Dyeing of poly(lactic acid) fibres with synthesised novel heterocyclic disazo disperse dyes

Poly(lactic acid) (PLA) is the first melt‐processable, renewable, sustainable and biodegradable natural‐based synthetic fibre. It has a broad range of uses and combines ecological advantages with outstanding performance in textiles. PLA fibre, as an aliphatic polyester, can be dyed with disperse dyes. Apart from the limited number of commercial disperse dyes, disperse dye exhaustion on PLA is generally lower than that on  poly(ethylene terephthalate) (PET). In this study, new heterocyclic disazo disperse dyes, substituted with methyl, nitro and chloro groups at their ortho‐, meta‐ and para‐ positions, synthesised in our previous study, were applied to PLA and PET fibres to examine their dyeing performance, and colour fastness and dye exhaustion properties. Different shades of yellow, orange, reddish brown and brown were obtained. Most of the synthesised novel heterocyclic disazo disperse dyes exhibited good build‐up properties with high K/S levels on both fibres. Para‐ bonding substituent provided higher K/S values than meta‐ and ortho‐ positions for –NO₂ and –Cl substituents for both fibres. Overall, the most synthesised novel heterocyclic disazo disperse dyes in this study exhibited good build‐up properties with high K/S, exhaustion and wet fastness levels on both PLA and PET fibres.      

SONIC: streaming overlapping community detection

A community within a graph can be broadly defined as a set of vertices that exhibit high cohesiveness (relatively high number of edges within the set) and low conductance (relatively low number of edges leaving the set). Community detection is a fundamental graph processing analytic that can be applied to several application domains, including social networks. In this context, communities are often overlapping, as a person can be involved in more than one community (e.g., friends, and family); and evolving, since the structure of the network changes. We address the problem of streaming overlapping community detection, where the goal is to maintain communities in the presence of streaming updates. This way, the communities can be updated more efficiently. To this end, we introduce SONIC—a find-and-merge type of community detection algorithm that can efficiently handle streaming updates. SONIC first detects when graph updates yield significant community changes. Upon the detection, it updates the communities via an incremental merge procedure. The SONIC algorithm incorporates two additional techniques to speed-up the incremental merge; min-hashing and inverted indexes. Results show that SONIC can provide high quality overlapping communities, while handling streaming updates several orders of magnitude faster than the alternatives performing from-scratch computation.     

River: an intermediate language for stream processing

This paper presents both a calculus for stream processing, named Brooklet, and its realization as an intermediate language, named River. Because River is based on Brooklet, it has a formal semantics that enables reasoning about the correctness of source translations and optimizations. River builds on Brooklet by addressing the real‐world details that the calculus elides. We evaluated our system by implementing front‐ends for three streaming languages, and three important optimizations, and a back‐end for the System S distributed streaming runtime. Overall, we significantly lower the barrier to entry for new stream‐processing languages and thus grow the ecosystem of this crucial style of programming. Copyright © 2015 John Wiley & Sons, Ltd.     

Auto-tuning Similarity Search Algorithms on Multi-core Architectures

In recent times, large high-dimensional datasets have become ubiquitous. Video and image repositories, financial, and sensor data are just a few examples of such datasets in practice. Many applications that use such datasets require the retrieval of data items similar to a given query item, or the nearest neighbors (NN or $k$ -NN) of a given item. Another common query is the retrieval of multiple sets of nearest neighbors, i.e., multi $k$ -NN, for different query items on the same data. With commodity multi-core CPUs becoming more and more widespread at lower costs, developing parallel algorithms for these search problems has become increasingly important. While the core nearest neighbor search problem is relatively easy to parallelize, it is challenging to tune it for optimality. This is due to the fact that the various performance-specific algorithmic parameters, or “tuning knobs”, are inter-related and also depend on the data and query workloads. In this paper, we present (1) a detailed study of the various tuning knobs and their contributions on increasing the query throughput for parallelized versions of the two most common classes of high-dimensional multi-NN search algorithms: linear scan and tree traversal, and (2) an offline auto-tuner for setting these knobs by iteratively measuring actual query execution times for a given workload and dataset. We show experimentally that our auto-tuner reaches near-optimal performance and significantly outperforms un-tuned versions of parallel multi-NN algorithms for real video repository data on a variety of multi-core platforms.     

