Intelligence metasynthesis and knowledge processing in intelligent systems

Intelligence and Knowledge play more and more important roles in building complex intelligent systems, for instance, intrusion detection systems, and operational analysis systems. Knowledge processing in complex intelligent systems faces new challenges from the increased number of applications and environment, such as the requirements of representing domain and human knowledge in intelligent systems, and discovering actionable knowledge on a large scale in distributed web applications. In this paper, we discuss the main challenges of, and promising approaches to, intelligence metasynthesis and knowledge processing in open complex intelligent systems. We believe (1) ubiquitous intelligence, including data intelligence, domain intelligence, human intelligence, network intelligence and social intelligence, is necessary for OCIS, which needs to be meta-synthesized; and (2) knowledge processing should pay more attention to developing innovative and workable methodologies, techniques, tools and systems for representing, modelling, transforming, discovering and servicing the uncertain, large-scale, deep, distributed, domain-oriented, human-involved, and actionable knowledge highly expected in constructing open complex intelligent systems. To this end, the meta-synthesis of ubiquitous intelligence is an appropriate way in designing complex intelligent systems. To support intelligence meta-synthesis, m-interaction can play as the working mechanism to form rn-spaces as problem-solving systems. In building such m-spaces, advancement in knowledge processing is necessary. © J.UCS

Advanced robust tracking control of a powered wheelchair system.

In this paper, the dynamic multivariable model of the wheelchair system is obtained including the presence of transportation lags. The triangular diagonal dominance (TDD) decoupling technique is applied to reduce this multivariable control problem into two independent scalar control problems. An advanced robust control technique for the wheelchair has been developed based on the combination of a TDD decoupling strategy and neural network controller design. The results obtained from the real-time implementation confirm that robust performance for this multivariable wheelchair control system can indeed be achieved

Effect of Dissolved Silicon on the Removal of Heavy Metals from Aqueous Solution by Aquatic Macrophyte Eleocharis acicularis

Silicon (Si) has been recently reconsidered as a beneficial element due to its direct roles in stimulating the growth of many plant species and alleviating metal toxicity. This study aimed at validating the potential of an aquatic macrophyte Eleocharis acicularis for simultaneous removal of heavy metals from aqueous solutions under different dissolved Si. The laboratory experiments designed for determining the removal efficiencies of heavy metals were conducted in the absence or presence of Si on a time scale up to 21 days. Eleocharis acicularis was transplanted into the solutions containing 0.5 mg L−1 of indium (In), gallium (Ga), silver (Ag), thallium (Tl), copper (Cu), zinc (Zn), cadmium (Cd), and lead (Pb) with various Si concentrations from 0 to 4.0 mg L−1. The results revealed that the increase of dissolved Si concentrations enhanced removal efficiencies of E. acicularis for Ga, Cu, Zn, Cd, and Pb, while this increase did not show a clear effect for In, Tl, and Ag. Our study presented a notable example of combining E. acicularis with dissolved Si for more efficient removals of Cu, Zn, Cd, Pb, and Ga from aqueous solutions. The findings are applicable to develop phytoremediation or phytomining strategy for contaminated environment.</jats:p

Energy Management and Time Scheduling for Heterogeneous IoT Wireless-Powered Backscatter Networks

© 2019 IEEE. In this paper, we propose a novel approach to jointly address energy management and network throughput maximization problems for heterogeneous IoT low-power wireless communication networks. In particular, we consider a low-power communication network in which the IoT devices can harvest energy from a dedicated RF energy source to support their transmissions or backscatter the signals of the RF energy source to transmit information to the gateway. Different IoT devices may have dissimilar hardware configurations, and thus they may have various communications types and energy requirements. In addition, the RF energy source may have a limited energy supply source which needs to be minimized. Thus, to maximize the network throughput, we need to jointly optimize energy usage and operation time for the IoT devices under different energy demands and communication constraints. However, this optimization problem is non-convex due to the strong relation between energy supplied by the RF energy source and the IoT communication time, and thus obtaining the optimal solution is intractable. To address this problem, we study the relation between energy supply and communication time, and then transform the non-convex optimization problem to an equivalent convex-optimization problem which can achieve the optimal solution. Through simulation results, we show that our solution can achieve greater network throughputs (up to five times) than those of other conventional methods, e.g., TDMA. In addition, the simulation results also reveal some important information in controlling energy supply and managing low-power IoT devices in heterogeneous wireless communication networks

Prediction of necrotic core and hypoxic zone of multicellular spheroids in a microbioreactor with a U-shaped barrier

