Pneumatic conveying involves the transportation powder, granular and piece of material and is based on the phenomenon that at the appropriate speed of air in the pipeline, the solid particles are brought in the desired direction. If inlet air velocity is too high the material flow rate may be reduced, the power requirements will be excessive, and operating problems will be severe. Considering the advantages that air-slide conveyors can offer in relation to pneumatic conveying systems especially in terms of low power consumption and operating problems associated with abrasive particles, such as erosive wear of system components and degradation of friable particles. In this paper is presented design calculation of airslide conveyor in the thermal power plant.

In this era, big data applications including biomedical are becoming attractive as the data generation and storage is increased in the last years. The big data processing to extract knowledge becomes challenging since the data mining techniques are not adapted to the new requirements. In this study, we analyse the EEG signals for epileptic seizure detection in the big data scenario using Rotation Forest classifier. Specifically, MSPCA is used for denoising, WPD is used for feature extraction and Rotation Forest is used for classification in a MapReduce framework to correctly predict the epileptic seizure. This paper presents a MapReduce-based distributed ensemble algorithm for epileptic seizure prediction and trains a Rotation Forest on each dataset in parallel using a cluster of computers. The results of MapReduce based Rotation Forest show that the proposed framework reduces the training time significantly while accomplishing a high level of performance in classifications.

The progressive nature of glaucoma suggests it should be possible to detect structural changes such as retinal nerve fiber layer (RNFL) thickness loss before the condition becomes clinically apparent with visual field (VF) impairment. Therefore, the aim was to analyze RNFL thickness and VF changes in study groups with unilateral exfoliation syndrome (XFS), bilateral XFS and bilateral exfoliative glaucoma (XFG), and compare it with controls. The study included 114 subjects (228 eyes) divided into 4 groups according to the presence of exfoliation: 30 subjects with unilateral XFS (30 with clinically visible XFS and 30 fellow eyes), 24 subjects (48 eyes) with bilateral XFS, 28 (56 eyes) subjects with bilateral XFG, and control group (32 subjects). All subjects underwent VF and RNFL measurements after ophthalmologic examination. Both eyes of unilateral XFS (clinically -visible and fellow eye) showed positive correlation between Mean Defect (MD) and square root of Loss of Variance (sLV) and between MD and inferior quadrant RNFL thickness. In bilateral XFS and XFG, there was negative correlation between MD and inferior quadrant RNFL thickness. Inferior, superior and nasal quadrant RNFL thickness was lower in XFG group than in other groups. In bilateral XFS group, the inferior quadrant RNFL thickness was lower as compared with unilateral XFS group (in both eyes). The mean RNFL thickness negatively correlated with MD in bilateral XFS and XFG groups. In conclusion, structural changes before VF impairment have an important role in early detection of glaucoma in subjects at risk.

Forest productive attributes changes over time in native forests has been recognized as crucial challenge for management of uneven aged mixed forests in Bosnia and Herzegovina since middle of the last century. Experimental study has been carried out on set of experimental plots established in mixed stands on mountain Igman in central Bosnia. The most important forest productivity attributes changes based on repeated measures have been monitored over time. The aim of this research was to conduct the post-hoc power analysis for monitored forest attributes: basal area per ha (BA), growing stock per ha (GS) and current annual increment per ha (CAIv). Here are used repeated measures conduced on the 10 experimental plots in two types of mixed stands: fir-spruce and fir-spruce-beech plots (five plots per each type) measured in five (BA and GS) and four (CAIv) occasions in periods between 10–20 years. Analyses of variance (ANOVA) within and within-between repeated measures were applied and power analysis was performed. ANOVA within forest type over time showed highly significant differences for all attributes (α = 0.05, p < 0.001). Here, power analysis for comparison of stand attributes resulted in observed high power values ranged from 82% to 99% (very low risk of Type II errors). Then, ANOVA between two forest types over time showed different significances for forest attributes (α = 0.05, pBA = 0.25, pGS = 0.23 and pCAIv = 0.02). The risks of Type II errors were high for BA and GS (from 66% to 72%) while conclusions for CAIv could be accepted with very low risk (4%). So, the post-hoc power analysis of comparisons of stand attributes between forests types found low power for BA (28%) and GS (34%) and high power for CAIv (96%). These findings confirm importance of proper forest species composition planning in mixed stands related to highest wood productivity and other forest characteristics as biodiversity.

Understanding the clonal architecture and evolutionary history of a tumour poses one of the key challenges to overcome treatment failure due to resistant cell populations. Previously, studies on subclonal tumour evolution have been primarily based on bulk sequencing and in some recent cases on single-cell sequencing data. Either data type alone has shortcomings with regard to this task, but methods integrating both data types have been lacking. Here, we present B-SCITE, the first computational approach that infers tumour phylogenies from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that B-SCITE systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. B-SCITE provides high-fidelity reconstructions even with a modest number of single cells and in cases where bulk allele frequencies are affected by copy number changes. On real tumour data, B-SCITE generated mutation histories show high concordance with expert generated trees. Intra-tumour heterogeneity provides important information about subclonal tumour evolution. Here, the authors develop B-SCITE, a computational method for inferring tumour phylogenies from combined single-cell and bulk sequencing data.

Parental socioeconomic status is a multidimensional concept of special importance for the growth, development, health outcomes and education of children. Its definition generally refers to the amount of parents' income, their employment status and level of education. Hence, lack of economic resources and poverty of parents affect all aspects of the child's life, health outcomes and education, as well as his/her social inclusion. Accordingly, the consequences of a reduced parental socioeconomic status leave long-term effects on their children. Therefore, in order to create interventional programs for children of parents with low income and lower socioeconomic status, as well as with lower level of education, it is important to address the direct aspects of poverty. This review contributes to the evidence indicating that the parental socioeconomic status is highly influential in determining the child's physical and mental health and future outcomes including his/her academic achievements and education, as well as the parameters of his/her physical abilities, cognitive function and fundamental neurobiology affecting brain development.