Deep-sea benthic ecosystems remain poorly studied in South Africa, limiting understanding of community biodiversity patterns and their environmental drivers. This is one of the first studies to (i) visually investigate marine epifaunal community patterns and their environmental drivers along the Agulhas ecoregion outer shelf, shelf edge and upper slope to support marine ecosystem classification and mapping, and (ii) to determine the conservation targets for selected national marine ecosystem types to inform improved management of the marine environment, through Marine Spatial Planning processes. Visual surveys of the seabed were conducted to quantify epifauna during the ACEP Deep Secrets Cruise in 2016, using a towed benthic camera system. Twenty-nine sites were sampled, ranging from 120-700 m in depth and spanning the shelf-slope transition from the western edge of the Agulhas Bank to offshore of the Kei River mouth. A total of 855 seabed images were processed, and 173 benthic taxa quantified. Corresponding environmental variables were used to determine potential drivers of observed biodiversity patterns. Data were analysed using multivariate analyses, including CLUSTER, MDS and DistLM, in PRIMER v6 with PERMANOVA. Ten different epifaunal communities were classified and described with key characteristic taxa identified. Communities found in habitats that comprised mostly hard rocky substrata generally exhibited higher in species richness and were most commonly characterized by stalked crinoids, various corals and bryozoans, whereas communities found in habitats comprising unconsolidated sediment were lower in species richness and commonly characterized by polychaetes, cerianthids and brittle stars. Communities found in habitats comprising both hard and soft substrata had a mix of the above-mentioned epifauna. The distribution of these communities was mostly influenced by substratum type, longitude, trawling intensity, depth, and presence of visible particulate organic matter. The combined interactions of topography, substratum and the unique hydrodynamic conditions along the Agulhas ecoregion shelf-slope transition are likely responsible for the observed patterns. The observed community patterns were also compared to the existing classification of marine ecosystem types from the 2018 National Biodiversity Assessment. Fine-scale heterogeneity was revealed within the examined marine ecosystem types, particularly with substratum type and associated community variability and should be recognized and incorporated into future iterations of the national marine ecosystem classification and map. Species-area curves were used to calculate conservation targets for three ecosystem types, defined by the 2018 National Biodiversity Assessment, namely the Agulhas Coarse Sediment Shelf Edge, South West Indian Upper Slope, and the Agulhas Rocky Shelf Edge. Considering the epifaunal species richness (using the bootstrap estimator) and area, per image and per ecosystem type, the rate of accumulation of species was calculated and used to estimate the percentage of species expected to be represented by any given percentage of protected ecosystem type area. Between 20 and 30% of the area within these ecosystem types will need to be protected to represent 80% of the species. This study has shown that an integration of environmental parameters together with biodiversity measures to better understand and classify offshore benthic ecosystems has worked well. However, to improve the resolution of the national marine ecosystem classification and map, there needs to be greater input of fine-scale biological and environmental sampling and mapping of substratum types across the Agulhas ecoregion shelf-slope transition zone. This work is contributing to improvements in the national marine ecosystem classification and map and hence the spatial assessment and planning processes that rely on these products.

The Edge of Things is what I like to call a love song to the dispossessed. Each of the eight stories in the collection is an examination of the lives of women who are exiled from modern American consumer culture, whether by circumstance or by choice. This separation brings them heartache, risk, and sometimes even hope. The collection is fueled by the landscape of Florida, observed at its most beautiful and most corrupted, from highways, landfills, and trailer parks to housing developments, gardens, and secret forests. Setting is a constant source of revelation, the external landscape offering insight into the internal struggles of the characters. Regardless of age, race, or sexual orientation, the women of The Edge of Things find themselves moving toward, or just past, incredible changes in their lives. In "Seed of the Golden Mango", "Raising the Dead", and "The Girl Who Loved Bugs", young women deal with the loss of loved ones. The women of "Zyczenie", "It Cannot Hold", and "Wasp Honey" must deal with old losses in order to survive the realities of the outside world that they have long ignored. "The Edge of Things" and "The Secret Letters" both deal with love, and the consequences of an inability to communicate. In each of these tales I hope to present unforgettable characters, women whose journeys will haunt, reminding readers that on some level, the love song of the dispossessed calls to us all.

