An adaptive method based on the improved LPA-ICI algorithm for MRI enhancement

Various diseases are diagnosed using medical imaging used for analysing internal anatomical structures. However, medical images are susceptible to noise introduced in both acquisition and transmission processes. We propose an adaptive data-driven image denoising algorithm based on an improvement of the intersection of confidence intervals (ICI), called relative ICI (RICI) algorithm. The 2D mask of the adaptive size and shape is calculated for each image pixel independently, and utilized in the design of the 2D local polynomial approximation (LPA) filters. Denoising performances, in terms of the PSNR, are compared to the original ICI-based method, as well as to the fixed sized filtering. The proposed adaptive RICI-based denoising outperformed the original ICI-based method by up to 1.32 dB, and the fixed size filtering by up to 6.48 dB. Furthermore, since the denoising of each image pixel is done locally and independently, the method is easy to parallelize.     

The use of data-derived label hierarchies in multi-label classification

Instead of traditional (multi-class) learning approaches that assume label independency, multi-label learning approaches must deal with the existing label dependencies and relations. Many approaches try to model these dependencies in the process of learning and integrate them in the final predictive model, without making a clear difference between the learning process and the process of modeling the label dependencies. Also, the label relations incorporated in the learned model are not directly visible and can not be (re)used in conjunction with other learning approaches. In this paper, we investigate the use of label hierarchies in multi-label classification, constructed in a data-driven manner. We first consider flat label sets and construct label hierarchies from the label sets that appear in the annotations of the training data by using a hierarchical clustering approach. The obtained hierarchies are then used in conjunction with hierarchical multi-label classification (HMC) approaches (two local model approaches for HMC, based on SVMs and PCTs, and two global model approaches, based on PCTs for HMC and ensembles thereof). The experimental results reveal that the use of the data-derived label hierarchy can significantly improve the performance of single predictive models in multi-label classification as compared to the use of a flat label set, while this is not preserved for the ensemble models.     

Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm

The idea of non-hierarchical production networks consisting of autonomous enterprises has been present in scientific community for more than 20 years. Although some global corporations are using their own production networks across continents, they are not similar to the original idea of non-hierarchical production networks in many aspects. It seems that this idea waited for production systems to acquire proper information and communications technology (ICT) or new industrial platforms, like Industry 4.0. The result is a new type of production network called Cyber-Physical Production Network (CPPN). The CPPN is, from ICT point of view, ready to act as non-hierarchical production networks consisting of autonomous production systems with many automated processes. One of the most important processes of the CPPN is a selection of optimal partners (enterprises) to be part of a new virtual enterprise, created inside production network. An optimisation problem emerges in this process, and it is called Partner Selection Problem (PSP). It is non-polynomial-hard combinatorial problem. Since metaheuristic algorithms are well-proven in solving that kind of problem, a specially designed metaheuristic algorithm derived from ant colony optimisation and named the HUMANT (HUManoid ANT) algorithm is used in this paper. It is multi-objective optimisation algorithm that successfully solves different instances of PSP with two, three, four or more objectives.     

Hydrodynemics of a cocurrent upflow liquid–liquid reciprocating plate reactor for homogeneously base-catalyzed methanolysis of vegetable oils

Hydrodynamics of a continuous cocurrent two-phase upflow reciprocating plate reactor (RPR) for homogeneously base-catalyzed methanolysis of sunflower oil was studied. Here, methanol constituted the dispersed phase and sunflower oil was the continuous phase. The measurements were performed in both the non-reactive (methanol–sunflower oil) and reactive (sunflower oil–methanol–KOH) systems. The main goal was to define the effects of the vibration intensity and the important reaction operating conditions on the pressure fluctuation at the reactor bottom, the power consumption, the dispersed phase holdup, the Sauter-mean drop diameter and the specific interfacial area. The power consumption under batch, single- and two-phase flow was proved to depend on the vibration intensity. The Sauter-mean drop diameter was found to depend on the specific power consumption in accordance with the turbulent model due to the turbulent energy dissipation in well-mixed regions around perforated plates. The simplified correlation of Kumar and Hartland could be used for estimating the Sauter-mean drop diameter. The energy dissipation due to reciprocating plate motion and the superficial dispersed phase velocity affected the dispersed phase holdup and the specific interfacial area. The present results are crucial for designing RPRs for application in continuous base-catalyzed methanolysis of vegetable oils.     