An investigation on renewable energy education at the university level in Turkey

In the present study, to discover how teaching of renewable energy sources in Turkey is carried out at the university level, a questionnaire was developed and applied at different universities in Turkey. The analyses conducted on the data obtained from the questionnaire revealed that education about geothermal, solar and wind energy is given at master’s level, other renewable energy sources are taught at the undergraduate level within the curriculum of some engineering courses. The teaching performed about renewable energy sources is in Turkish and at encyclopedic level. As preparing and obtaining the required materials are costly, some problems are encountered in the teaching of renewable sources. In Turkish universities, no degree about renewable energy sources is granted. Hence, the country must make use of the people having engineering degree to capitalize on its renewable energy sources. As there is no program specializing on providing training about the renewable energy sources, it seems to be difficult to find experts about this issue. The current state of renewable energy sources education seems to be inadequate and it should be expanded and strengthened.     

Evaluation of three-dimensional effects in short deep beams using a rigid-body-spring-model

Three-dimensional (3-D) effects in short deep beams without stirrups that failed in shear were investigated experimentally and analytically. Two deep beams with a shear span to depth ratio (a/d) of 0.5 and with different beam widths were tested. The effect of beam width on load-carrying capacity, failure mode, crack pattern and 3-D behavior was investigated, and shape effect due to beam width was clarified. In addition, the beams were analyzed by the 3-D rigid-body-spring model (RBSM). RBSM is a discrete form of modeling that presents realistic behavior from cracking to failure, and 3-D RBSM is applicable to simulate 3-D behavior as well as the confinement effect of concrete. Analytical results in terms of load–displacement curves and crack pattern are compared with the experimental results. Three-dimensional deformations, strut widths and cross-sectional stress distribution are investigated analytically and compared with the experimental results to determine 3-D behavior in detail. The 3-D effects in short deep beams are clarified.     

A case of Alstr?m syndrome associated with diabetes insipidus

The production of biogenic amines by 50 poultry-associated bacterial strains (25 Pseudomonas, 13 Salmonella and 12 Listeria) was investigated on amine agar plates containing lysine, histidine, ornithine, phenylalanine, tryptophan and tyrosine. Seventy-four per cent of all the strains produced cadaverine and putrescine, while phenylethylamine, histamine, tyramine and tryptamine were produced by 72, 56, 34 and 24% of strains, respectively. Different patterns of biogenic amine production amongst the three bacterial genera tested were apparent as well as amongst strains of the same genus. This study highlighted a high incidence of biogenic amine-producing bacterial strains associated with poultry.     

Plasma endothelin-1 levels in non-insulin dependent diabetes mellitus patients with macrovascular disease

BACKGROUND: Endothelin-1 (ET-1) with its well-known vasoconstrictive and mitogenic action and through its interaction with insulin, blood glucose, and lipids might play an important role in the accelerated atherogenic process in diabetes mellitus. OBJECTIVE: To determine whether ET-1 levels are indicative of macrovascular disease in diabetes mellitus. METHODS: In the present cross-sectional study, plasma ET-1 concentrations were measured in members of three groups. The first group consisted of 20 patients (15 men and five women; aged 56.3 +/- 12.5 years) with non-insulin-dependent diabetes mellitus and coronary artery disease, the second group of 20 patients (16 men and four women, aged 56.9 +/- 11.2 years) with coronary artery disease only, and the third group of 10 healthy subjects who served as controls. ET-1 levels were determined by a radioimmunoassay. RESULTS: The mean plasma ET-1 levels for the three groups were 3.59 +/- 1.88, 4.31 +/- 1.32, and 4.42 +/- 1.01 pmol/l respectively, and there was no statistically significant difference among the groups (P = 0.23). There was also no correlation between the plasma ET-1 concentration and age, sex, body mass index, triglyceride, total cholesterol, high-, low- and very-low density lipoprotein levels, for all groups, and, for the first group, hemoglobin A1c (HbA1c), the duration of diabetes mellitus. CONCLUSION: The plasma ET-1 concentration is not elevated in non-insulin-dependent diabetes mellitus patients with macrovascular disease, which might reflect the fact that its action occurs in a paracrine or an autocrine rather than an endocrine fashion and suggests that ET-1 levels are not necessarily indicative of macrovascular disease in diabetes mellitus.     

Incremental <Emphasis Type="Italic">k</Emphasis>-core decomposition: algorithms and evaluation

A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-hard problems on real networks efficiently, like maximal clique finding. In many real-world applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for dynamic graph data. In this paper, we propose a suite of incremental k-core decomposition algorithms for dynamic graph data. These algorithms locate a small subgraph that is guaranteed to contain the list of vertices whose maximum k-core values have changed and efficiently process this subgraph to update the k-core decomposition. We present incremental algorithms for both insertion and deletion operations, and propose auxiliary vertex state maintenance techniques that can further accelerate these operations. Our results show a significant reduction in runtime compared to non-incremental alternatives. We illustrate the efficiency of our algorithms on different types of real and synthetic graphs, at varying scales. For a graph of 16 million vertices, we observe relative throughputs reaching a million times, relative to the non-incremental algorithms.