Three-dimensional computer models, especially geospatial architectural data sets, can be visualized in the same way humans experience the world, providing a realistic, interactive experience. Scene familiarization, architectural analysis, scientific visualization, and many other applications would benefit from finely detailed, high resolution, 3D models. Automated methods to construct these 3D models traditionally has produced data sets that are often low fidelity or inaccurate; otherwise, they are initially highly detailed, but are very labor and time intensive to construct. Such data sets are often not practical for common real-time usage and are not easily updated. This thesis proposes Variable Resolution & Dimensional Mapping (VRDM), a methodology that has been developed to address some of the limitations of existing approaches to model construction from images. Key components of VRDM are texture palettes, which enable variable and ultra-high resolution images to be easily composited; texture features, which allow image features to integrated as image or geometry, and have the ability to modify the geometric model structure to add detail. These components support a primary VRDM objective of facilitating model refinement with additional data. This can be done until the desired fidelity is achieved as practical limits of infinite detail are approached. Texture Levels, the third component, enable real-time interaction with a very detailed model, along with the flexibility of having alternate pixel data for a given area of the model and this is achieved through extra dimensions. Together these techniques have been used to construct models that can contain GBs of imagery data.

A combination of factors including land use change and fire suppression has resulted in the loss of pine savanna habitats across the southeastern U.S., affecting many avian species dependent on these habitats. However, due to the ephemeral nature of the habitat requirements of many pine savanna species (e.g., habitat is only present for a couple of years after a fire), targeted management of such habitats can be challenging. Moreover, the growing numbers of imperiled pine savanna species can make prioritizing management difficult. One potential tool to better inform management of pine savanna species is satellite imagery. Sentinel-2 satellite imagery data provides an instantaneous snapshot of habitat quality at a high resolution and across a large geographic area, which may make it more efficient than traditional, ground-based vegetation surveying. Thus, the objectives of my research were to 1) evaluate the use of remote sensing technology to predict habitat occupancy for pine savanna species, and 2) use satellite imagery-based models to inform multispecies management in a pine savanna habitat. To meet my objectives, I conducted point count surveys and built predictive models for three pine savanna bird species: Bachman's Sparrow (BACS; Peuacea aestivalis), Northern Bobwhite (NOBO; Colinus virginianus), and Red-Cockaded Woodpecker (RCW; Dryobates borealis) across Georgia. I assessed the performance of satellite imagery in predicting habitat occupancy of these pine savanna species and its potential for multispecies management. I found that models created using satellite imagery habitat metric data performed well at predicting the occupancy of all three species as measured by the Area Under the Receiver Operating Characteristic Curve: BACS=0.84, NOBO=0.87, RCW=0.76 (with values between 0.7-1 defined as acceptable or good predictive capacity). For BACS and NOBO, I was able to compare these satellite imagery models to field-based models, and satellite models performed better than those using traditional vegetation survey data (BACS=0.80, NOBO=0.79). Moreover, I found that satellite imagery data provided useful insights into the potential for multispecies management within the pine savanna habitats of Georgia. Finally, I found differences in the habitat selected by BACS, NOBO, and RCW, and that BACS may exhibit spatial variations in habitat use. The results of this study have significant implications for the conservation of pine savanna species, demonstrating that satellite imagery can allow users to build reliable occupancy models and inform multispecies management without intensive vegetation surveying. / Master of Science / Land-use changes have resulted in the disruption of natural disturbances such as fires, resulting in the loss of pine savanna habitats throughout the southeastern U.S. Although many of the species that occupy these habitats are experiencing rapid population declines, habitat for pine savanna species can be challenging to manage. Without reoccurring fire, pine savanna habitat can become unsuitable for obligate species within short periods of time, forcing these species to disperse to newly disturbed habitats. The transient nature of the preferred habitat of pine savanna species makes targeting management for these species difficult, as it can be challenging to locate exactly where occupied habitats exist. Furthermore, as the number of pine savanna species that are declining is large, prioritizing management of these species can be difficult especially given limited conservation funding. One potential tool to better inform the management of pine savanna species is satellite imagery. Satellite imagery can capture habitat information across broad areas, at fine resolutions, and at frequent intervals, potentially making satellite imagery more efficient than conducting field vegetation surveys on the ground for gaining information on habitat suitability. Thus, the objectives of my research were to 1) determine if satellite imagery can effectively predict the habitats occupied by pine savanna species (habitat occupancy), and 2) use satellite imagery-based models to inform the simultaneous management of multiple species (multispecies management) in a pine savanna habitat. To meet these objectives, I conducted surveys and built predictive models for three pine savanna bird species: Bachman's sparrow (BACS; Peuacea aestivalis), Northern Bobwhite (NOBO; Colinus virginianus), and Red-Cockaded Woodpecker (RCW; Dryobates borealis) in Georgia. I found models informed by satellite imagery performed well at predicting habitats occupied for all three species. Furthermore, models developed using satellite imagery performed better at predicting the habitats occupied by pine savanna species than models developed using on the ground vegetation surveys. I also found that satellite imagery data provided useful insights into strategies to manage pine savanna species simultaneously. I found evidence that BACS, NOBO, and RCW may have contrasting habitat needs and that BACS may use habitat differently between sites in Georgia. The results of this study demonstrate that satellite imagery can be used to predict the habitats occupied by pine savanna species and inform multispecies management without surveying vegetation on the ground, which is a more efficient use of time and funding.