Reducing Variability to Improve Performance as a Lean Construction Principle

Variability is common on construction projects and must be managed effectively. New management thinking, like that of lean production, has suggested that better labor and cost performance can be achieved by reducing output variability. Efforts to utilize lean thinking in construction, so far, have generated limited evidence to support this claim. This paper investigates the relationship between variability and project performance to test the notion that reducing output variability will result in improved labor performance. Using productivity data from concrete formwork activities on multiple projects, various measures of output variability are tested against construction performance. It is shown that variability in output is inevitable and that there is little correlation between output variability and project performance, but that variability in labor productivity is closely correlated to project performance. It is concluded that lean improvement initiatives should be redirected to adaptable workforce management capabilities to reduce variability in labor productivity instead of output in order to improve project performance.     

The effect of high temperatures on the mechanical properties of concrete made with different types of aggregates

The main objective of this paper was to assess the benefits of using materials that were formed at high temperatures as an aggregate for concrete that was exposed to high temperature. The fire resistance of concrete made with some locally available, potential “fire-resistant” aggregates, such as diabase, steel slag, crushed bricks and crushed tiles, was investigated. The specimens of measurements 4×4×16 cm³ were kept in molds for 24 h and, after demolding, were kept in water at room temperature of about 20±2 °C until testing. At the age of 28 days, the specimens, with moisture content within the limits of 3-5%, were exposed to high temperatures in a previously heated test furnace. The residual mechanical properties (compressive and flexural strengths) of these concretes after natural cooling were compared with the residual mechanical properties of concrete made with commonly used river and dolomite aggregates. The replacement of natural concrete aggregates with brick and steel industry waste materials was justified, not only in terms of increased fire resistance, but also in terms of more responsible waste disposal.     

Comparison of different modes of excitation in X-ray emission spectroscopy in the detection of trace elements in coal and coal ash

Three different modes of sample excitation in X-ray emission spectroscopy were used in trace element analysis of coal and coal ash: proton beam, Mo X-ray tube and radioactive sources ⁵⁷Co and ¹⁰⁹Cd. The results of analysis are presented and methods and their possibilities are compared in order to indicate the most appropriate technique for the particular purpose.     

Polyphenolic profile,antioxidant properties and antimicrobial activity of grape skin extracts of 14 Vitis vinifera varieties grown in Dalmatia (Croatia)

The aim of the present study was to determine polyphenolic composition, related antioxidative and antimicrobial properties of grape skin extracts from 14 grape varieties (seven white and seven red grape) grown in Dalmatia (Croatia). The content of total phenols, flavonoids, catechins, flavanols and individual polyphenols ((+)-catechin, (−)-epicatechin, epicatechin gallate, procyanidin B1 and procyanidin B2, quercetin glucoside, resveratrol monomers, piceid and astringin) was variety dependent. Antioxidant properties were determined as DPPH radical-scavenging ability (IC₅₀), ferric reducing/antioxidant power (FRAP), Fe²⁺-chelating activity (IC₅₀), and using β-carotene bleaching assay. The high antioxidant capacity of all extracts, both red and white, has been observed and related to the relative amounts of polyphenolic compounds with good antioxidant properties. The antimicrobial activity was screened by broth microdilution test using Gram-positive (Staphylococcus aureus, Bacillus cereus) and Gram-negative bacteria (Escherichia coli O157:H7, Salmonella Infantis, Campylobacter coli). It was confirmed against all tested organisms. Minimum inhibitory concentrations (MICs), were found in the range 0.014–0.59 mg of gallic acid equivalents (GAE)/ml, with lower MICs of white cultivars, especially against Campylobacter and Salmonella.