© 2018 by the authors. Microfluidic devices have been widely used for biological and cellular studies. Microbioreactors for three-dimensional (3D) multicellular spheroid culture are now considered as the next generation in in vitro diagnostic tools. The feasibility of using 3D cell aggregates to form multicellular spheroids in a microbioreactor with U-shaped barriers has been demonstrated experimentally. A barrier array is an alternative to commonly used microwell traps. The present study investigates oxygen and glucose concentration distributions as key parameters in a U-shaped array microbioreactor using finite element simulation. The effect of spheroid diameter, inlet concentration and flow rate of the medium are systematically studied. In all cases, the channel walls are considered to be permeable to oxygen. Necrotic and hypoxic or quiescent regions corresponding to both oxygen and glucose concentration distributions are identified for various conditions. The results show that the entire quiescent and necrotic regions become larger with increasing spheroid diameter and decreasing inlet and wall concentration. The shear stress (0.5-9 mPa) imposed on the spheroid surface by the fluid flow was compared with the critical values to predict possible damage to the cells. Finally, optimum range of medium inlet concentration (0.13-0.2 mM for oxygen and 3-11 mM for glucose) and flow rate (5-20 μL/min) are found to form the largest possible multicellular spheroid (500 μm), without any quiescent and necrotic regions with an acceptable shear stress. The effect of cell-trap types on the oxygen and glucose concentration inside the spheroid was also investigated. The levels of oxygen and glucose concentration for the microwell are much lower than those for the other two traps. The U-shaped barrier created with microposts allows for a continuous flow of culture medium, and so improves the glucose concentration compared to that in the integrated U-shaped barrier. Oxygen concentration for both types of U-shaped barriers is nearly the same. Due to the advantage of using U-shaped barriers to culture multicellular spheroids, the results of this paper can help to choose the experimental and design parameters of the microbioreactor

A novel numerical model to predict the morphological behavior of magnetic liquid marbles using coarse grained molecular dynamics concepts

© 2018 Author(s). Liquid marbles are liquid droplets coated with superhydrophobic powders whose morphology is governed by the gravitational and surface tension forces. Small liquid marbles take spherical shapes, while larger liquid marbles exhibit puddle shapes due to the dominance of gravitational forces. Liquid marbles coated with hydrophobic magnetic powders respond to an external magnetic field. This unique feature of magnetic liquid marbles is very attractive for digital microfluidics and drug delivery systems. Several experimental studies have reported the behavior of the liquid marbles. However, the complete behavior of liquid marbles under various environmental conditions is yet to be understood. Modeling techniques can be used to predict the properties and the behavior of the liquid marbles effectively and efficiently. A robust liquid marble model will inspire new experiments and provide new insights. This paper presents a novel numerical modeling technique to predict the morphology of magnetic liquid marbles based on coarse grained molecular dynamics concepts. The proposed model is employed to predict the changes in height of a magnetic liquid marble against its width and compared with the experimental data. The model predictions agree well with the experimental findings. Subsequently, the relationship between the morphology of a liquid marble with the properties of the liquid is investigated. Furthermore, the developed model is capable of simulating the reversible process of opening and closing of the magnetic liquid marble under the action of a magnetic force. The scaling analysis shows that the model predictions are consistent with the scaling laws. Finally, the proposed model is used to assess the compressibility of the liquid marbles. The proposed modeling approach has the potential to be a powerful tool to predict the behavior of magnetic liquid marbles serving as bioreactors

Novel approaches in cancer management with circulating tumor cell clusters

© 2019 The Authors Tumor metastasis is responsible for the vast majority of cancer-associated morbidities and mortalities. Recent studies have disclosed the higher metastatic potential of circulating tumor cell (CTC) clusters than single CTCs. Despite long-term study on metastasis, the characterizations of its most potent cellular drivers, i.e., CTC clusters have only recently been investigated. The analysis of CTC clusters offers new intuitions into the mechanism of tumor metastasis and can lead to the development of cancer diagnosis and prognosis, drug screening, detection of gene mutations, and anti-metastatic therapeutics. In recent years, considerable attention has been dedicated to the development of efficient methods to separate CTC clusters from the patients’ blood, mainly through micro technologies based on biological and physical principles. In this review, we summarize recent developments in CTC clusters with a particular emphasis on passive separation methods that specifically have been developed for CTC clusters or have the potential for CTC cluster separation. Methods such as liquid biopsy are of paramount importance for commercialized healthcare settings. Furthermore, the role of CTC clusters in metastasis, their physical and biological characteristics, clinical applications and current challenges of this biomarker are thoroughly discussed. The current review can shed light on the development of more efficient CTC cluster separation method that will enhance the pivotal understanding of the metastatic process and may be practical in contriving new strategies to control and suppress cancer and metastasis

Highly Stretchable Conductive Covalent Coacervate Gels for Electronic Skin.

Highly stretchable electrically conductive hydrogels have been extensively researched in recent years, especially for applications in strain and pressure sensing, electronic skin, and implantable bioelectronic devices. Herein, we present a new cross-linked complex coacervate approach to prepare conductive hydrogels that are both highly stretchable and compressive. The gels involve a complex coacervate between carboxylated nanogels and branched poly(ethylene imine), whereby the latter is covalently cross-linked by poly(ethylene glycol) diglycidyl ether (PEGDGE). Inclusion of graphene nanoplatelets (Gnp) provides electrical conductivity as well as tensile and compressive strain-sensing capability to the hydrogels. We demonstrate that judicious selection of the molecular weight of the PEGDGE cross-linker enables the mechanical properties of these hydrogels to be tuned. Indeed, the gels prepared with a PEGDGE molecular weight of 6000 g/mol defy the general rule that toughness decreases as strength increases. The conductive hydrogels achieve a compressive strength of 25 MPa and a stretchability of up to 1500%. These new gels are both adhesive and conformal. They provide a self-healable electronic circuit, respond rapidly to human motion, and can act as strain-dependent sensors while exhibiting low cytotoxicity. Our new approach to conductive gel preparation is efficient, involves only preformed components, and is scalable