Photoelectrocatalytic humic acid degradation kinetics and effect of pH,applied potential and inorganic ions

This study addresses the removal of humic acid (HA) dissolved in an aqueous medium by a photoelectrocatalytic process. UV₂₅₄ removal and the degradation of color (Vis₄₀₀) followed pseudo‐first order kinetics. Rate constants were 1.1 × 10^?1 min^?1, 8.3 × 10^?2 min^?1 and 2.49 × 10^?2 min^?1 (R² > 0.97) for UV₂₅₄ degradation and 1.7 × 10^?1 min^?1, 6.5 × 10^?2 min^?1 and 2.0 × 10^?2 min^?1 for color removal from 5 mg dm^?3, 10 mg dm^?3 and 25 mg dm^?3 HA respectively. Following a 2 h irradiation time, 96% of the color, 98% of the humic acid and 85% of the total organic carbon (TOC) was removed from an initial 25 mg dm^?3 HA solution in the photoanode cell. Photocatalytic removal on the same photoanode was also studied in order to compare the two methods of degradation. Results showed that the photoelectrocatalytic method was much more effective than the photocatalytic method especially at high pH values and with respect to UV₂₅₄ removal. The effect of other important reaction variables, eg pH, external potential and electrolyte concentration, on the photoelectrocatalytic HA degradation was also studied. Copyright © 2003 Society of Chemical Industry     

A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory

The Journal of Supercomputing - In recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks. The long short-term memory...     

Functional polyamides. 3. Synthesis and chelating properties of a new poly(amide thioether)

Polymerization of β,β′-(ethylenedithio)dipropionitrile

1 IUPAC name: 4,7-dithiadecane dinitrile.

with formaldehyde in the presence of sulfuric acid was carried out in order to permit the synthesis of polymides containing thioether groups. The obtained poly(amide thioether)s were characterized; they coordinate mercury (II) with a high degree of selectivity.     

A modified genetic algorithm for forecasting fuzzy time series

Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques.     

Topology-Aware Multiple Routing Configurations for Fault Tolerant Networking

Real-time services require reliable and fault tolerant communication networks to support their stringent Quality of Service requirements. Multi Topology Routing based IP Fast Re-route (MT-IPFRR) technologies provide seamless forwarding of IP packets during network failures by constructing virtual topologies (VTs) to re-route the disrupted traffic. Multiple Routing Configurations (MRC) is a widely studied MT-IPFRR technique. In this paper, we propose two heuristics, namely mMRC-1 and mMRC-2, to reduce the number of VTs required by the MRC to provide full coverage for single link/node failures, and hence, to decrease its operational complexity. Both heuristics are designed to construct more robust VTs against network partitioning by taking their topological characteristics into consideration. We perform extensive experiments on 3200 topologies with diverse structural properties using our automated topology generation and analysis tool. Numerical results show that the amount of reductions in VT requirements get higher up to 31.84 %, as the networks tend to have more hub nodes whose degree is much higher than the rest of the network.     

The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting

Computational Economics - Looking at the artificial neural networks’ literature, most of the studies started with feedforward artificial neural networks and the training of many feedforward...     

The relation between EA effectiveness and stakeholder satisfaction

Enterprise Architecture (EA) is increasingly being used by large organizations to get a grip on the complexity of their business processes, information systems and technical infrastructure. Although seen as an important instrument to help solve major organizational problems, effectively applying EA seems no easy task. Active participation of EA stakeholders is one of the main critical success factors for EA. This participation depends on the degree in which EA helps stakeholders achieve their individual goals. A highly related topic is effectiveness of EA, the degree in which EA helps to achieve the collective goals of the organization. In this article we present our work regarding EA stakeholder satisfaction and EA effectiveness, and compare these two topics. We found that, regarding EA, the individual goals of stakeholders map quite well onto the collective goals of the organization. In a case study we conducted, we found that the organization is primarily concerned with the final results of EA, while individual stakeholders also worry about the way the architects operate.