SmMn2O5(+/ - 2000 J) = -107085(+/ - 2200) + 69.74(+/ - 1:70)T / K Using obtained results and available literature data, thermodynamic data tables for the two ternary phases have been compiled from 298.15 to 1400 K. Streszczenie polskie: W pracy przedstawiono wyniki badań dotyczące własności termodynamicznych manganinów samaru, wyznaczone metodą pomiaru SEM ogniw ze stałym elektrolitem: MnO + Sm2O3 + SmMnO3 / O-2/ Ni + NiO ogniwo I Mn3O4 + SmMnO3 + SmMn2O5 / O-2 / powietrze ogniwo II oraz określono równowagowe ciśnienie parcjalne tlenu dla reakcji tworzenia SmMnO3 i SmMn2O5 w zakresie temperatur 1173�1450 K: MnO + 1/2Sm2O3 + 1/4O2 = SmMnO3 1/3Mn3O4 + SmMnO3 + 1/3O2 = SmMn2O5 Z tych danych doświadczalnych wyznaczono zależności temperaturowe energii swobodnych tworzenia powyższych manganinów samaru: ΔG0f,SmMnO3(+/ - 250J) = -131321(+/ - 2000) + 48.02(+/ - 0:35)T / K ΔG0f,SmMn2O5(+/ - 2000 J) = -107085(+/ - 2200) + 69.74(+/ - 1:70)T / K W tablicach I i II zamieszczono dane termodynamiczne dla dwóch potrójnych faz otrzymane poprzez kompilacje własnych danych doświadczalnych z danymi literaturowymi.

Generative Maximum Entropy Learning for Multiclass Classification

Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative in nature. In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature. To tackle the curse of dimensionality of large data sets, we employ conditional independence assumption (Naive Bayes) and we perform feature selection simultaneously, by enforcing a `maximum discrimination' between estimated class conditional densities. For two class problems, in the proposed method, we use Jeffreys (J) divergence to discriminate the class conditional densities. To extend our method to the multi-class case, we propose a completely new approach by considering a multi-distribution divergence: we replace Jeffreys divergence by Jensen-Shannon (JS) divergence to discriminate conditional densities of multiple classes. In order to reduce computational complexity, we employ a modified Jensen-Shannon divergence (JS(GM)), based on AM-GM inequality. We show that the resulting divergence is a natural generalization of Jeffreys divergence to a multiple distributions case. As far as the theoretical justifications are concerned we show that when one intends to select the best features in a generative maximum entropy approach, maximum discrimination using J-divergence emerges naturally in binary classification. Performance and comparative study of the proposed algorithms have been demonstrated on large dimensional text and gene expression datasets that show our methods scale up very well with large dimensional datasets.

Phytomedicinal importance of Saraca asoca (Ashoka): an exciting past, an emerging present and a promising future

Since the dawn of civilization, natural resources have remained the mainstay of various remedial approaches of humans vis-a-vis a large number of illnesses. Saraca asoca (Roxb.) de Wilde (Saraca indica L.) belonging to the family Caesalpiniaceae has been regarded as a universal panacea in old Indian Ayurvedic texts and has especially been used to manage gynaecological complications and infections besides treating haemmorhagic dysentery, uterine pain, bacterial infections, skin problems, tumours, worm infestations, cardiac and circulatory problems. Almost all parts of the plant are considered pharmacologically valuable. Extensive folkloric practices and ethnobotanical applications of this plant have even lead to the availability of several commercial S. asoca formulations recommended for different indications though adulteration of these remains a pressing concern. Though a wealth of knowledge on this plant is available in both the classical and modern literature, extensive research on its phytomedicinal worth using state-of-the-art tools and methodologies is lacking. Recent reports on bioprospecting of S. asoca endophytic fungi for industrial bioproducts and useful pharmacologically relevant metabolites provide a silver lining to uncover single molecular bio-effectors from its endophytes. Here, we describe socio-ethnobotanical usage, present the current pharmacological status and discuss potential bottlenecks in harnessing the proclaimed phytomedicinal worth of this prescribed Ayurvedic medicinal plant. Finally, we also look into the possible future of the drug discovery and pharmaceutical R&D efforts directed at exploring its pharma legacy.

A Perspective on Computer Assisted Assessment Techniques for Short Free-Text Answers

Computer Assisted Assessment (CAA) has been existing for several years now. While some forms of CAA do not require sophisticated text understanding (e.g., multiple choice questions), there are also student answers that consist of free text and require analysis of text in the answer. Research towards the latter till date has concentrated on two main sub-tasks: (i) grading of essays, which is done mainly by checking the style, correctness of grammar, and coherence of the essay and (ii) assessment of short free-text answers. In this paper, we present a structured view of relevant research in automated assessment techniques for short free-text answers. We review papers spanning the last 15 years of research with emphasis on recent papers. Our main objectives are two folds. First we present the survey in a structured way by segregating information on dataset, problem formulation, techniques, and evaluation measures. Second we present a discussion on some of the potential future directions in this domain which we hope would be helpful for researchers.