Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing

Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies

Effects of Ketogenic Diets on Autistic Symptoms of Female EL Mice

The ketogenic diet (KD) is a restricted carbohydrate, high fat and sufficient protein metabolic therapy that elevates ketones as an alternative fuel source, and that reduces seizures in persons with epilepsy which is often comorbid with autism. Autism is characterized by communication deficits, decreased sociability and repetitive behaviours. A restrictive KD reverses symptoms in the BTBR mouse model of autism but its severity is a factor in its clinical applicability. In a study with the EL mouse model of epilepsy and autism, sex-dependent effects were found where only females displayed the behavioural effects of the KD. In the current study, a strict and a milder KD were tested on female EL mice to compare their effects on behavior, blood chemistry and body weight. This study investigated if increased ketones and lowered blood glucose were necessary for behavioural improvement. In order to do so, female EL mice were fed either a standard rodent chow control diet, the restrictive KD or the moderate KD from five weeks of age. At eight weeks of age, behavioral testing, using the 3-chamber test which measures sociability and self-directed repetitive behaviour (grooming), were conducted in order to determine whether autistic symptoms were still present. In addition, the social transmission of food preference test which measures sociability as well was carried out. Weight, blood glucose and ketone levels were also measured. The diets had very similar behavioural effects on the animals, increasing sociability and reducing repetitive behaviours. Interestingly, the moderate KD caused increased weight and did not lower blood glucose yet still improved autistic behaviours. This suggests that caloric restriction and lowered blood glucose may not be necessary for improved behaviours as had previously been thought. Also, a clinical strength KD may possibly be beneficial for autistic children and should be further studied

IMPLEMENTASI MODEL QUANTUM TEACHING UNTUK MENINGKATKAN HASIL BELAJAR PKn SISWA KELAS III SDN 015 TANDUN KECAMATAN TANDUN KABUPATEN ROKAN HULU

Civics learning at school is an easy and fun lesson when a teacher is able to aplly a model that attracts students to learn. But in reality so far there are still many teachers who use conventional learning models so that learning becomes boring and students do not participate in the learning process which results in low student learning outcomes. This study aims to determine the differences in PKn learning outcomes before and after implementing the Quantum Teaching model. The type of research used in this study is quantitative research with experimental methods. The form of this study was pre-experimental (non-design) ukwith one-group pretest-posttest design. This research was conducted in class III of SDN 015 Tandun, Tandun District, Rokan Hulu Regency, which amounted to 20 students. Based on the results of the study obtained an average pretest that is equal to 39.75. while the posttest average is 70.25. The average N-Gain value is 0.52 with moderate interpretation. The results of the t test are obtained that the tcount score is greater than t table or 58.08> 2.0930. This is in accordance with the Reject H0 criteria, if thitung> t table. This proves that there are significant differences in PKn learning outcomes of grade 3 students of SDN 015 Tandun between before implementing the quantum teaching model and after implementing the quantum teaching model

Navigating Childhood Health: Unraveling the Tapestry of Anthropometric Indicators and Musculoskeletal Fitness in Elementary School Boys

