Improving urban park design: A comparative and analytical study addressing resources available and components necessary to improve the environmental and economic sustainability of urban parks

Urban parks have long been valued for the environmental, social, and economic benefits they provide. Increasingly, parks are also being recognized as features important for sustainable city design. This Capstone Project will identify, compare, analyze, and discuss means for designing sustainable urban parks. Recommendations for designing sustainable urban parks, based on project results, include: 1) ensure park features will support high levels of human activity; 2) use gravel to construct park trails; 3) purchase playground structures made of recycled materials; 4) plant a high number of perennials in flowerbeds and other vegetated areas; 5) plant climate-appropriate plants in vegetated areas; 6) ensure parks have high levels of plant diversity; and 7) develop future studies further exploring sustainable park design.

Analysis of the Convention on International Trade in Endangered Species (CITES) and the Endangered Species Act (ESA) and Its Effects on Giant Panda Conservation

The giant panda, Ailuropoda melanoleuca is an endangered species that is protected under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and the Endangered Species Act (ESA). Numerous factors have led to a decline in giant panda populations in China including habitat loss from human activity, poaching, panda inbreeding and a low reproductive rate. This capstone analyzes the effects of CITES and ESA as policies for the protection of panda populations and their habitat. CITES and ESA provide some protection for panda populations in the United States. However, these policies do not address panda habitat protection in China.

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.