Artificial reefs concentrate nutrients and alter benthic community structure in an oligotrophic, subtropical estuary

The construction of artificial reefs in the oligotrophic seagrass meadows of central Florida Bay attracted large aggregations of fish and invertebrates, and assays of nutrient availability indicated increases in availability of nutrients to sediment microalgae, periphyton, and seagrasses around reefs. An average of 37.8 large (> 10 cm) mobile animals were observed on each small artificial reef. The dominant fish species present was the gray snapper (Lutjanus griseus Linnaeus, 1758). Four yrs after the establishment of the artificial reefs, microphytobenthos abundance was twice as high in reef plots (1.7 ± 0.1 μg chl-a cm-2) compared to control plots (0.9 ± 0.1 μg chl-a cm-2). The accumulation of periphyton on glass periphytometers was four times higher in artificial reef plots (200.1 ± 45.8 mg chl-a m-2) compared to control plots (54.8 ± 6.8 mg chl-a m-2). The seagrass beds surrounding the artificial reefs changed rapidly, from a sparse Thalassia testudinum (Banks & Soland. ex König) dominated community, which persisted at control plots, to a community dominated by Halodule wrightii (Ascherson). Such changes mirror the changes induced in experimentally fertilized seagrass beds in Florida, strongly suggesting that the aggregations of animals attracted by artificial reefs concentrated nutrients in this oligotrophic seascape, favoring the growth of fast-growing primary producers like microphytobenthos and periphyton, and changing the competitively dominant seagrass from slow-growing T. testudinum to faster-growing H. wrightii in the vicinity of the reefs.

Modeling, simulation, and characterization of space debris in low-Earth orbit

Every space launch increases the overall amount of space debris. Satellites have limited awareness of nearby objects that might pose a collision hazard. Astrometric, radiometric, and thermal models for the study of space debris in low-Earth orbit have been developed. This modeled approach proposes analysis methods that provide increased Local Area Awareness for satellites in low-Earth and geostationary orbit. Local Area Awareness is defined as the ability to detect, characterize, and extract useful information regarding resident space objects as they move through the space environment surrounding a spacecraft. The study of space debris is of critical importance to all space-faring nations. Characterization efforts are proposed using long-wave infrared sensors for space-based observations of debris objects in low-Earth orbit. Long-wave infrared sensors are commercially available and do not require solar illumination to be observed, as their received signal is temperature dependent. The characterization of debris objects through means of passive imaging techniques allows for further studies into the origination, specifications, and future trajectory of debris objects. Conclusions are made regarding the aforementioned thermal analysis as a function of debris orbit, geometry, orientation with respect to time, and material properties. Development of a thermal model permits the characterization of debris objects based upon their received long-wave infrared signals. Information regarding the material type, size, and tumble-rate of the observed debris objects are extracted. This investigation proposes the utilization of long-wave infrared radiometric models of typical debris to develop techniques for the detection and characterization of debris objects via signal analysis of unresolved imagery. Knowledge regarding the orbital type and semi-major axis of the observed debris object are extracted via astrometric analysis. This knowledge may aid in the constraint of the admissible region for the initial orbit determination process. The resultant orbital information is then fused with the radiometric characterization analysis enabling further characterization efforts of the observed debris object. This fused analysis, yielding orbital, material, and thermal properties, significantly increases a satellite's Local Area Awareness via an intimate understanding of the debris environment surrounding the spacecraft.

Enhancing physical therapy students' attitudes toward telecommunication technology by using a demonstration model

The purpose of this study was to assess and enhance the attitudes and knowledge of physical therapy students toward telecommunication technology. A questionnaire was given to appraise the attitudes and knowledge of 156 physical therapy students toward telecommunication technology. The intervention was a one hour presentation on applications relevant to physical therapy practice. The majority of students expressed interest in telecommunication before the presentation, and felt that expanded use of telecommunication was important to the profession. However, only a minority of students demonstrated knowledge about specific medical telecommunication applications. The post-intervention questionnaire showed the presentation to be effective in changing students' attitudes toward telecommunication, and increasing their knowledge relevant to the practice of physical therapy. If physical therapy curricula were to include exposure to telecommunication, perhaps physical therapists will be more inclined to use the technology in the future.

Modeling, Simulation, and Characterization of Space Debris in low-Earth Orbit

Every space launch increases the overall amount of space debris. Satellites have limited awareness of nearby objects that might pose a collision hazard. Astrometric, radiometric, and thermal models for the study of space debris in low-Earth orbit have been developed. This modeled approach proposes analysis methods that provide increased Local Area Awareness for satellites in low-Earth and geostationary orbit. Local Area Awareness is defined as the ability to detect, characterize, and extract useful information regarding resident space objects as they move through the space environment surrounding a spacecraft. The study of space debris is of critical importance to all space-faring nations. Characterization efforts are proposed using long-wave infrared sensors for space-based observations of debris objects in low-Earth orbit. Long-wave infrared sensors are commercially available and do not require solar illumination to be observed, as their received signal is temperature dependent. The characterization of debris objects through means of passive imaging techniques allows for further studies into the origination, specifications, and future trajectory of debris objects. Conclusions are made regarding the aforementioned thermal analysis as a function of debris orbit, geometry, orientation with respect to time, and material properties. Development of a thermal model permits the characterization of debris objects based upon their received long-wave infrared signals. Information regarding the material type, size, and tumble-rate of the observed debris objects are extracted. This investigation proposes the utilization of long-wave infrared radiometric models of typical debris to develop techniques for the detection and characterization of debris objects via signal analysis of unresolved imagery. Knowledge regarding the orbital type and semi-major axis of the observed debris object are extracted via astrometric analysis. This knowledge may aid in the constraint of the admissible region for the initial orbit determination process. The resultant orbital information is then fused with the radiometric characterization analysis enabling further characterization efforts of the observed debris object. This fused analysis, yielding orbital, material, and thermal properties, significantly increases a satellite’s Local Area Awareness via an intimate understanding of the debris environment surrounding the spacecraft.

