Novel Packet Switching for Green IP Networks

A green technology for reducing energy consumption has become a critical factor in ICT industries. However, for the telecommunications sector in particular, most network elements are not usually optimized for power efficiency. Here, we propose a novel energy‐efficient packet switching method for use in an IP network for reducing unnecessary energy consumption. As a green networking approach, we first classify the network nodes into either header or member nodes. The member nodes then put the routing‐related module at layer 3 to sleep under the assumption that the layer in the OSI model can operate independently. The entire set of network nodes is then partitioned into clusters consisting of one header node and multiple member nodes. Then, only the header node in a cluster conducts IP routing and its member nodes conduct packet switching using a specially designed identifier, a tag. To investigate the impact of the proposed scheme, we conducted a number of simulations using well‐known real network topologies and achieved a more energy‐ efficient performance than that achieved in previous studies.     

An X-ray diffraction and electron spectroscopic study of metallic glass In20Te80

Results of X-ray diffraction and X-ray photoelectron spectroscopy investigations on the crystallization behaviour of an amorphous In₂₀Te₈₀ system and the effects of crystallization on the electronic core levels of In and Te atoms are presented. During controlled heat treatments three crystalline phases, Te, -In₂Te₃, and In₂Te₅-I, were observed in this system. In addition, a few splat-cooled samples were found to exhibit a new metastable crystalline phase. Photoelectron measurements revealed that the Te 3d and 4d core levels of amorphous In₂₀Te₈₀ were shifted downwards in energy from their characteristic values of pure Te metal. The In 3d and 4d levels experienced large energy shifts due to alloying, but remained unaffected by heating at temperatures below 520 K.     

Wastewater treatment: New insight provided by interactive multiobjective optimization

In this paper, we describe a new interactive tool developed for wastewater treatment plant design. The tool is aimed at supporting the designer in designing new wastewater treatment plants as well as optimizing the performance of already available plants. The idea is to utilize interactive multiobjective optimization which enables the designer to consider the design with respect to several conflicting evaluation criteria simultaneously. This is more important than ever because the requirements for wastewater treatment plants are getting tighter and tighter from both environmental and economical reasons. By combining a process simulator to simulate wastewater treatment and an interactive multiobjective optimization software to aid the designer during the design process, we obtain a practically useful tool for decision support. The applicability of our tool is illustrated with a case study related to municipal wastewater treatment where three conflicting evaluation criteria are considered.     

Seasonality: Nature,People's Preferences and Urban Planning in Oulunsalo,Finland

Seasonal changes are particularly strong in northern Europe. Daylight, temperature, flora, fauna and landscape change from day to day. We discuss how seasonality exists in nature, is felt by people and is reflected in urban planning. The case study regards Oulunsalo municipality with a population of 9,000 at the northern most Baltic Sea coast in Finland. In 2003 the mean temperature of the coldest month in Oulunsalo was ?16.4 and the warmest +19.5 centigrade. The longest day was 22 hours 3 minutes and the shortest 3 hours and 35 minutes. A survey among the adult Oulunsalo population shows that summer is the best season for the majority of people (72%)—more often for men (78%) than for women (66%). Almost everybody (96%) likes the change of seasons. Seasons and seasonal preferences should be considered more in urban planning processes and land-use plans.     

Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives

A new bi-objective genetic programming (BioGP) technique has been developed for meta-modeling and applied in a chromatographic separation process using a simulated moving bed (SMB) process. The BioGP technique initially minimizes training error through a single objective optimization procedure and then a trade-off between complexity and accuracy is worked out through a genetic algorithm based bi-objective optimization strategy. A benefit of the BioGP approach is that an expert user or a decision maker (DM) can flexibly select the mathematical operations involved to construct a meta-model of desired complexity or accuracy. It is also designed to combat bloat – a perennial problem in genetic programming along with over fitting and under fitting problems. In this study the meta-models constructed for SMB reactors were compared with those obtained from an evolutionary neural network (EvoNN) developed earlier and also with a polynomial regression model. Both BioGP and EvoNN were compared for subsequent constrained bi-objective optimization studies for the SMB reactor involving four objectives. The results were also compared with the previous work in the literature. The BioGP technique produced acceptable results and is now ready for data-driven modeling and optimization studies at large.     

