Combined single-pass conversion of methane via oxidative coupling and dehydro-aromatization

A single-pass process with the combination of oxidative coupling (OCM) and dehydro-aromatization (MDA) for the direct conversion of methane is carried out. With the assistance of the OCM reaction over the SrO-La2O3/CaO catalyst loaded on top of the catalyst bed, the duration of the dehydro-aromatization reaction catalyzed by a 6Mo/HMCM-49 catalyst shows a significant improvement, and. the initial deactivation rate constant of the overall process revealed about 1.5 x 10(-6) s(-1). Up to 72 h on stream, the yield of aromatics was still maintained at 5.0% with a methane conversion of 9.6%, which is obviously higher than that reported for the conventional MDA process with single catalyst. Upon the TPR results, this wonderful enhancement would be attributed to an in-situ formation of CO2 and H2O through the OCM reaction, which serves as a scavenger for actively removing the coke formed during the MDA reaction via a reverse Boudouard reaction and the water gas reaction as well.

Tactile Sensing for Mechatronics: A State of the Art Survey

Lee M.H. and Nicholls H.R., Tactile Sensing for Mechatronics: A State of the Art Survey, Mechatronics, 9, Jan 1999, pp1-31.

ART: An ontology based tool for the translation of papers into Semantic Web format

To be presented at SIG/ISMB07 ontology workshop: http://bio-ontologies.org.uk/index.php To be published in BMC Bioinformatics. Sponsorship: JISC

From Art to Avant Garde? Television, Formalism and the Arts Documentary in 1960's Britain

Sexton, J. (2008). From Art to Avant Garde? Television, Formalism and the Arts Documentary in 1960's Britain. In L. Mulvey and J. Sexton (Eds.), Experimental British Television (pp.89-105). Manchester: Manchester University Press. RAE2008

KONSTYTUCYJNE PRAWO DO OCHRONY ZDROWIA W ŚWIETLE DERYWACYJNEJ KONCEPCJI WYKŁADNI

W niniejszym artykule autor przedstawił sposób odtworzenia normy prawnej zawierającej prawo do ochrony zdrowia na podstawie przepisu prawnego art. 68 ust. 1 Konstytucji RP. Przełożenie przepisu prawnego na normę prawną odbyło się zgodnie z założeniami derywacyjnej koncepcji wykładni, której istotą jest uzyskanie równoznaczności pomiędzy przepisem a odtworzoną z niego normą. W tym celu konieczne było przeprowadzenie wszystkich trzech faz wykładni: porządkującej, rekonstrukcyjnej i percepcyjnej. Niniejszy artykuł ukazuje, jak przebiegają poszczególne czynności interpretacyjne podejmowane w oparciu o dyrektywy derywacyjnej koncepcji wykładni. Pozwala to zobaczyć w szczegółowy sposób przebieg procesu przekształcania przepisu prawnego w normę prawną. Rezultatem przeprowadzonej wykładni było uzyskanie dostatecznie jednoznacznej normy prawnej. Na tej podstawie wykazano, że prawo do ochrony zdrowia jest zasadą prawa, ponieważ możliwe było odniesienie treści normy prawnej do określonych kryteriów. Ponadto, norma prawna odtworzona na podstawie przepisu konstytucyjnego zawiera wszystkie elementy, przede wszystkim określa adresata i nakazane zachowanie. Pozwala to wskazać sytuacje prawne, jakie wyznacza odtworzona w toku wykładni norma prawna, czyli obowiązek, uprawnienie i prawo podmiotowe. Postrzeganie konstytucyjnego prawa do ochrony zdrowia jako normy prawnej umożliwia także znacznie szersze i głębsze rozpatrzenie aspektów jego obowiązywania. W tym kontekście szczególnie przydatna jest derywacyjna koncepcja wykładni prawa.

The conversion of the Maoris, by Donald MacDougall

http://www.archive.org/details/conversionmaoris00macduoft

Rambo's Interdisciplinary Approach to Religious Conversion: The Case of St. Augustine

This dissertation illustrates the merits of an interdisciplinary approach to religious conversion by employing Lewis Rambo’s systemic stage model to illumine the process of St. Augustine’s conversion. Previous studies of Augustine’s conversion have commonly explored his narrative of transformation from the perspective of one specific discipline, such as theology, history, or psychology. In doing so, they have necessarily restricted attention to a limited set of questions and problems. By bringing these disciplines into a structured, critical conversation, this study demonstrates how formulating and responding to the interplay among personal, social, cultural, and religious dimensions of Augustine’s conversion process may eventuate in the consideration of issues previously unarticulated and thus unaddressed. Rambo (1993) formulates a model of religious change that consists of what he calls context, crisis, quest, encounter, interaction, commitment, and consequences. Change is explained by drawing upon the research and scholarship of psychologists, sociologists, anthropologists, and religionists, in conjunction with the contributions of theologians. This study unfolds in the following chapters: I. Introduction; II. Literature review of scholarship about conversion, with emphasis on explication of Rambo’s model; III. A description of the case of Augustine, drawn from a close reading of the Confessions; IV. Literature review of scholarship about Augustine’s conversion; V. Interdisciplinary interpretation of Augustine’s conversion; and VI. Implications for scholars of conversion, and for pastoral caregivers, as well as recommendations for future research. This dissertation demonstrates how Augustine’s conversion experience was deeply influenced by 1) psychological distress and crisis; 2) the quest to know himself and the divine; 3) interactions with significant others; 4) participation in Christian communities; 5) philosophical and cultural changes; and 6) the encounter with the divine. As such, this study reveals the value of interpreting Augustine’s conversion as an evolving process constituted in multiple factors that can be differentiated from one another, yet clearly interact with one another. It examines the implications of constructing an interdisciplinary approach to Augustine’s conversion narrative for both the academy and the Christian community, and recommends the use of Rambo’s model in studies of other cases of religious change.