Resource allocation in multi-user telecommunication networks

This dissertation discussed resource allocation mechanisms in several network topologies including infrastructure wireless network, non-infrastructure wireless network and wire-cum-wireless network. Different networks may have different resource constrains. Based on actual technologies and implementation models, utility function, game theory and a modern control algorithm have been introduced to balance power, bandwidth and customers' satisfaction in the system. ^ In infrastructure wireless networks, utility function was used in the Third Generation (3G) cellular network and the network was trying to maximize the total utility. In this dissertation, revenue maximization was set as an objective. Compared with the previous work on utility maximization, it is more practical to implement revenue maximization by the cellular network operators. The pricing strategies were studied and the algorithms were given to find the optimal price combination of power and rate to maximize the profit without degrading the Quality of Service (QoS) performance. ^ In non-infrastructure wireless networks, power capacity is limited by the small size of the nodes. In such a network, nodes need to transmit traffic not only for themselves but also for their neighbors, so power management become the most important issue for the network overall performance. Our innovative routing algorithm based on utility function, sets up a flexible framework for different users with different concerns in the same network. This algorithm allows users to make trade offs between multiple resource parameters. Its flexibility makes it a suitable solution for the large scale non-infrastructure network. This dissertation also covers non-cooperation problems. Through combining game theory and utility function, equilibrium points could be found among rational users which can enhance the cooperation in the network. ^ Finally, a wire-cum-wireless network architecture was introduced. This network architecture can support multiple services over multiple networks with smart resource allocation methods. Although a SONET-to-WiMAX case was used for the analysis, the mathematic procedure and resource allocation scheme could be universal solutions for all infrastructure, non-infrastructure and combined networks. ^

In vivo biocompatibility evaluation of composite polymeric materials for use in a novel artificial heart valve

A novel trileaflet polymer valve is a composite design of a biostable polymer poly(styrene-isobutylene-styrene) (SIBS) with a reinforcement polyethylene terephthalate (PET) fabric. Surface roughness and hydrophilicity vary with fabrication methods and influence leaflet biocompatibility. The purpose of this study was to investigate the biocompatibility of this composite material using both small animal (nonfunctional mode) and large animal (functional mode) models. Composite samples were manufactured using dip coating and solvent casting with different coating thickness (251μm and 50μm). Sample's surface was characterized through qualitative SEM observation and quantitative surface roughness analysis. A novel rat abdominal aorta model was developed to test the composite samples in a similar pulsatile flow condition as its intended use. The sample's tissue response was characterized by histological examination. Among the samples tested, the 25μm solvent-cast sample exhibited the smoothest surface and best biocompatibility in terms of tissue capsulation thickness, and was chosen as the method for fabrication of the SIBS valve. Phosphocholine was used to create a hydrophilic surface on selected composite samples, which resulted in improved blood compatibility. Four SIBS valves (two with phosphocholine modification) were implanted into sheep. Echocardiography, blood chemistry, and system pathology were conducted to evaluate the valve's performance and biocompatibility. No adverse response was identified following implantation. The average survival time was 76 days, and one sheep with the phosphocholine modified valve passed the FDA minimum requirement of 140 days with approximately 20 million cycles of valve activity. The explanted valves were observed under the aid of a dissection microscope, and evaluated via histology, SEM and X-ray. Surface cracks and calcified tissue deposition were found on the leaflets. In conclusion, we demonstrated the applicability of using a new rat abdominal aorta model for biocompatibility assessment of polymeric materials. A smooth and complete coating surface is essential for the biocompatibility of PET/SIBS composite, and surface modification using phosphocholine improves blood compatibility. Extrinsic calcification was identified on the leaflets and was associated with regions of surface cracks.

A research platform for artificial neural networks with applications in pediatric epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and nonepileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that (1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and (2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Water resources assessment and geographic information system (GIS)-based stormwater runoff estimates for artificial recharge of freshwater aquifers in New Providence, Bahamas

The Bahamas is a small island nation that is dealing with the problem of freshwater shortage. All of the country’s freshwater is contained in shallow lens aquifers that are recharged solely by rainfall. The country has been struggling to meet the water demands by employing a combination of over-pumping of aquifers, transport of water by barge between islands, and desalination of sea water. In recent decades, new development on New Providence, where the capital city of Nassau is located, has created a large area of impervious surfaces and thereby a substantial amount of runoff with the result that several of the aquifers are not being recharged. A geodatabase was assembled to assess and estimate the quantity of runoff from these impervious surfaces and potential recharge locations were identified using a combination of Geographic Information Systems (GIS) and remote sensing. This study showed that runoff from impervious surfaces in New Providence represents a large freshwater resource that could potentially be used to recharge the lens aquifers on New Providence.

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).