A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

Computationally expensive multiobjective optimization problems arise, e.g. in many engineering applications, where several conflicting objectives are to be optimized simultaneously while satisfying constraints. In many cases, the lack of explicit mathematical formulas of the objectives and constraints may necessitate conducting computationally expensive and time-consuming experiments and/or simulations. As another challenge, these problems may have either convex or nonconvex or even disconnected Pareto frontier consisting of Pareto optimal solutions. Because of the existence of many such solutions, typically, a decision maker is required to select the most preferred one. In order to deal with the high computational cost, surrogate-based methods are commonly used in the literature. This paper surveys surrogate-based methods proposed in the literature, where the methods are independent of the underlying optimization algorithm and mitigate the computational burden to capture different types of Pareto frontiers. The methods considered are classified, discussed and then compared. These methods are divided into two frameworks: the sequential and the adaptive frameworks. Based on the comparison, we recommend the adaptive framework to tackle the aforementioned challenges.     

Behaviour of oat globulins in lactic acid fermentation of oat bran

Oat bran fermentation (OBF) is used to produce non-dairy yogurt-type products. Such products may be designed being rich in probiotic bacteria and/or dietary-fibre. Oat bran is, however, also rich in proteins, especially 12 S globulins. Understanding the behaviour of globulins in OBF would thus offer a basis for further exploitation of proteins in the product design. The behaviour of oat globulins was monitored during a model OBF in order to study changes in protein solubility and possible protein hydrolysis. Proteins were extracted from OBF samples with a buffered and a non-buffered extraction procedure. The extracts were analyzed with SDS-PAGE and a Lowry assay. Combined effect of pH and NaCl-concentration on the solubility of oat globulin isolate was studied. The solubility of oat globulins decreased during OBF; this appeared as their shift from the salt-soluble fraction to the residual protein fraction. The shift in oat globulin solubility was due to the acidifying conditions present in OBF, which lead to the unfolding of globulins and also apparently induced protein aggregation. No major protein hydrolysis was observed during OBF.     

Cutting thin sheet metal with a water jet guided laser using various cutting distances, feed speeds and angles of incidence

This research deals with the cutting of thin sheet metals at various distances, feed speeds and angles of incidence using a water jet guided laser. In the water jet guided laser process a laser beam is focused into a jet of water, which transmits the beam to the workpiece. This eliminates the need for any focus control. Nevertheless, most of its applications are in planar cutting where this advantage is not utilized. For the laser parameters, jet pressure and diameter in question, the value of 50 mm was found to be a fairly reliable upper limit to the cutting distance for both normal and inclined surfaces. In addition to the laser beam being absorbed partially by the water jet, the jet was found to be susceptible to disturbances. Specimen vibration caused by the water jet also impeded cutting a continuous kerf.     

Consistent mapping of common cause failure rates and alpha factors

The problem addressed is how to combine event experience data from multiple source plants to estimate common cause failure (CCF) rates for a target plant. Alternative models are considered for transforming CCF parameters from systems with different numbers of similar components to obtain CCF-rates for a specific group of components. Two sets of rules are reviewed and compared for transforming rates and assessment uncertainties from larger to smaller systems, i.e. mapping down. Mapping down equations are presented also for the alpha-factors and for the variances of CCF rates. Consistent rules are developed for mapping up CCF-rates and uncertainties from smaller to larger systems. These mapping up rules are not limited to a binomial CCF model. It is shown how consistency requirements set certain limits to possible parametric values. Empirical alpha factors are used to estimate robust mapping parameters, and mapping up equations are derived for alpha factors as well. An assessment uncertainty procedure is presented for treating incomplete or vague information when estimating CCF-rates. Numerical studies illustrate mapping rules and procedures. Recommendations are made for practical applications.     

Efficient current reuse for low-power transceivers

Current boosting is a method where the performance of an active circuit block is optimized by placing a constant current source in parallel with the active signal path to provide optimal biasing for different components. In this paper, a technique to replace the constant dc current source with active building blocks typically required in transceivers is proposed. By using this method the total current consumption of the transceiver can be efficiently reduced without modifying its performance. A design example where the proposed technique reduces the receiver current consumption by 45% is given.