No description available.

Northern bobwhite (Colinus virginianus) monitoring (e.g., covey-call surveys) is labor-intensive and imprecise. We evaluated the influence of bobwhite covey size and cover type on covey detectability when surveyed with a thermal camera-equipped small unmanned aerial system (sUAS). We placed bobwhite groups (3, 6, and 12 individuals/cage) among three cover types (grass, shrub, forest) on a private farm in Clay County, Mississippi (3 replicates, 27 total cages). At civil twilight, the sUAS flew over cages at 30 m, capturing photographs every 5 s. We asked 31 volunteers to evaluate 57 photographs for covey presence. Overall true positive rate was 0.551, but improved with increasing covey size. Coveys in grass had lowest true positive rate by photograph (0.403), followed by forest (0.562) and shrub (0.605). Results indicate that thermal sUAS could be a viable method for surveying intact bobwhite coveys, especially if detection of smaller groups and those in denser vegetation improves.

No description available.

One of the many challenges humanity faces is developing energy access in a sustainable manner, that does not further contribute to the burning of fossil fuels, increasing greenhouse gas emissions, and global warming. In 2020, 2.4 billion people used inefficient and polluting cooking systems due to lack of energy access while 25% of schools lacked access to electricity, drinking water and basic sanitation. This thesis seeks to investigate this challenge by using satellite imagery big data to improve energy access in a sustainable manner.The theoretical framework explains the key concepts and outlines the theoretical underpinnings of this research in transformative social innovation theory and behavioural theory which help guide the analysis. The link between this research and existing research is also explained in this section. The methodology used will be to research review articles and choose the most appropriate and credible texts to answer two research questions. The first challenge relates to identifying promising applications of satellite imagery big data in improving energy access, and the second relates to explaining how we can ensure that development of energy access from satellite imagery is conducted in a sustainable manner.The primary findings of this research are that there are a number of credible review articles which contain real opportunities for improved energy access and include identifying optimum photovoltaics investment locations, identifying optimum small hydropower plant sites, CAM plant cultivation locations, an indicator to directly address sustainable energy investments and rural electricity access needs, and mapping of remote off-grid homes for improvement of energy access. The findings also indicated three key factors that are crucial for the sustainable development of energy access which include communication, collaboration, and community.There are a number of varied applications of satellite imagery big data discovered that each exhibit significant value in improving energy access. The value that can be gained is closely related to the ability of the research community, to engage with local actors, to build a collaborative environment, where knowledge is shared, and community is built.

