Presence, molecular characteristics and geosmin producing ability of actinomycetes isolated from South Korean terrestrial and aquatic environments

The unpleasant odor of drinking water is one of the major problems in many water utilities in the world. Actinomycetes have long been associated with odorous compounds. Considering the paucity of research on Actinomycetes producing odorous compounds in South Korea, presence of Actinomycetes, their molecular characteristics and ability to produce odorous compounds were investigated in this study. Findings confirmed the presence of Actinomycetes in surface soil, sediment, and water samples from four sites: two artificial lakes [Paldang and Cheongpyeong (CP)], and two streams [Gyeongan (GA) and Yangpyeong]. Surface soil and sediment from CP area had the greatest concentration of Actinomycetes (8.2 x 10(7) and 6.8 x 10(6) colony forming units (CFUs)/gram, dry weight, respectively). When water samples are considered, samples from GA had the highest concentration (1.9 x 10(2) CFU/mL). 16S rRNA sequencing and molecular phylogenetic analysis showed that Streptomyces was the dominant genus (64.1%). In addition, the isolated Actinomycetes synthesized 5.4 ng/L geosmin as demonstrated by thermal desorption unit-gas chromatograph/mass spectrometry analysis.     

PASU: A personal area situation understanding system using wireless camera sensor networks

In this paper, we present a personal area situation understanding (PASU) system, a novel application of a smart device using wireless camera sensor networks. The portability of a PASU system makes it an attractive solution for monitoring and understanding the current situation of the personal area around a user. The PASU system allows its user to construct a 3D scene of the environment and view the scene from various vantage points for better understanding of the environment. The paper describes the architecture and implementation of the PASU system addressing limitations of wireless camera sensor networks, such as low bandwidth and limited computational capabilities. The capabilities of PASU are validated with extensive experiments. The PASU system demonstrates the potential of a portable system combining a smart device and a wireless camera sensor network for personal area monitoring and situation understanding.     

An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation

A new approach to the design of a neural network (NN) based navigator is proposed in which the mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigator can be optimized for any user-defined objective function through the use of an evolutionary algorithm. The motivation of this research is to develop an efficient methodology for general goal-directed navigation in generic indoor environments as opposed to learning specialized primitive behaviors in a limited environment. To this end, a modular NN has been employed to achieve the necessary generalization capability across a variety of indoor environments. Herein, each NN module takes charge of navigating in a specialized local environment, which is the result of decomposing the whole path into a sequence of local paths through clustering of all the possible environments. We verify the efficacy of the proposed algorithm over a variety of both simulated and real unstructured indoor environments using our autonomous mobile robot platform.     

Granular Neural Networks and Their Development Through Context-Based Clustering and Adjustable Dimensionality of Receptive Fields

In this study, we present a new architecture of a granular neural network and provide a comprehensive design methodology as well as elaborate on an algorithmic setup supporting its development. The proposed neural network relates to a broad category of radial basis function neural networks (RBFNNs) in the sense that its topology involves a collection of receptive fields. In contrast to the standard architectures encountered in RBFNNs, here we form individual receptive fields in subspaces of the original input space rather than in the entire input space. These subspaces could be different for different receptive fields. The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-means clustering while the information granules in the multidimensional input space are formed by using the so-called context-based fuzzy C-means, which takes into account the structure being already formed in the output space. The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization [genetic algorithms (GAs), to be more specific] to determine the optimal input subspaces. A series of numeric studies exploiting synthetic data and data coming from the Machine Learning Repository, University of California at Irvine, provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.     

Hybrid fuzzy set-based polynomial neural networks and their development with the aid of genetic optimization and information granulation

We introduce a new architecture of feed-forward neural networks called hybrid fuzzy set-based polynomial neural networks (HFSPNNs) that are composed of heterogeneous feed-forward neural networks such as polynomial neural networks (PNNs) and fuzzy set-based polynomial neural networks (FSPNNs). We develop their comprehensive design methodology by embracing mechanisms of genetic optimization and information granulation. The construction of information granulation-driven HFSPNN exploits fundamental technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the resulting information granulation-driven genetically optimized HFSPNN results from a synergistic usage of the hybrid system generated by combining original fuzzy set-based polynomial neurons (FSPNs)-based FSPNN with polynomial neurons (PNs)-based PNN. The design of the conventional genetically optimized HFPNN exploits the extended Group Method of Data Handling (GMDH) whose some essential parameters of the network being tuned with the use of genetic algorithms throughout the overall development process. Two general optimization mechanisms are explored. First, the structural optimization is realized via GAs while the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through extensive experimentation where we considered a number of modeling benchmarks (synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling).     