Introduction: Childhood serves as the foundational bedrock shaping future health and well-being, with the musculoskeletal system playing a pivotal role in overall physical development. This study investigated the intricate relationship between anthropometric indicators and musculoskeletal fitness among boys aged 9-12 years, illuminating the nuances of this crucial developmental phase. Methods: A cohort of 100 boys from Wheaton International Schools underwent comprehensive anthropometric measurements, encompassing height, weight, BMI, waist circumference, body fat percentage, and skinfold assessments. Their musculoskeletal fitness was evaluated through diverse physical fitness tests, including Sit and Reach, Push-Up, Standing Long Jump, and Shuttle Run. Descriptive statistics were utilized to present the mean values and standard deviations of the collected anthropometric indicators, providing insights into the physical attributes and body composition of the boys. Subsequently, correlation analysis was performed between these anthropometric indicators and the physical fitness tests to understand their relationships. Results: Age showcased inverse relationships with flexibility and agility, while height emerged as a predominant influencer across all physical tests. BMI exhibited multifaceted impacts on various aspects of physical capabilities, shedding light on its potential implications for musculoskeletal health. The discussion extrapolates upon these correlations, elucidating age-related changes during adolescence, the profound influence of height on overall physical performance, and the intricate associations between body composition metrics and specific physical abilities. These insights foster a deeper understanding of childhood health and pave the way for targeted interventions in youth fitness programs. Conclusion: This study's revelations underscore the significance of anthropometric markers in assessing musculoskeletal fitness among elementary school boys, offering valuable insights into the interplay between physical attributes and functional capabilities. These findings lay the groundwork for informed strategies aimed at nurturing optimal musculoskeletal health in the formative years, thereby shaping healthier futures for the upcoming generation.Introducción: La infancia es la base fundamental que da forma a la salud y el bienestar futuros, y el sistema musculoesquelético desempeña un papel fundamental en el desarrollo físico general. Este estudio investigó la intrincada relación entre los indicadores antropométricos y la aptitud musculoesquelética entre niños de 9 a 12 años, iluminando los matices de esta fase crucial del desarrollo. Métodos: Una cohorte de 100 niños de Wheaton International Schools se sometió a mediciones antropométricas integrales, que abarcaron altura, peso, IMC, circunferencia de la cintura, porcentaje de grasa corporal y evaluaciones de pliegues cutáneos. Su aptitud musculoesquelética se evaluó mediante diversas pruebas de aptitud física, incluidas Sit and Reach, Push-Up, Standing Long Jump y Shuttle Run. Se utilizaron estadísticas descriptivas para presentar los valores medios y las desviaciones estándar de los indicadores antropométricos recopilados, proporcionando información sobre los atributos físicos y la composición corporal de los niños. Posteriormente, se realizó un análisis de correlación entre estos indicadores antropométricos y las pruebas de aptitud física para comprender sus relaciones. Resultados: La edad mostró relaciones inversas con la flexibilidad y la agilidad, mientras que la altura surgió como un factor de influencia predominante en todas las pruebas físicas. El IMC mostró impactos multifacéticos en varios aspectos de las capacidades físicas, arrojando luz sobre sus posibles implicaciones para la salud musculoesquelética. La discusión extrapola estas correlaciones, aclarando los cambios relacionados con la edad durante la adolescencia, la profunda influencia de la altura en el rendimiento físico general y las intrincadas asociaciones entre las métricas de composición corporal y las habilidades físicas específicas. Estos conocimientos fomentan una comprensión más profunda de la salud infantil y allanan el camino para intervenciones específicas en programas de acondicionamiento físico para jóvenes. Conclusión: Las revelaciones de este estudio subrayan la importancia de los marcadores antropométricos en la evaluación de la aptitud musculoesquelética entre niños de escuela primaria, ofreciendo información valiosa sobre la interacción entre los atributos físicos y las capacidades funcionales. Estos hallazgos sientan las bases para estrategias informadas destinadas a fomentar una salud musculoesquelética óptima en los años de formación, configurando así futuros más saludables para la próxima generación

Remote Sensing of Coastal Wetlands: Long term vegetation stress assessment and data enhancement technique

