Visualization and quantification of liquid water transport in softwood by means of neutron radiography

Liquid water uptake in an orthotropic, cellular, hierarchical and natural material namely wood is investigated using neutron radiography. During water uptake in wood, liquid does not move up as a regular front as uptake rates differ in latewood and earlywood. In addition, moisture is adsorbed by the cell wall, resulting in a swelling that influences the process of moisture transport in wood. The high sensitivity of neutron to hydrogen atoms enables an accurate determination of the change in moisture content in the wood at the growth ring scale. The analysis of the spatial and temporal change of water content distribution shows that liquid water transport has different characteristics, depending on the direction of uptake and initial moisture content state.Our results show that latewood cells play a more significant role in water uptake than earlywood cells and that ray tracheids also contribute to liquid transport. Latewood tracheids possess smaller cell lumens than earlywood cells that make them the preferential pathways for transport along the longitudinal direction. The process of liquid uptake is different in the radial and tangential directions as the path of the liquid is more intricate, involving also the rays and requiring more often traversing pits. In tangential direction, water uptake is occurring first in the latewood with a subsequent radial redistribution towards the earlywood. In radial direction, the growth ring boundary decreases the liquid transport rate, an indication that a significant portion of the rays are interrupted at that location. The moisture uptake rate in initially moist specimens is seen to be higher. Liquid transport leads to sorption and thus swelling of the specimens, which was dealt with by affine registration. Water uptake in wood cellular structure is a three-dimensional process that is controlled by the morphologic and sorption properties of the material at its different scales.     

Composition,antimicrobial, antiradical and spasmolytic activity of Ferula heuffelii Griseb. ex Heuffel (Apiaceae) essential oil

The essential oil from underground parts of Ferula heuffelii from N.E. Serbia, was analysed using GC and GC–MS. The main compounds of the essential oil were elemicin (35.4%) and myristicin (20.6%). The essential oil exhibited the best antimicrobial activity against two strains of Candida albicans (MIC = 7.0 and 13.7 μg/ml), as well as against Micrococcus luteus (MIC = 13.7 μg/ml), Staphylococcus epidermidis (MIC = 17.6 μg/ml), Bacillus subtilis (MIC = 21.1 μg/ml) and Micrococcus flavus (MIC = 28.2 μg/ml). In the DPPH radical scavenging assay, essential oil showed substantial activity with SC₅₀ = 22.43 μl/ml. The essential oil was also tested for antispasmodic activity. It inhibited spontaneous contraction of isolated rat ileum dose-dependently, and at the concentration of 86.64 μg/ml exhibited 50% of the maximum effect of atropine. After incubation with 75.00 μg/ml of essential oil, acetylcholine did not induce contractions of ileum, and at 250.00 μg/ml, the essential oil almost completely abolished the spasmodic effect of potassium chloride (80 mM).     

Toward High Thermoelectric Performance of Thiophene and Ethylenedioxythiophene (EDOT) Molecular Wires

The design of thermoelectric materials for the efficient conversion of waste heat into electricity requires simultaneous tuning of their electrical and thermal conductance. A comparative theoretical study of electron and phonon transport in thiophene and ethylenedioxythiophene (EDOT) based molecular wires is performed. It is shown that modifying thiophene by substituting ethylenedioxy enhances the thermoelectric figure of merit ZT for molecules of the same length. Furthermore, it is demonstrated that the electrical conductance of EDOT‐based wires decays more slowly with length than that of thiophene‐based wires and that their thermal conductance is lower. The room‐temperature ZT of undoped EDOT is found to be rather low. However, doping of EDOT by the electron acceptor tolunenesulfunate increases the Seebeck coefficient and electrical conductance, while decreasing the thermal conductance, leading to a thermoelectric figure of merit as high as ZT = 2.4.     

Determination of flow stress properties of machinable materials with help of simple compression and orthogonal machining test

This paper describes a possible method for predicting values of orthogonal metal cutting properties such as shear angle, cutting force etc., on a basis of the well known Hollomon equation, using a simple compression test in order to avoid any cutting experiments. There are two possibilities: the flow stress properties can be obtained from an independent material test; or by measuring the active and passive cutting forces from the orthogonal machining test itself. This paper is concerned with a material flow stress equation, including the effects of strain (ε), strain rate ( ) and temperature (T), which is one of the five equations that have to be solved in simulation analysis with the finite element method. In finding a solution for those five equations, it is necessary to dispose of flow stress properties by rearrangement of the Hollomon equation and so making it usable for cutting process investigation. The rearrangement is described in this paper.     

AN UNSUPERVISED INCREMENTAL LEARNING ALGORITHM FOR DOMAIN-SPECIFIC LANGUAGE DEVELOPMENT

While grammar inference (or grammar induction) has found extensive application in the areas of robotics, computational biology, and speech recognition, its application to problems in programming language and software engineering domains has been limited. We have found a new application area for grammar inference which intends to make domain-specific language development easier for domain experts not well versed in programming language design, and finds a second application in construction of renovation tools for legacy software systems. As a continuation of our previous efforts to infer context-free grammars (CFGs) for domain-specific languages which previously involved a genetic-programming based CFG inference system, we discuss extensions to the inference capabilities of GenInc, an incremental learning algorithm for inferring CFGs. We show that these extensions enable GenInc to infer more comprehensive grammars, discuss the results of applying GenInc to various domain-specific languages and evaluate the results using a comprehensive suite of grammar metrics.     

