MODIS imagery of turbid plumes in San Diego coastal waters during rainstorm events

Data of normalized water-leaving radiance, nLw, obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite at spatial resolution of 250 m (band 1 centered at 645 nm) and 500 m (band 4 at 555 nm) are used to study turbid plumes in coastal waters of southern California during rainstorm events in winter of 2004-2005. Our study area includes San Diego coastal waters, which extend approximately 25 km offshore between Point Loma and 10 km south of the US-Mexican border. These waters are influenced by terrigenous input of particulate and dissolved materials from San Diego and Tijuana watersheds and non-point sources along the shore. Optimum threshold values of satellite-derived normalized water-leaving radiances at both wavebands were established for distinguishing the plume from ambient ocean waters. These threshold values were determined by searching for a maximum correlation between the estimates of satellite-derived plume area calculated using a broad range of nLw values and the environmental variables characterizing rainfall, river discharge, wind, and tides. A correlation analysis involving the amount of precipitated water accumulated during a storm event over the San Diego and Tijuana watersheds was selected as the basis for final determinations of the optimum threshold nLw_thr and subsequent calculations of the plume area. By applying this method to a sequence of MODIS imagery, we demonstrate the spatial extent and evolution of the plume during rainstorm events under various conditions of precipitation, river discharge, wind forcing, and coastal currents.     

Conjugate mixture models for clustering multimodal data

The problem of multimodal clustering arises whenever the data are gathered with several physically different sensors. Observations from different modalities are not necessarily aligned in the sense there there is no obvious way to associate or compare them in some common space. A solution may consist in considering multiple clustering tasks independently for each modality. The main difficulty with such an approach is to guarantee that the unimodal clusterings are mutually consistent. In this letter, we show that multimodal clustering can be addressed within a novel framework: conjugate mixture models. These models exploit the explicit transformations that are often available between an unobserved parameter space (objects) and each of the observation spaces (sensors). We formulate the problem as a likelihood maximization task and derive the associated conjugate expectation-maximization algorithm. The convergence properties of the proposed algorithm are thoroughly investigated. Several local and global optimization techniques are proposed in order to increase its convergence speed. Two initialization strategies are proposed and compared. A consistent model selection criterion is proposed. The algorithm and its variants are tested and evaluated within the task of 3D localization of several speakers using both auditory and visual data.     

Performance-friendly rule extraction in large water data-sets with AOC posets and relational concept analysis

In this paper, we consider data analysis methods for knowledge extraction from large water data-sets. More specifically, we try to connect physico-chemical parameters and the characteristics of taxons living in sample sites. Among these data analysis methods, we consider formal concept analysis (FCA), which is a recognized tool for classification and rule discovery on object–attribute data. Relational concept analysis (RCA) relies on FCA and deals with sets of object–attribute data provided with relations. RCA produces more informative results but at the expense of an increase in complexity. Besides, in numerous applications of FCA, the partially ordered set of concepts introducing attributes or objects (AOC poset, for Attribute–Object–Concept poset) is used rather than the concept lattice in order to reduce combinatorial problems. AOC posets are much smaller and easier to compute than concept lattices and still contain the information needed to rebuild the initial data. This paper introduces a variant of the RCA process based on AOC posets rather than concept lattices. This approach is compared with RCA based on iceberg lattices. Experiments are performed with various scaling operators, and a specific operator is introduced to deal with noisy data. We show that using AOC poset on water data-sets provides a reasonable concept number and allows us to extract meaningful implication rules (association rules whose confidence is 1), whose semantics depends on the chosen scaling operator.     

A novel optimization-based bandwidth-aware minimum power multicast routing algorithm in green wireless networks

Multicast routing in wireless networks that possess the wireless multicast advantage could significantly reduce the power and energy consumption. However, this kind of multicast routing that only addresses the transmission radius coverage might not be able to meet the bandwidth requirement of the users. As a result, additional transmissions are required to incur more energy consumption and carbon dioxide emissions that make existing algorithms not applicable to bandwidth constrained applications. In this paper, for the first time, we address the bandwidth aware minimum power multicast routing problem in wireless networks where the objective function is to minimize the total power consumption subject to the users?? bandwidth requirements. This problem is a challenging cross-layer design problem that requires seamless and sophisticated integrated design in the network layer (multicast routing) and physical layer (bandwidth-aware wireless transmission and power control). We first formulate this problem as a mixed integer linear programming problem and then propose a Lagrangian relaxation based algorithm to solve this problem. Numerical results demonstrate that the proposed approach is a sound green networking algorithm that outperforms the existing power efficient multicast routing approaches under all tested cases, especially in large bandwidth request, fine radius granularity, large group size and sparse network.     

Enablers That Help Foreign Architectural, Engineering, and Construction Firms Win Construction Contracts in China

With China’s fast economic growth and entry into the World Trade Organization (WTO), more foreign architectural, engineering, and construction (AEC) firms are expected to venture into this huge and attractive market. This paper explores the important strategies to help foreign AEC firms clinch construction projects in China. Using a questionnaire survey approach complemented by in-depth interviews, it was found that firms need to pay more attention to the market and working conditions in China and to strengthen their own capabilities. To win projects in China, foreign AEC firms should also be able to offer project financing services. The findings are useful to foreign AEC firms because it may help them win projects in the Chinese construction industry.     

Nano-structured zinc oxide–cotton fibers: synthesis, characterization and applications

Zinc oxide nanoparticles were prepared and subsequently deposited onto the surface of the cotton fiber by ultrasonic irradiation. The optical, structure and morphology of the coated and un-coated cotton were examined by UV, fourier transform infrared spectroscopy, X-ray diffraction analysis (XRD) and scanning electron microscope (SEM)/Energy Dispersive X-ray analysis. XRD analysis revealed the presence of the crystalline metal oxide of hexagonal phase with an average crystallite size of 12 nm. These nanoparticles are probably physically adsorbed onto the cotton fiber surface. SEM analysis showed a distribution of ZnO nanorod assemblies of various diameters and lengths physically adsorbed onto the cotton fiber surface may take place. The ZnO-cotton fiber nano-composite were tested against Escherichia coli (gram negative) and Staphylococcus aureus (gram positive) cultures, and showed a significant antimicrobial activity.     

Template Synthesis of Iminodiacetic Acid Polysiloxane Immobilized Ligand Systems and their Metal Uptake Capacity

Porous solid polysiloxane ligand systems bearing an iminodiacetic acid chelating ligand of the general formula P-IDA (where P- represents [Si-O] _n polysiloxane network and IDA represent an organofunctional group) were achieved in two-step reaction. The first step includes a template synthesis of 3-iodopropylpolysiloxane (P-I) or 3-aminopropylpolysiloxane (P-MA) by hydrolytic polycondensation of TEOS and the corresponding silane agent in the presence of CTAB as a surfactant. The second step includes a modification of 3-iodopropylpolysiloxane with diethyliminodiacetate or 3-aminopropylpolysiloxane with ethylchloroacetate to produce IDA-functionalized polysiloxane ligand systems P-IDA-I & P-IDA-II respectively. It was found that the modified IDA-functionalized polysiloxane ligand systems exhibited higher capacities for uptake of the metal ions (Ni ²⁺, Cu ²⁺ and Pb ²⁺) than those prepared without surfactants.