Apalachicola Bay in the Florida panhandle is home to a rich variety of salt water and freshwater wetlands but unfortunately is also subject to a wide range of hydrologic extreme events. Extreme hydrologic events such as hurricanes and droughts continuously threaten the area. The impact of hurricane and drought on both fresh and salt water wetlands was investigated over the time period from 2000 to 2015 in Apalachicola Bay using spatio-temporal changes in the Landsat based NDVI. Results indicate that salt water wetlands were more resilient than fresh water wetlands. Results also suggest that in response to hurricanes, the coastal wetlands took almost a year to recover while recovery following a drought period was observed after only a month. This analysis was successful and provided excellent insights into coastal wetland health. Such long term study is heavily dependent on optical sensor that is subject to data loss due to cloud coverage. Therefore, a novel method is proposed and demonstrated to recover the information contaminated by cloud. Cloud contamination is a hindrance to long-term environmental assessment using information derived from satellite imagery that retrieve data from visible and infrared spectral ranges. Normalized Difference Vegetation Index (NDVI) is a widely used index to monitor vegetation and land use change. NDVI can be retrieved from publicly available data repositories of optical sensors such as Landsat, Moderate Resolution Imaging Spectro-radiometer (MODIS) and several commercial satellites. Landsat has an ongoing high resolution NDVI record starting from 1984. Unfortunately, the time series NDVI data suffers from the cloud contamination issue. Though simple to complex computational methods for data interpolation have been applied to recover cloudy data, all the techniques are subject to many limitations. In this paper, a novel Optical Cloud Pixel Recovery (OCPR) method is proposed to repair cloudy pixels from the time-space-spectrum continuum with the aid of a machine learning tool, namely random forest (RF) trained and tested utilizing multi-parameter hydrologic data. The RF based OCPR model was compared with a simple linear regression (LR) based OCPR model to understand the potential of the model. A case study in Apalachicola Bay is presented to evaluate the performance of OCPR to repair cloudy NDVI reflectance for two specific dates. The RF based OCPR method achieves a root mean squared error of 0.0475 sr?1 between predicted and observed NDVI reflectance values. The LR based OCPR method achieves a root mean squared error of 0.1257 sr?1. Findings suggested that the RF based OCPR method is effective to repair cloudy values and provide continuous and quantitatively reliable imagery for further analysis in environmental applications

Sparse matrix based power flow solver for real-time simulation of power system

Analyzing a massive number of Power Flow (PF) equations even on almost identical or similar network topology is a highly time-consuming process for large-scale power systems. The major computation time is hoarded by the iterative linear solving process to solve nonlinear equations until convergence is achieved. This is a paramount concern for any PF analysis methods. This thesis presents a sparse matrix-based power flow solver that is fast enough to be implemented in the real-time analysis of largescale power systems. It uses KLU, a sparse matrix solver, for PF analysis. It also implements parallel processing of CPU and GPU which enables the simultaneous computation of multiple blocks in the algorithm leading to faster execution. It runs 1000 times and 200 times faster than newton raphson method for DC and AC power system respectively. On average, it is around 10 times faster than MATPOWER for both AC and DC power system

Endogenous Risk Perception, Geospatial Characteristics and Temporal Variation in Hurricane Evacuation Behavior

The main focus of this thesis was to gain a better understanding about the dynamics of risk perception and its influence on people’s evacuation behavior. Another major focus was to improve our knowledge regarding geo-spatial and temporal variations of risk perception and hurricane evacuation behavior. A longitudinal dataset of more than eight hundred households were collected following two major hurricane events, Ivan and Katrina. The longitudinal survey data was geocoded and a geo-spatial database was integrated to it. The geospatial database was composed of distance, elevation and hazard parameters with respect to the respondent’s household location. A set of Bivariate Probit (BP) model suggests that geospatial variables have had significant influences in explaining hurricane risk perception and evacuation behavior during both hurricanes. The findings also indicated that people made their evacuation decision in coherence with their risk perception. In addition, people updated their hurricane evacuation decision in a subsequent similar event

Sparse matrix based power flow solver for real-time simulation of power system

Analyzing a massive number of Power Flow (PF) equations even on almost identical or similar network topology is a highly time-consuming process for large-scale power systems. The major computation time is hoarded by the iterative linear solving process to solve nonlinear equations until convergence is achieved. This is a paramount concern for any PF analysis methods. This thesis presents a sparse matrix-based power flow solver that is fast enough to be implemented in the real-time analysis of largescale power systems. It uses KLU, a sparse matrix solver, for PF analysis. It also implements parallel processing of CPU and GPU which enables the simultaneous computation of multiple blocks in the algorithm leading to faster execution. It runs 1000 times and 200 times faster than newton raphson method for DC and AC power system respectively. On average, it is around 10 times faster than MATPOWER for both AC and DC power system