Minimizing the number of late jobs in a stochastic setting using a chance constraint

We consider the single-machine scheduling problem of minimizing the number of late jobs. We omit here one of the standard assumptions in scheduling theory, which is that the processing times are deterministic. In this scheduling environment, the completion times will be stochastic variables as well. Instead of looking at the expected number of on time jobs, we present a new model to deal with the stochastic completion times, which is based on using a chance constraint to define whether a job is on time or late: a job is on time if the probability that it is completed by the deterministic due date is at least equal to a certain given minimum success probability. We have studied this problem for four classes of stochastic processing times. The jobs in the first three classes have processing times that follow: (i) A gamma distribution with shape parameter p _j and scale parameter β, where β is common to all jobs; (ii) A negative binomial distribution with parameters p _j and r, where r is the same for each job; (iii) A normal distribution with parameters p _j and σ _j ². The jobs in the fourth class have equally disturbed processing times, that is, the processing times consist of a deterministic part and a random component that is independently, identically distributed for each job. We show that the first two cases have a common characteristic that makes it possible to solve these problems in O(nlog n) time through the algorithm by Moore and Hodgson. To analyze the third and fourth problem we need the additional assumption that the due dates and the minimum success probabilities are agreeable. We show that under this assumption the third problem is -hard in the ordinary sense, whereas the fourth problem is solvable by Moore and Hodgson’s algorithm. We further indicate how the problem of maximizing the expected number of on time jobs (with respect to the standard definition) can be tackled if we add the constraint that the on time jobs are sequenced in a given order and when we require that the probability that a job is on time amounts to at least some given lower bound. Supported by EC Contract IST-1999-14186 (Project alcom-FT).     

A non-parametric CAE approach to office rents: Identification of Helsinki metropolitan area submarkets

The study attempts to identify and estimate the office rents of submarkets in the Helsinki metropolitan area. We applied a non-parametric empirical approach called the CAE method to identify six parameters: highway APD (access point distance), car traffic density, light rail APD, main retail distance, office building density and effective age. Our results suggest that car traffic density is the single most influential parameter. Office rent decreases with effective age and increases with the density of office buildings. Longer distances to highway access points and to the main retail centres decrease office rents, while shorter distances to the light rail access points increase office rents in general and particularly for locations close to highway access points. We identified local peaks by inspecting multiple graphs. The local peaks were considered evidence for the existence of commercial office submarkets within the Helsinki metropolitan area. We identified seven submarkets at different rent levels. Interpreting submarkets from the CAE graphs allowed us to recognise particular business districts in the Helsinki metropolitan area. In addition, it is of great significance that the roles of the given and estimated variables can be exchanged. The method is directly applicable in real estate studies using adapted database and prescribed smoothing parameters.     

Regulation of Autophagic Signaling by Mechanical Loading and Inflammation in Human PDL Fibroblasts

Autophagy (cellular self-consumption) is a crucial adaptation mechanism during cellular stress conditions. This study aimed to examine how this important process is regulated in human periodontal ligament (PDL) fibroblasts by mechanical and inflammatory stress conditions and whether the mammalian target of rapamycin (mTOR) signaling pathway is involved. Autophagy was quantified by flow cytometry. Qualitative protein phosphorylation profiling of the mTOR pathway was carried out. Effects of mTOR regulation were assessed by quantification of important synthesis product collagen 1, cell proliferation and cell death with real-time PCR and flow cytometry. Autophagy as a response to mechanical or inflammatory treatment in PDL fibroblasts was dose and time dependent. In general, autophagy was induced by stress stimulation. Phosphorylation analysis of mTOR showed regulatory influences of mechanical and inflammatory stimulation on crucial target proteins. Regulation of mTOR was also detectable via changes in protein synthesis and cell proliferation. Physiological pressure had cell-protective effects (p = 0.025), whereas overload increased cell death (p = 0.003), which was also promoted in long-term inflammatory treatment (p < 0.001). Our data provide novel insights about autophagy regulation by mechanical and inflammatory stress conditions in human PDL fibroblasts. Our results suggest some involvement of the mTOR pathway in autophagy and cell fate regulation under the named conditions.     

An optimal radial basis function neural network for fault location in a distribution network with high penetration of DG units

Due to environmental concerns and growing cost of fossil fuel, high levels of distributed generation (DG) units have been installed in power distribution systems. However, with the installation of DG units in a distribution system, many problems may arise such as increase and decrease of short circuit levels, false tripping of protective devices and protection blinding. This paper presents an automated and accurate fault location method for identifying the exact faulty line in the test distribution network with high penetration level of DG units by using the Radial Basis Function Neural Network with Optimum Steepest Descent (RBFNN–OSD) learning algorithm. In the proposed method, to determine the fault location, two RBFNN–OSD have been developed for various fault types. The first RBFNN–OSD is used for predicting the fault distance from the source and all DG units while the second RBFNN is used for identifying the exact faulty line. Several case studies have been simulated to verify the accuracy of the proposed method. Furthermore, the results of RBFNN–OSD and RBFNN with conventional steepest descent algorithm are also compared. The results show that the proposed RBFNN–OSD can accurately determine the location of faults in a test given distribution system with several DG units.