The fabrication of functional biosurface composed of iron storage protein, ferritin

A functional biosurface applicable to a biomemory device was fabricated using ferritin, which is one of the globular protein complexes consisting of 24 protein subunits, which can be classified as metalloproteins. For the fabrication of uniform ferritin layer, 11-MUA(11-mercaptoundecanoic acid) was used as a linker material. The formation of the ferritin layer was confirmed by surface plasmon resonance (SPR) spectroscopy, and the morphology of the immobilized ferritin was analyzed by scanning tunneling microscopy (STM). The electrochemical redox property investigation was accomplished by the cyclic voltammetry (CV) technique. These results of adsorbed ferritin on the modified electrode can be used for the fabrication of bioelectronics.     

Preparation and characterization of nanoporous polyimide films from triblock copolyimides

Nanoporous polyimide films were prepared from triblock copolyimides. A thermally labile polymer, poly(propylene glycol), was incorporated into polyimides based on 4,4-(hexafluoroisopropylidene)diphthalic anhydride to obtain triblock copolyimides. Nanofoams were formed by thermolysis of the labile block. The thermal properties of the copolymers were investigated by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The nanopores were characterized by scanning electron microscopy (SEM).     

Fabrication of silicon membrane using fusion bonding and two-step electrochemical etch-stopping

A new type of silicon membrane structure was fabricated using wafer fusion bonding and two-step electrochemical etch-stopping methods. An active wafer of p-type epi/n-type epi/p-type substrate was first elctrochemically etched to form a shallow cavity on the p-type epitaxial layer. Then, the cavity-formed side was fusionally bonded with p-type silicon working wafer and, afterwards, the p-type substrate of the active wafer part was removed by a second electrochemical etch-stopping leaving only the n-type membrane on the shallow cavity. Using the new membrane structure in mechanical sensors, more precise control of cavity depth and membrane thickness was achievable and the influence of crystalline imperfections on the sensing circuits located near the bonding seam was avoidable.     

Cooling of3He-4He dilute solution down to 97 μK. Thermal boundary resistance between dilute solution and metal powder

We have cooled a³He-⁴He dilute solution down to 97 K, which is the lowest temperature ever been achieved in a dilute mixture. However, there is no sign of the superfluid transition of³He quasiparticles in the solution. In the sub-millikelvin region, we have measured the thermal boundary resistance between the solutions and sintered metal powder as a function of temperature T. We find that the thermal boundary resistence is proportional to T^–2 below 1 mK and that the resistance shows a strong dependence on magnetic fields below 0.1 T. These results suggest that the magnetic coupling is dominant in this temperature region. We have also estimated the heat leak into the dilute solution. It is found that the heat leak is proportional to the power of one third of inverse time, and the main source of the heat leak is ascribed to the viscous movement of³He quasiparticles.     

Preparation of PbTiO3 thin films using an alkoxide-alkanolamine sol-gel system

The stable range of PbTiO₃ sol and the processing conditions of uniform thin films were investigated using a solution of titanium isopropoxide, three kinds of alkanolamines (monoethanolamine, diethanolamine, triethanolamine), lead acetate trihydrate and isopropanol. Depending on the sol state with various alkanolamine/alkoxide molar ratios, diethanolamine (DEA) was very effective in preparing uniform and dense oxide films through room-temperature reaction, owing to its superior stability during the hydrolysis and condensation reaction. Perovskite PbTiO₃ thin films were obtained on oxidized silicon wafer above 550 °C and completely pure films were obtained at 650 °C using DEA as a complexing agent. The dielectric constant and loss tangent of these thin films fired at 650 °C for 30 min were found to be 240 and 0.01 at 1 kHz, respectively.