Дослідження властивостей композитних адсорбційних матеріалів «силікагель – кристалогідрат» для теплоакумулюючих пристроїв

Heat energy storage is one of the most common technical solutions in the conditions of operation of low-potential and renewable energy sources. Adsorption heat energy storage devices based on the composite media “silica gel – salt” are the most effective in these conditions. The technique and technology of sol-gel synthesis of the composite adsorption materials “silica gel – sodium sulfate” and “silica gel – sodium acetate” have been developed. A special feature of this technique is a two-stage process involving the formation of silicon phase nuclei in the interaction of aqueous solutions of silicate glass and sulphuric or acetic acids in the presence of a polymeric quaternary ammonium salt and subsequent coarsening of the particles with the gradual addition of solutions of silicate glass and the corresponding acids. The essence of the technology consists in successive stages of formation and integration of the silicic phase nuclei, hydrolysis of functional OH- groups, filtration and drying of the fine precipitate. A qualitative difference in the adsorption properties of the synthesized composites and the mechanical mixture of salt – silica gel with sorption capacity inferior to them on average by 30% is revealed by differential thermal analysis. The processes of application of the composite adsorption materials “silica gel – sodium sulfate” and “silica gel – sodium acetate” obtained by the sol-gel method have been studied. A qualitative difference in the kinetics of adsorption of water by the composite adsorbents is shown as compared to massive salts. It is established that the amount of heat of adsorption of water vapor by the composite adsorbents of the materials “silica gel – sodium sulfate” and “silica gel – sodium acetate” is approximately 30 % greater than the linear superposition of salt and silica gel.Изучены процессы применения композитных адсорбционных материалов «силикагель – сульфат натрия» и «силикагель – ацетат натрия», полученных золь – гель методом. С помощью дифференциально-термического анализа выявлено качественное отличие адсорбционных свойств синтезированных композитов и механической смеси соль – силикагель, адсорбционная емкость которой уступает им в среднем на 30 %. Установлено, что теплоты адсорбции водяного пара композитными адсорбентами материалов «силикагель – натрий сульфат» и «силикагель – натрий ацетат» примерно на 30% больше, чем линейная суперпозиция соли и силикагеляВивчено процеси застосування композитних адсорбційних матеріалів «силікагель – натрій сульфат» та «силікагель – натрій ацетат», отриманих золь – гель методом. За допомогою диференційно-термічного аналізу виявлено якісну відмінність адсорбційних властивостей синтезованих композитів та механічної суміші сіль – силікагель, сорбційна ємність якої поступається їм в середньому на 30 %. Встановлено, що теплоти адсорбції водяної пари композитними адсорбентами матеріалів «силікагель – натрій сульфат» та «силікагель – натрій ацетат» близько на 30 % більше, ніж лінійна суперпозиція солі та силікагел

Algorithms for Temporal Information Processing of Text and their Applications (Algoritmen voor het verwerken van temporele informatie in tekst en hun toepassingen)

Temporal information processing of text is a complex information extractiontask in which temporally relevant information in text has to be extracted andproperly represented in order to be used by a machine. In general the temporalinformation processing task regards the major concepts of temporal cognitionsuch as time, events, and relations between events and times when they areencoded in language.This thesis explores the algorithms for temporal information processing of textand focuses on the automated extraction of temporal information. Three majortemporal concepts in language are identified: time expressions - expressionsin text that denote time, temporal events - events that happen or last intime, and temporal relations between events and times. With respect tothis distinction temporal information processing of text can be divided into anumber of corresponding sub-tasks, such as recognition and normalization oftime expressions, recognition of events, and recognition of temporal relationsbetween events and times. In this thesis we describe approaches for automatedrecognition and representation of times, events, and recognition of temporalrelations performed by means of computer algorithms. The proposed algorithmsare based on supervised statistical machine learning methods that sometimesare accompanied by symbolic rule-based approaches.In detail, the thesis contributes (i) the supervised learning algorithms fortemporal expression recognition in text with sparse training data and abootstrapping approach that addresses the sparsity problem, (ii) the novelparadigm for modularized normalization of temporal expressions based on a deepsemantic analysis of temporal expression constituents, (iii) the novel annotationparadigm of temporal information that aims at a full and coherent set ofannotated temporal relations, but does not require annotating an exhaustive setof temporal relations, and (iv) the novel algorithms for the temporal documentstructure recognition composed of temporal events and temporal relations whichis an important step towards automated story understanding.status: publishe

Magnetism of f-electron ternary compounds and their hydrides.

Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi

Comparing two approaches for the recognition of temporal expressions

Temporal expressions are important structures in natural language. In order to understand text, temporal expressions have to be extracted and normalized. In this paper we present and compare two approaches for the automatic recognition of temporal expressions, based on a supervised machine learning approach and trained on TimeBank. The first approach performs a token-by-token classification and the second one does a binary constituent-based classification of chunk phrases. Our experiments demonstrate that on the TimeBank corpus constituent-based classification performs better than the token-based one. It achieves F1-measure values of 0.852 for the detection task and 0.828 when an exact match is required, which is better than the state-of-the-art results for temporal expression recognition on TimeBank.status: publishe

KUL: recognition and normalization of temporal expressions

In this paper we describe a system for the recognition and normalization of temporal expressions in written text (Task 13: TempEval-2, Task A). The recognition task is approached as a classification problem of sentence constituents and the normalization is implemented in a rule-based manner. One of the system features is extending positive annotations in the corpus by semantically similar words automatically obtained from a large unannotated textual corpus. The best results obtained by the system are 0.85 and 0.84 for precision and recall respectively for recognition of temporal expressions.status: publishe

Towards animated visualization of actors and actions in a learning environment

status: publishe

KUL: A data-driven approach to temporal parsing of documents

This paper describes a system for temporal processing of text, which participated in the Temporal Evaluations 2013 campaign. The system employs a number of machine learning classifiers to perform the core tasks of: identification of time expressions and events, recognition of their attributes, and estimation of temporal links between recognized events and times. The central feature of the proposed system is temporal parsing – an approach which identifies temporal relation arguments (event-event and event-timex pairs) and the semantic label of the relation as a single decision.status: publishe

Meeting TempEval-2: shallow approach for temporal tagger

Temporal expressions are one of the important structures in natural language. In order to understand text, temporal expressions have to be identified and normalized by providing ISO-based values. In this paper we present a shallow approach for automatic recognition of temporal expressions based on a supervised machine learning approach trained on an annotated corpus for temporal information, namely TimeBank. Our experiments demonstrate a performance level comparable to a rule-based implementation and achieve the scores of 0.872, 0.836 and 0.852 for precision, recall and F1-measure for the detection task respectively, and 0.866, 0.796, 0.828 when an exact match is required.status: publishe

Machine learning approaches for temporal information extraction: a comparative study

Temporal expressions are important structures in natural language. In order to understand text, temporal expressions have to be extracted and normalized to ISO-based values. For these purposes rule-based and machine learning techniques were proposed. In this paper we present and compare two approaches for automatic recognition of temporal expressions in free text, based on a supervised machine learning approach and trained on an annotated corpus for temporal information, namely TimeBank. The first approach performs a token-by-token classification following B-I-O encoding. The second one does a binary constituent-based classification of chunk phrases. Our experiments demonstrate that on the TimeBank corpus the constituent-based classification performs better than the token-based one. It achieves F1-measure values of 0.852 for the detection task and 0.828 when an exact match is required, which is better than the state-of-the-art results for temporal expression detection on TimeBank.status: publishe

A survey on question answering technology from an information retrieval perspective

This article provides a comprehensive and comparative overview of question answering technology. It presents the question answering task from an information retrieval perspective and emphasises the importance of retrieval models, i.e., representations of queries and information documents, and retrieval functions which are used for estimating the relevance between a query and an answer candidate. The survey suggests a general question answering architecture that steadily increases the complexity of the representation level of questions and information objects. On the one hand, natural language queries are reduced to keyword-based searches, on the other hand, knowledge bases are queried with structured or logical queries obtained from the natural language questions, and answers are obtained through reasoning. We discuss different levels of processing yielding bag-of-words-based and more complex representations integrating part-of-speech tags, classification of the expected answer type, semantic roles, discourse analysis, translation into a SQL-like language and logical representations.status: publishe