Design improvements on rotary valve particle feeders used for obtaining suspended airflows

The back repulsion of low density particles, sticking of particles in feeder valve and interlocking and cohesive archs of particles in feeder hopper have been widely observed problems of a conventional rotary valve feeder used for feeding of granular particles into an airflow line. In this study, these problems of the rotary valve feeders were isolated by means of novel modifications to its conventional design. Results of the modifications showed that the modified feeder can be sensitively used to feed granular particles with mass feeding rates ranging between 5±0.15 and 85±0.85 g/s without encountering problems experienced with conventional rotary valves.     

Hesperidin Promotes Osteogenesis and Modulates Collagen Matrix Organization and Mineralization In Vitro and In Vivo

This study evaluated the direct effect of a phytochemical, hesperidin, on pre-osteoblast cell function as well as osteogenesis and collagen matrix quality, as there is little known about hesperidin’s influence in mineralized tissue formation and regeneration. Hesperidin was added to a culture of MC3T3-E1 cells at various concentrations. Cell proliferation, viability, osteogenic gene expression and deposited collagen matrix analyses were performed. Treatment with hesperidin showed significant upregulation of osteogenic markers, particularly with lower doses. Mature and compact collagen fibrils in hesperidin-treated cultures were observed by picrosirius red staining (PSR), although a thinner matrix layer was present for the higher dose of hesperidin compared to osteogenic media alone. Fourier-transform infrared spectroscopy indicated a better mineral-to-matrix ratio and matrix distribution in cultures exposed to hesperidin and confirmed less collagen deposited with the 100-µM dose of hesperidin. In vivo, hesperidin combined with a suboptimal dose of bone morphogenetic protein 2 (BMP2) (dose unable to promote healing of a rat mandible critical-sized bone defect) in a collagenous scaffold promoted a well-controlled (not ectopic) pattern of bone formation as compared to a large dose of BMP2 (previously defined as optimal in healing the critical-sized defect, although of ectopic nature). PSR staining of newly formed bone demonstrated that hesperidin can promote maturation of bone organic matrix. Our findings show, for the first time, that hesperidin has a modulatory role in mineralized tissue formation via not only osteoblast cell differentiation but also matrix organization and matrix-to-mineral ratio and could be a potential adjunct in regenerative bone therapies.     

Fabrication of chitosan/polyvinyl alcohol/amine modified carbon nanotube composite films for rapid chromate removal

Composite adsorbent films with amine and hydroxyl functionalities were synthesized from chitosan (CS), polyvinyl alcohol (PVA), and amine-modified carbon nanotubes (a-MWCNT) by solvent casting method. Weight proportions of CS to PVA and weight percent of a-MWCNT were optimized to achieve highest chromate removal capacity. Structural characteristics of the composites were investigated using scanning electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, and thermal gravimetric analysis. Accordingly, incorporation of a-MWCNT to CS/PVA structure resulted in the generation of nanochannels, which enhanced adsorption capacity. Moreover, the composite comprising 0.4% wt. a-MWCNT provided over 99% of Cr (VI) removal from 50 mg L⁻¹ Cr (VI) solution within five minutes of contact time. Redlich–Peterson and Radke–Prausnitz isotherm models provided the highest conformity to adsorption data. Maximum chromate sorption capacity of CS/PVA/a-MWCNT composite film was determined as 134.2 mg g⁻¹ being 172% higher than that of CS/PVA. Regeneration was best achieved in 1.0 M NaOH and the composite was shown to retain at least 70% of its original capacity after five consecutive adsorption–desorption cycles.     

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.     

Strategic effort allocation in online innovation tournaments

Online innovation tournaments, such as those hosted by crowdsourcing platforms (e.g., Kaggle), have been widely adopted by firms to evolve creative solutions to various problems. Solvers compete in these tournaments to earn rewards. In such competitive environments, it is imperative that solvers provide creative solutions with minimum effort. This article explores the factors that influence the solvers’ effort allocation decisions in a dynamic tournament setting. Specifically, comprehensive time variant data of teams that participated in crowdsourcing competitions on Kaggle were analyzed to gain insight into how solvers continually formulate strategies in light of performance feedback obtained through interim ranking. The results suggest that solvers strategically allocate their efforts throughout the contest to dynamically optimize their payoffs through balancing the probability of winning and the cost of expending effort. In particular, solvers tend to increase their efforts toward the end of tournaments or when they get closer to winning positions. Furthermore, our findings indicate that a last-minute surge in effort is more prevalent among high-skill solvers than in those with lower skill levels. In addition to providing insights that may help solvers develop strategies to improve their performance, the study has implications for the design of online crowdsourcing platforms, particularly in terms of incentivizing solvers to put forth their best effort.