Multichromic conducting copolymer of 1-benzyl-2,5-di(thiophen-2-yl)-1H-pyrrole with EDOT

Despite the significant progress made in the field of electrochromic polymers, the multichromic facility of current knowledge is restricted. Therefore, as previously proven, electrochemical copolymerization of 1-benzyl-2,5-di(thiophen-2-yl)-1H-pyrrole (SNBS) and 3,4-ethylenedioxythiophene (EDOT) was used as a strategy to achieve desired multichromic properties, where the resultant copolymer displayed distinct color changes between claret red, yellow, green, and blue colors with short switching times and high optical contrast. As an application, absorption/transmission type electrochromic device with indium tin oxide (ITO)/copolymer/gel electrolyte PEDOT/ITO configuration was constructed, where copolymer and PEDOT functioned as the anodically and the cathodically coloring layers, respectively. Results implied the successive use of this copolymer in electrochromic device applications, since the device exhibited short switching times with a wide color variation upon applied potential.     

A Unique Processable Green Polymer with a Transmissive Oxidized State for Realization of Potential RGB‐Based Electrochromic Device Applications

Realization of commercial RGB‐based polymer electrochromic‐device applications can only be achieved by processable materials that possess three complementary colors in the reduced state and are transparent in the oxidized state. This report highlights the synthesis of the first processable green polymer with a transmissive oxidized state. The polymer revealed superior optical contrast in the visible region with fast switching times and robust stability. Hence, this material is the outstanding candidate for completion of RGB color space through commercial polymeric electrochromics.     

Queries = examples + counterexamples

Database queries can be expressed as unordered collections of examples and counterexamples. The resulting languages are intuitive, first order complete, and applicable to a variety of data models including relational, object oriented, and visual models. This class of languages based on examples and counterexamples employ a minimal number of constructs with no sequencing or nesting of the constructs implied, allowing a simple and uniform theoretical analysis of their syntax, semantics, and completeness.     

Modeling the pathways linking childhood hyperactivity and substance use disorder in young adulthood.

This study modeled direct and mediated pathways linking childhood hyperactivity and substance use disorder (SUD). Boys (n = 112) were administered the revised Drug Use Screening Inventory at age 12-14 years and the Structured Clinical Interview for DSM-IV at age 22 years. Six newly derived scales having established heritability were conceptually organized into internalizing and externalizing pathways to SUD emanating from childhood hyperactivity. Hyperactivity directly predicts SUD. Neuroticism, conduct problems, and their respective manifestations of social withdrawal and school problems mediated the association between hyperactivity and SUD. Hyperactivity also predicted neuroticism that, in turn, predicted low self-esteem leading to social withdrawal and SUD. These results indicate that hyperactivity is a diathesis for both internalizing and externalizing disturbances that, in turn, portend differential expression of psychosocial maladjustment presaging SUD. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Characterization of the polymer of a dipyrrolyl monomer by pyrolysis mass spectrometry

In this work, characterization of a homopolymer of succinic acid bis(4‐pyrrol‐1‐ylphenyl) ester prepared by galvonastatic polymerization was carried out by direct pyrolysis mass spectrometry. Although decomposition of the monomer yielding mainly butadionic acid and pyrrole occurred under the galvonastatic polymerization conditions, growth of the polymer through the pyrrole moieties was also achieved, yielding a ladder‐type polymer film. The polypyrrole chains contained both quinoid and aromatic units as in the case of polypyrrole, yet the extent of network structure was significantly diminished. A three‐step mechanism is proposed for the thermal decomposition process. The first step involves the cleavage of C₄H₄NC₆H₄O end groups. In the second step, decomposition of phenyl ester units and polypyrrole chains having quinoid structure takes place. The final stage of thermal degradation was attributed to decomposition of polypyrrole chains having aromatic structure. Copyright © 2004 Society of Chemical Industry     

Nested set languages for functional databases

A class of languages based on nested sets are introduced in the context of functional databases. The major linguistic characteristics of each are identified and compared with others. The significance of the functional model and the nested set languages are demonstrated through examples.     

A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier

With increasing Internet connectivity and traffic volume, recent intrusion incidents have reemphasized the importance of network intrusion detection systems for combating increasingly sophisticated network attacks. Techniques such as pattern recognition and the data mining of network events are often used by intrusion detection systems to classify the network events as either normal events or attack events. Our research study claims that the Hidden Naïve Bayes (HNB) model can be applied to intrusion detection problems that suffer from dimensionality, highly correlated features and high network data stream volumes. HNB is a data mining model that relaxes the Naïve Bayes method’s conditional independence assumption. Our experimental results show that the HNB model exhibits a superior overall performance in terms of accuracy, error rate and misclassification cost compared with the traditional Naïve Bayes model, leading extended Naïve Bayes models and the Knowledge Discovery and Data Mining (KDD) Cup 1999 winner. Our model performed better than other leading state-of-the art models, such as SVM, in predictive accuracy. The results also indicate that our model significantly improves the accuracy of detecting denial-of-services (DoS) attacks.     

Analytical Network Process for logistics management: A case study in a small electronic appliances manufacturer

The purpose of this study is to present an application of Analytical Network Process (ANP) for determination of best logistics partnership strategy of a small electronic appliances manufacturer operating in Turkey. The model includes both qualitative and quantitative factors which have been determined by literature review and the expertise of the company managers. Three different logistics management alternatives, “In-house Logistics”, “Third Party Arrangements”, and “Strategic Alliance”, are evaluated by an ANP model. Pairwise comparisons have been obtained through a survey conducted with the company personnel and sector managers. The results provide a decision support for the company courting strategies for logistics management under the aspects of “Law and Regulations”, “Corporate Structure”, and “Economical Factors”; given in “Customer”, “Finance” and “Technology” Dimensions.     

Neural network-based symbol recognition using a few labeled samples

The recognition of pen-based visual patterns such as sketched symbols is amenable to supervised machine learning models such as neural networks. However, a sizable, labeled training corpus is often required to learn the high variations of freehand sketches. To circumvent the costs associated with creating a large training corpus, improve the recognition accuracy with only a limited amount of training samples and accelerate the development of sketch recognition system for novel sketch domains, we present a neural network training protocol that consists of three steps. First, a large pool of unlabeled, synthetic samples are generated from a small set of existing, labeled training samples. Then, a Deep Belief Network (DBN) is pre-trained with those synthetic, unlabeled samples. Finally, the pre-trained DBN is fine-tuned using the limited amount of labeled samples for classification. The training protocol is evaluated against supervised baseline approaches such as the nearest neighbor classifier and the neural network classifier. The benchmark data sets used are partitioned such that there are only a few labeled samples for training, yet a large number of labeled test cases featuring rich variations. Results suggest that our training protocol leads to a significant error reduction compared to the baseline approaches.