Formation of polypyrrole coatings onto low carbon steel by electrochemical process

Thin polypyrrole coatings (∼ 10 μm thick) were formed on low carbon steel by an aqueous constant current electrochemical polymerization using oxalic acid as the electrolyte. The amount of polypyrrole coatings formed on steel increased with the applied current and monomer concentration. No significant change in the electropolymerization of pyrrole occurred as a result of increased electrolyte concentration. The induction time for electropolymerization decreased significantly with current density but was unaffected by the initial monomer and electrolyte concentration. The electropolymerization potential of pyrrole increased with increased current density (Cd), i.e., Ep = 0.62 + 0.41 [Cd], and decreased exponentially with increased monomer and electrolyte concentration, Ep = E₀ exp^-[M]. Scanning electron microscopy (SEM) showed that the microstructure of the polypyrrole coatings formed on steel was dependent on the current density to the extent that smoother and more uniform coatings are formed at low current density. © 1997 John Wiley & Sons, Inc. J Appl Polm Sci 65:417–424, 1997     

Combining Symbolic and Neural Learning

Conclusion Connectionist machine learning has proven to be a fruitful approach, and it makes sense to investigate systems that combine the strengths of the symbolic and connectionist approaches to AI. Over the past few years, researchers have successfully developed a number of such systems. This article summarizes one view of this endeavor, a framework that encompasses the approaches of several different research groups. This framework (see Figure 1) views the combination of symbolic and neural learning as a three-stage process: (1) the insertion of symbolic information into a neural network, thereby (partially) determining the topology and initial weight settings of a network, (2) the refinement of this network using a numeric optimization method such as backpropagation, possibly under the guidance of symbolic knowledge, and (3) the extraction of symbolic rules that accurately represent the knowledge contained in a trained network. These three components form an appealing, complete picture—approximately-correct symbolic information in, more-accurate symbolic information out—however, these three stages can be independently studied. In conclusion, the research summarized in this paper demonstrates that combining symbolic and connectionist methods is a promising approach to machine learning.     

Potential due to some arbitrarily charged strips between parallel earthed planes

The potential problem in the infinite region $\text{D = {(x, y)¦?∞ < x < ∞, 0 < y < c}}$ is considered for the case of the mixed Dirichlet-Neumann boundary conditions. The problem is formulated as a Volterra modified problem for a complex function and the solution of this last problem is written explicitly. In the case of the constant boundary data (i.e. constant potentials of the strips) the solution has been written in a simpler form.     

Attachment and representations of peer relationships.

Three studies explored the connection between attachment and peer-related representations. Children heard stories in which a peer with ambiguous intent caused a negative event. Study 1 examined three aspects of peer-related representations in 3.5-yr-olds: representations of (1) peer intent, (2) behavioral responses to the event, and (3) peer feelings. Children's representations of the mother's response to the event were also examined. Study 2 examined the connection between attachment and the same aspects of peer-related representations in kindergarten and 1st-grade children. The proposition implicit in attachment theory that it is children's representations of peer relationships that in part account for the connection between child–parent attachment and relations with peers was also tested. Study 3 focused on representations of peer intent in connection with self-reported maternal and paternal rejection in 5th graders. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Cognitive activity and suggestions for analgesia in the reduction of reported pain.

Assessed 38 undergraduates' pain magnitude and pain tolerance for arm immersion in ice water during a baseline and posttest session. Before the posttest, half the Ss received an analgesia suggestion. On the basis of their written testimony, Ss were classified as having either predominately coped (e.g., imagined event inconsistent with pain or made positive self-statements) or predominantly exaggerated (e.g., worried about and exaggerated the noxious aspects of the situation) during each immersion. On both immersions, copers reported less pain and exhibited higher pain tolerance than exaggerators. Moreover, the suggestion was associated with reductions in reported pain only when it transformed baseline exaggerators into posttest copers. (28 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Saponification Procedure for the Determination of Some Chlorinated Hydrocarbons in Fish

A rapid method is described for the separation and analysis of chlorinated hydrocarbon residues in Great Lakes fishes. The method involves saponification with alcoholic KOH and chromatography on activated silica gel column with petroleum ether and diethyl ether elutions. Final analysis is achieved by gas chromatography equipped with electron-capture detection system. The recovery of the chlorinated hydrocarbon residues, using the silica gel clean-up, ranged from 82 to 94% with an average standard deviation of ± 0.8. Analysis by this method of some chlorinated hydrocarbons in Lake Huron fish gave values ranging from 0.130 to 1.89 ppm for PCB, 0.018 to 0.76 ppm for DDT and metabolites, and 0.002 to 0.040 ppm for aldrin.     

Investigation on crystallinity and optical properties of l-tartaric acid single crystal at dynamic shocked conditions

Journal of Materials Science: Materials in Electronics - In this article, the authors demonstrate the experimental findings of the effect of dynamic shock waves on the crystallographic structural...     

A System for Building Intelligent Agents that Learn to Retrieve and Extract Information

We present a system for rapidly and easily building instructable and self-adaptive software agents that retrieve and extract information. Our Wisconsin Adaptive Web Assistant (WAWA) constructs intelligent agents by accepting user preferences in the form of instructions. These user-provided instructions are compiled into neural networks that are responsible for the adaptive capabilities of an intelligent agent. The agent’s neural networks are modified via user-provided and system-constructed training examples. Users can create training examples by rating Web pages (or documents), but more importantly WAWA’s agents uses techniques from reinforcement learning to internally create their own examples. Users can also provide additional instruction throughout the life of an agent. Our experimental evaluations on a ‘home-page finder’ agent and a ‘seminar-announcement extractor’ agent illustrate the value of using instructable and adaptive agents for retrieving and extracting information.     

Spatio‐temporal variations in the trophic status of Lake Naivasha,Kenya

The spatio‐temporal dynamics of the trophic state of a lake are crucial in defining its water quality, as well as biodiversity. Accordingly, this study focused on the spatio‐temporal variations of the trophic state, and the possible causes of the heterogeneous turbidity in Lake Naivasha, Kenya. The trophic state of the lake oscillated between a eutrophic and hypereutrophic condition, being found to be more eutrophic than reported in previous studies, indicating a progressive deterioration of its water quality. Inferences from the graphical representation of the deviations of total phosphorus and Secchi depth from the chlorophyll‐a trophic state indices revealed that the lake is predominantly phosphorus limited. Furthermore, the turbidity in the northern part of the lake is dominated by suspended sediment and dissolved coloured material. Discriminant analysis resulted in identification of three distinct trophic state regions in Lake Naivasha, namely the northern region, the mid and southern part and the more or less isolated Crescent Lake. The results of this study provide a good basis for further investigation of the current loading magnitude of both nutrients and sediments, in order to facilitate sustainable management to ensure community integrity and ecosystem functions of the lake.