Bring ART into the ACT

ACT is compared with a particular type of connectionist model that cannot handle symbols and use non-biological operations that cannot learn in real time. This focus continues an unfortunate trend of straw man "debates" in cognitive science. Adaptive Resonance Theory, or ART, neural models of cognition can handle both symbols and sub-symbolic representations, and meets the Newell criteria at least as well as these models.

Biased ART: A Neural Architecture that Shifts Attention Toward Previously Disregarded Features Following an Incorrect Prediction

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Twodimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/.

On the Relationship Between the Generalized Equality Classifier and ART 2 Neural Networks

In this paper, we introduce the Generalized Equality Classifier (GEC) for use as an unsupervised clustering algorithm in categorizing analog data. GEC is based on a formal definition of inexact equality originally developed for voting in fault tolerant software applications. GEC is defined using a metric space framework. The only parameter in GEC is a scalar threshold which defines the approximate equality of two patterns. Here, we compare the characteristics of GEC to the ART2-A algorithm (Carpenter, Grossberg, and Rosen, 1991). In particular, we show that GEC with the Hamming distance performs the same optimization as ART2. Moreover, GEC has lower computational requirements than AR12 on serial machines.

ART-EMAP: A Neural Network Architecture for Learning and Prediction by Evidence Accumulation

This paper introduces ART-EMAP, a neural architecture that uses spatial and temporal evidence accumulation to extend the capabilities of fuzzy ARTMAP. ART-EMAP combines supervised and unsupervised learning and a medium-term memory process to accomplish stable pattern category recognition in a noisy input environment. The ART-EMAP system features (i) distributed pattern registration at a view category field; (ii) a decision criterion for mapping between view and object categories which can delay categorization of ambiguous objects and trigger an evidence accumulation process when faced with a low confidence prediction; (iii) a process that accumulates evidence at a medium-term memory (MTM) field; and (iv) an unsupervised learning algorithm to fine-tune performance after a limited initial period of supervised network training. ART-EMAP dynamics are illustrated with a benchmark simulation example. Applications include 3-D object recognition from a series of ambiguous 2-D views.

Sequential Memory with ART: A Self-Organizing Network Capable of Learning Sequences of Patterns

A model which extends the adaptive resonance theory model to sequential memory is presented. This new model learns sequences of events and recalls a sequence when presented with parts of the sequence. A sequence can have repeated events and different sequences can share events. The ART model is modified by creating interconnected sublayers within ART's F2 layer. Nodes within F2 learn temporal patterns by forming recency gradients within LTM. Versions of the ART model like ART I, ART 2, and fuzzy ART can be used.

ART-EMAP: A Neural Network Architecture for Object Recognition by Evidence Accumulation

A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and 3-D object recognition from a series of ambiguous 2-D views. The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. A concluding set of simulations demonstrate ART-EMAP performance on a difficult 3-D object recognition problem.

Fuzzy ART

Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP synthesize fuzzy logic and ART networks by exploiting the formal similarity between the computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic: intersection (∩) with the fuzzy intersection (∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric: theory in which the fuzzy inter:>ec:tion and the fuzzy union (∨), or component-wise maximum, play complementary roles. Complement coding preserves individual feature amplitudes while normalizing the input vector, and prevents a potential category proliferation problem. Adaptive weights :otart equal to one and can only decrease in time. A geometric interpretation of fuzzy AHT represents each category as a box that increases in size as weights decrease. A matching criterion controls search, determining how close an input and a learned representation must be for a category to accept the input as a new exemplar. A vigilance parameter (p) sets the matching criterion and determines how finely or coarsely an ART system will partition inputs. High vigilance creates fine categories, represented by small boxes. Learning stops when boxes cover the input space. With fast learning, fixed vigilance, and an arbitrary input set, learning stabilizes after just one presentation of each input. A fast-commit slow-recode option allows rapid learning of rare events yet buffers memories against recoding by noisy inputs. Fuzzy ARTMAP unites two fuzzy ART networks to solve supervised learning and prediction problems. A Minimax Learning Rule controls ARTMAP category structure, conjointly minimizing predictive error and maximizing code compression. Low vigilance maximizes compression but may therefore cause very different inputs to make the same prediction. When this coarse grouping strategy causes a predictive error, an internal match tracking control process increases vigilance just enough to correct the error. ARTMAP automatically constructs a minimal number of recognition categories, or "hidden units," to meet accuracy criteria. An ARTMAP voting strategy improves prediction by training the system several times using different orderings of the input set. Voting assigns confidence estimates to competing predictions given small, noisy, or incomplete training sets. ARPA benchmark simulations illustrate fuzzy ARTMAP dynamics. The chapter also compares fuzzy ARTMAP to Salzberg's Nested Generalized Exemplar (NGE) and to Simpson's Fuzzy Min-Max Classifier (FMMC); and concludes with a summary of ART and ARTMAP applications.