Phase local approximation (PhaseLa) technique for phase unwrap from noisy data.

The local polynomial approximation (LPA) is a nonparametric regression technique with pointwise estimation in a sliding window. We apply the LPA of the argument of cos and sin in order to estimate the absolute phase from noisy wrapped phase data. Using the intersection of confidence interval (HCI) algorithm, the window size is selected as adaptive pointwise varying. This adaptation gives the phase estimate with the accuracy close to optimal in the mean squared sense. For calculations, we use a Gauss-Newton recursive procedure initiated by the phase estimates obtained for the neighboring points. It enables tracking properties of the algorithm and its ability to go beyond the principal interval [-pi, pi] and to reconstruct the absolute phase from wrapped phase observations even when the magnitude of the phase difference takes quite large values. The algorithm demonstrates a very good accuracy of the phase reconstruction which on many occasion overcomes the accuracy of the state-of-the-art algorithms developed for noisy phase unwrap. The theoretical analysis produced for the accuracy of the pointwise estimates is used for justification of the HCI adaptation algorithm.     

The Lambda Library: unnamed functions in C++

The Lambda Library (LL) adds a form of lambda functions to C++, which are common in functional programming languages. The LL is implemented as a template library using standard C++; thus no language extensions or preprocessing is required. The LL consists of a rich set of tools for defining unnamed functions. In particular these unnamed functions work seamlessly with the generic algorithms in the C++ Standard Library. The LL offers significant improvements, in terms of generality and ease of use, compared to the current tools in the C++ Standard Library. Copyright © 2003 John Wiley & Sons, Ltd.     

Potential of microbial growth control in water hydraulic systems by UV‐irradiation and filtration

Water hydraulic systems use water as a pressure medium and, thus, do not pose such adverse environmental impacts as oil hydraulics. Microbial deterioration of the pressure medium and biofouling of the surfaces restrict the applicability of the water‐based technology. The potential of microbial growth control by UV‐irradiation and filtration was studied in a pilot‐scale water hydraulic system. The UV‐irradiation (25 m Ws cm^?2) of the pressure medium reduced the total viable counts of bacteria by 1–2 log₁₀ cfu cm^?3, whereas the total microbial cell numbers and the numbers of surface‐attached microorganisms remained unaffected. Prefiltration (1.2 µm, absolute) of the pressure medium decreased the total microbial cell number in the water phase and retarded the attachment of bacteria. The filtration during the operation (2 µm, absolute) decreased the total numbers of microbial cells and the total viable counts in the pressure medium, and microbial attachment on the surfaces. Microbial attachment was not prevented by filtration. The microbial water quality obtained by pre‐ and on‐line filtration of the pressure medium was sufficient to ensure the long‐term operation of the water hydraulic system assuming that clean work practices are complied with in assembly and during the operation. © 2002 Society of Chemical Industry     

Fault tolerant machine learning for nanoscale cognitive radio

We introduce a machine learning-based classifier that identifies free radio channels for cognitive radio. The architecture is designed for nanoscale implementation, under nanoscale implementation constraints; we do not describe all physical details but believe future physical implementation to be feasible. The system uses analog computation and consists of cyclostationary feature extraction and a radial basis function network for classification. We describe a model for nanoscale faults in the system, and simulate experimental performance and fault tolerance in recognizing WLAN signals, under different levels of noise and computational errors. The system performs well under expected non-ideal manufacturing and operating conditions.     

Complexity of the consistency problem for certain Post classes

The complexity of the consistency problem for several important classes of Boolean functions is analyzed. The classes of functions under investigation are those which are closed under function composition or superposition. Several of these so-called Post classes are considered within the context of machine learning with an application to breast cancer diagnosis. The considered Post classes furnish a user-selectable measure of reliability. It is shown that for realistic situations which may arise in practice, the consistency problem for these classes of functions is polynomial-time solvable.     

Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions. Applications of artificial neural network

Artificial neural networks (ANNs) for predicting critical heat flux (CHF) under low pressure and oscillation conditions have been trained successfully for either natural circulation or forced circulation (FC) in the present study. The input parameters of the ANN are pressure, mean mass flow rate, relative amplitude, inlet subcooling, oscillation period and the ratio of the heated length to the diameter of the tube, L/D. The output is a nondimensionalized factor F, which expresses the relative CHF under oscillation conditions. Based on the trained ANN, the influences of principal parameters on F for FC were analyzed. The parametric trends of the CHF under oscillation obtained by the trained ANN are as follows: the effects of pressure below 500 kPa are complex due to the influence of other parameters. F will increase with increasing mean mass flow rate under any conditions, and will decrease generally with an increase in relative amplitude. F will decrease initially and then increase with increasing inlet subcooling. The influence curves of mean mass flow rate on F will be almost the same when the period is shorter than 5.0 s or longer than 15 s. The influence of L/D will be negligible if L/D>200. It is found that the minimum number of neurons in the hidden layer is a product of the number of neurons in the input layer and in the output layer.     

Changes in soil carbon stock after cropland conversion to grassland in Russian temperate zone: measurements versus model simulation

The collapse of Soviet Union in early 1990s led to abandonment of large area of arable land which is assumed to act as a carbon (C) sink. We studied the ability of two dynamic soil C models (Yasso07 and RothC) to predict changes in soil C content after cropland abandonment. The performance of the models was compared using the results of a long-term experiment in Pushchino, Moscow region (54°50′N, 37°35′E) in Russia. The experiment was divided in four combinations of fertilizer or mowing treatments on former cropland soil. The soil C content was determined in the year of establishment (1980) and thereafter in 1999 and 2004. The soil C stocks increased by about 1.5- to 1.8-fold during the study period. Both models predicted the overall change in soil C relatively well (modelling efficiency of Yasso07 and RothC were 0.60 and 0.73, respectively). According to the models, the soil gained on average 140–150 g C m^?2 year^?1 during the first 5 years after conversion of cropland to grassland. The C sequestration rate decreased to 40–50 g C m^?2 year^?1 after 20 years of land use change. The sequestration rates estimated in this study are comparable to the rates observed in other studies.     

Value creation dynamics in a project alliance

Project alliance requires all parties to work together in good faith, share project risks, and make unanimous decisions for the betterment of the project. A key feature of successful implementation of a project alliance is a focus on value creation and value for money. This paper proposes a qualitative system dynamics model to specify and explain dynamics of value creation processes in the context of project alliance. By synthesizing the existing literature and reports on project alliancing, this paper identifies four processes that have a strong influence on the value created in the project alliance context: work progression, rework, redesign and innovation, and rescheduling. In addition, we show how these value creation processes are interrelated and evolve over time. The effectiveness of these processes is influenced by the capability and motivation of the project alliance partners to discover works that do not fully utilize the available resources, and make quick decisions to capture these benefits.     

Optimization of the metakaolin geopolymer preparation for maximized ammonium adsorption capacity

Geopolymers are functional materials that can be used in various environmental applications such as adsorbents in pollutant removal from wastewaters. Metakaolin geopolymer (MK-GP) has been proven to be especially suitable for ammonium (NH₄ ⁺) removal. In this research, the optimal reagent and raw material ratios in the preparation of MK-GP in terms of NH₄ ⁺ adsorption capacity were investigated. The response surface methodology based on the face-centered central composite design was used to optimize the levels of three factors: the amounts of hydroxide, silicate, and metakaolin. In addition, the effect of Na or K as the charge-balancing cation was studied. Empirical models were fitted to the experimental data using multiple linear regression. The significance of the models was confirmed by means of analysis of variance. Optimal NH₄ ⁺ removal efficiency was achieved when the amounts of hydroxide and silicate were maximized, the amount of metakaolin was minimized, and Na-based reagents were used. These trends are most likely a result of optimized conversion of metakaolin into MK-GP.