A B S T R A C TThe antioxidant capacities and total phenolic contents of lipophilic and hydrophilic extracts of 56 commonly consumed vegetables were studied. The resulted showed that antioxidant capacities and total phenolic contents in the lipophilic fraction were higher than those in hydrophilic fraction. The different vegetables had diverse antioxidant capacities. The highest antioxidant capacities and phenolic contents were found in Chinese toon bud, loosestrife, perilla leaf, cowpea, caraway, lotus root, sweet potato leaf, soy bean (green), pepper leaf, ginseng leaf, chives, and broccoli, while the values were very low in marrow squash and eggplant (purple). Furthermore, several phenolic compounds were detected, and chlorogenic acid, gallic acid and galangin were widely found in these vegetables. The results provide support for dietary guidelines as well as epidemiological research.

Fruit wastes are one of the main sources of municipal waste. In order to explore the potential of fruit wastes as natural resources of bioactive compounds, the antioxidant potency and total phenolic contents (TPC) of lipophilic and hydrophilic components in wastes (peel and seed) of 50 fruits were systematically evaluated. The results showed that different fruit residues had diverse antioxidant potency and the variation was very large. Furthermore, the main bioactive compounds were identified and quantified, and catechin, cyanidin 3-glucoside, epicatechin, galangin, gallic acid, homogentisic acid, kaempferol, and chlorogenic acid were widely found in these residues. Especially, the values of ferric-reducing antioxidant power (FRAP), trolox equivalent antioxidant capacity (TEAC) and TPC in the residues were higher than in pulps. The results showed that fruit residues could be inexpensive and readily available resources of bioactive compounds for use in the food and pharmaceutical industries.

The success of e-commerce, messaging middleware, and other Internet-based applications depends in part on the ability of network servers to respond in a timely and reliable manner to simultaneous service requests. Multithreaded systems, due to their efficient use of system resources and the popularity of shared-memory multi-processor architectures, have become the server implementation of choice. However, creating and destroying a thread is far from free, requiring run-time memory allocation and deallocation. These overheads become especially onerous during periods of high load and can be a major factor behind system slowdowns. A thread-pool architecture addresses this problem by prespawning and then managing a pool of threads. Threads in the pool are reused, so that thread creation and destruction overheads are incurred only once per thread, and not once per request. However, efficient thread management for a given system load highly depends on the thread pool size, which is currently determined heuristically. In this paper, we characterize several system resource costs associated with thread pool size. If the thread pool is too large, and threads go unused, then processing and memory resources are wasted maintaining the thread pool. If the thread pool is too small, then additional threads must be created and destroyed on the fly to handle new requests. We analytically determine the optimal thread pool size to maximize the expected gain of using a thread.

No abstract

In the service network, there exist various objects and rich relations among them. These various objects and rich relations naturally form a heterogeneous information network. Service recommendations can help users to locate their desired services. Previous service recommendation studies mainly aim at homogeneous networks or consider few kinds of relations rather than using the rich heterogeneous information. In this paper, we propose a mashup group preference-based service recommendation method in the heterogeneous information network for mashup creation. First of all, we analyze the historical invocation records between mashups and services and exploit the heterogeneous information to construct diverse meta paths with different semantic meanings. Then, we measure the similarity between the starting object and the ending object from different perspectives and integrate different similarity measures to obtain the hybrid similarity. Next, we introduce group preference to capture the rich interactions among mashups and apply a group preference-based Bayesian personalized ranking algorithm to learn the model. Finally, we recommend a list of personalized ranking services for mashup developers. A series of experiments conducted on a realworld dataset demonstrate the superiority of our proposed approach over other baseline approaches. INDEX TERMS Heterogeneous information network, meta path, service recommendation, group preference.

No abstract

No abstract

No abstract