BACKGROUND: Due to their diverse bioactivity, natural product (NP)s have been developed as commercial products in the pharmaceutical, food and cosmetic sectors as natural compound (NC)s and in the form of extracts. Following administration, NCs typically interact with multiple target proteins to elicit their effects. Various machine learning models have been developed to predict multi-target modulating NCs with desired physiological effects. However, due to deficiencies with existing chemical-protein interaction datasets, which are mostly single-labeled and limited, the existing models struggle to predict new chemical-protein interactions. New techniques are needed to overcome these limitations. RESULTS: We propose a novel NC discovery model called OptNCMiner that offers various advantages. The model is trained via end-to-end learning with a feature extraction step implemented, and it predicts multi-target modulating NCs through multi-label learning. In addition, it offers a few-shot learning approach to predict NC-protein interactions using a small training dataset. OptNCMiner achieved better prediction performance in terms of recall than conventional classification models. It was tested for the prediction of NC-protein interactions using small datasets and for a use case scenario to identify multi-target modulating NCs for type 2 diabetes mellitus complications. CONCLUSIONS: OptNCMiner identifies NCs that modulate multiple target proteins, which facilitates the discovery and the understanding of biological activity of novel NCs with desirable health benefits.

Rapid advancement of semiconductor, personal computing, and mobile communications technology during last decades has been transforming one's life style into "Digital Life Style", in which one can create, share, and enjoy multimedia information in his own personalized virtual space in mobile environment. There are, in general, three key enabling factors to realize the future digital life style: easily accessible multimedia contents for diversified user applications, communication and information infrastructure to support such access from user, and intelligent user devices to deliver such digital contents in an user friendly manner. In this talk, future prospect of user devices in consumer electronics space and associated technological challenges, especially in low power aspects, will be discussed. Brief overview of current state of low power technology employed by many portable devices and wireless data collection systems will be followed by the suggestion of future research areas for low power user devices in the areas of portable power sources, components, software architecture, and SOC (System-On-Chips) technology. Finally, Samsung's R&D strategy in low power technology will be discussed.

In this paper, an area efficient VLSI architecture of decision feedback equalizer is derived accommodating 64/256 QAM modulators. This architecture is implemented efficiently in reusable VLSI structure using EDA tool due to its regular structure. The main idea is to employ a time-multiplexed design scheme grouping the adjacent filter taps with correlated internal dataflow and with data transfer having same processing sequence between blocks. We simulated the proposed design scheme using SYNOPSYSTM and SPWTM. Index Terms - Decision feedback equalizer, QAM, reusable VLSI implementation, FIR filter.

The paper discusses a control scheme for broadband switching systems consisting of a hybrid multilevel control network and a three-phase commit control protocol. The proposed architecture and protocol allow for fast connection setup in point-to-point connections while efficiently handling multicast and broadcast connections. The consistency of system status is maintained through the three-phase commit procedure. The optimal control configuration to minimize the connection setup service time is derived by using both analytical and simulation approaches.