Soil improvement using plant-derived urease-induced calcium carbonate precipitation

This study addresses a soil improvement technique using plant-derived urease-induced calcium carbonate (CC) precipitation (PDUICCP) as an alternative to microbially induced carbonate precipitation (MICP). A crude extract of crushed watermelon (Citrullus lanatus) seeds was used as the urease source along with calcium chloride (CaCl₂) and urea (CO (NH₂)₂) for CC precipitation. Test specimens (φ?=?2.3?cm, h?=?7.1?cm) made from commercially available Mikawa sand (mean diameter, D₅₀?=?870?µm) were cemented, and estimated unconfined compressive strength (UCS) of several kPa to MPa was obtained by changing the concentration of CaCl₂- urea, urease activity, curing time, and temperature. The increase of curing time and that of the CaCl₂-urea concentration from 0.3?M to 0.7?M caused an increase in estimated UCS value. The average estimated UCS obtained after 14?days’ curing time for 0.7?M CaCl₂-urea and 3.912 U/mL urease was around 3.0?MPa and for 0.3 and 0.5?M CaCl₂-urea and 0.877 U/mL urease, it was around 1.5–2.0?MPa at 25?°C. By changing each of the abovementioned parameters, it may be possible to apply this method for strength improvement of loose sand, to mitigate the liquefaction, protection and restoration of limestone monuments and statuaries, and artificial soft rock formations. Crude urease from crushed watermelon seeds has the potential to replace commercially available urease for carbonate precipitation and for use as a low environmental impact type soil improvement method.     

Resistance of the flow over rough surfaces

The mechanism of the flow resistance in open channels and pipelines is of vital importance for various critical issues related to the water flow.The Nikurade's ...     

Nutritional,physicochemical, and textural properties of gluten-free extruded snacks containing cowpea and whey protein concentrate

Ready-to-eat extruded snacks with high protein and fibre were developed from a composite flour comprising rice flour, cowpea flour and whey protein concentrate (WPC). Nutritional, physicochemical, and textural properties of extrudates were evaluated, at five ratios of cowpea: WPC (10:0, 15:05, 20:10, 25:15, 30:20); rice flour was used as a control. The protein and fibre content in the extrudates significantly increased (P ≤ 0.05) with cowpea (10%–30%) and WPC (5%–20%) incorporation compared to the control. The extrudates with higher levels of cowpea and WPC showed a significant increase in bulk density and hardness. A slight decrease of 12% was observed in the expansion of 15% cowpea and 5% WPC fortified extrudates compared to the control. The number of peaks during compression increased with incorporations of cowpea and WPC. All cowpea and WPC containing snacks were darker than the control. Significant correlations were found between the protein, fibre, colour values and textural properties. The essential and non-essential amino acid profiles increased in the extrudates, proportionally to the cowpea and WPC fortification.     

Changes at an activated sludge sewage treatment plant alter the numbers of airborne aerobic microorganisms

In 1976, the activated sludge sewage treatment plant in Edmonton, Canada, was surveyed to determine the numbers of culturable airborne microorganisms. Many changes have been made at the plant to reduce odors and improve treatment efficiency, so in 2004 another survey was done to determine if these changes had reduced the bioaerosols. Covering the grit tanks and primary settling tanks greatly reduced the numbers of airborne microbes. Changing the design and operation of indoor automated sampling taps and sinks also reduced bioaerosols. The secondary was expanded and converted from a conventional activated sludge process using coarse bubble aeration to a biological nutrient removal system using fine bubble aeration. Although the surface area of the secondary more than doubled, the average number of airborne microorganisms in this part of the plant in 2004 was about 1% of that in 1976.     

Hyper-structure mining of frequent patterns in uncertain data streams

Data uncertainty is inherent in many real-world applications such as sensor monitoring systems, location-based services, and medical diagnostic systems. Moreover, many real-world applications are now capable of producing continuous, unbounded data streams. During the recent years, new methods have been developed to find frequent patterns in uncertain databases; nevertheless, very limited work has been done in discovering frequent patterns in uncertain data streams. The current solutions for frequent pattern mining in uncertain streams take a FP-tree-based approach; however, recent studies have shown that FP-tree-based algorithms do not perform well in the presence of data uncertainty. In this paper, we propose two hyper-structure-based false-positive-oriented algorithms to efficiently mine frequent itemsets from streams of uncertain data. The first algorithm, UHS-Stream, is designed to find all frequent itemsets up to the current moment. The second algorithm, TFUHS-Stream, is designed to find frequent itemsets in an uncertain data stream in a time-fading manner. Experimental results show that the proposed hyper-structure-based algorithms outperform the existing tree-based algorithms in terms of accuracy, runtime, and memory usage.     

A GPU-oriented online recommendation algorithm for efficient processing of time-varying continuous data streams

Research on recommendation systems has gained a considerable amount of attention over the past decade as the number of online users and online contents continue to grow at an exponential rate. With the evolution of the social web, people generate and consume data in real time using online services such as Twitter, Facebook, and web news portals. With the rapidly growing online community, web-based retail systems and social media sites have to process several millions of user requests per day. Generating quality recommendations using this vast amount of data is itself a very challenging task. Nevertheless, opposed to the web-based retailers such as Amazon and Netflix, the above-mentioned social networking sites have to face an additional challenge when generating recommendations as their contents are very rapidly changing. Therefore, providing fresh information in the least amount of time is a major objective of such recommender systems. Although collaborative filtering is a widely used technique in recommendation systems, generating the recommendation model using this approach is a costly task, and often done offline. Hence, it is difficult to use collaborative filtering in the presence of dynamically changing contents, as such systems require frequent updates to the recommendation model to maintain the accuracy and the freshness of the recommendations. Parallel processing power of graphic processing units (GPUs) can be used to process large volumes of data with dynamically changing contents in real time, and accelerate the recommendation process for social media data streams. In this paper, we address the issue of rapidly changing contents, and propose a parallel on-the-fly collaborative filtering algorithm using GPUs to facilitate frequent updates to the recommendations model. We use a hybrid similarity calculation method by combining the item–item collaborative filtering with item category information and temporal information. The experimental results on real-world datasets show that the proposed algorithm outperformed several existing online CF algorithms in terms of accuracy, memory consumption, and runtime. It was also observed that the proposed algorithm scaled well with the data rate and the data volume, and generated recommendations in a timely manner.