Context: Motor Imagery (MI) signal classification is a crucial task for developing Brain-Computer Interfaces (BCIs) that allow people to control devices using their thoughts. However, traditional machine learning approaches often suffer from limited performance due to inter-subject variability and limited data availability. In response, adversarial learning has emerged as a promising solution to enhance the resilience and accuracy of BCI systems. However, to the best of our knowledge, there has not been a review of the literature on adversarial learning specifically focusing on MI classification. Objective: The objective of this thesis is to perform a Systematic Literature Review (SLR) focusing on the latest techniques of adversarial learning used to classify motor imagery signals. It aims to analyze the publication trends of the reviewed studies, investigate their use-cases, and identify the challenges in the field. Additionally, this research recognizes the datasets used in previous studies and their associated use-cases. It also identifies the pre-processing and adversarial learning techniques, and compare their performance. Additionally, it could aid in evaluating the replicability of the studies included. The outcomes of this study will assist future researchers in selecting appropriate datasets, pre-processing, and adversarial learning techniques to advance their research objectives. The comparison of models will also provide practical insights, enabling researchers to make informed decisions when designing models for motor imagery classification. Furthermore, assessing reproducibility might help in validating the research outcomes and hence elevate the overall quality of future research. Method: A thorough and systematic search following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines is undertaken to gather primary research articles from several databases such as Scopus, Web of Science, IEEEXplore, PubMed, and ScienceDirect. Two independent reviewers evaluated the articles obtained based on predetermined eligibility criteria at the title-abstract level, and their agreement was measured using Cohen's Kappa. The articles that fulfill the criteria are then scrutinized at the full-text level by the same reviewers. Any discrepancies are resolved by the judge – played by the supervisor. Critical appraisal was employed to choose appropriate studies for data extraction, which was subsequently examined using bibliometric and descriptive analyses to answer the research questions. Result: The study's findings indicate substantial growth within the domain over the past six years, notably propelled by contributions from the Asian region. However, the need for augmented collaboration becomes evident as evidenced by the prevalence of insular co-author networks. Four principal use-cases for adversarial learning are identified, spanning data augmentation, domain adaptation, feature extraction, and artifact removal. The favored datasets are BCI Competition IV's 2a and 2b, often accompanied by band-pass filtering and exponential moving standardization preprocessing. This study identifies two primary adversarial learning techniques: GAN and Adversarial Training. GAN is mainly used for data augmentation and artifact removal, while adversarial training is employed for domain adaptation and feature extraction. Based on the results reported in the chosen papers, the accuracy achieved for data augmentation and domain adaptation use cases is nearly identical at 95.3%, while the highest accuracy for the feature extraction use case is 86.91%. However, for artifact removal, both correlation and root mean square methods have been referenced. Furthermore, a reproducibility table has been established which may help in evaluating the replicability of the selected studies . Conclusion: The outcomes provide researchers with valuable perspectives on less-explored areas that hold room for additional enhancement. Ultimately, these perspectives hold the promise of improving the practical applications intended to support individuals dealing with motor impairments.

Indiana University-Purdue University Indianapolis (IUPUI) / Semantic segmentation is a machine learning task that is seeing increased utilization in multiples fields, from medical imagery, to land demarcation, and autonomous vehicles. Semantic segmentation performs the pixel-wise classification of images, creating a new, seg- mented representation of the input that can be useful for detected various terrain and objects within and image. Recently, convolutional neural networks have been heavily utilized when creating neural networks tackling the semantic segmentation task. This is particularly true in the field of autonomous driving systems. The requirements of automated driver assistance systems (ADAS) drive semantic seg- mentation models targeted for deployment on ADAS to be lightweight while maintaining accuracy. A commonly used method to increase accuracy in the autonomous vehicle field is to fuse multiple sensory modalities. This research focuses on leveraging the fusion of long wave infrared (LWIR) imagery with visual spectrum imagery to fill in the inherent perfor- mance gaps when using visual imagery alone. This comes with a host of benefits, such as increase performance in various lighting conditions and adverse environmental conditions. Utilizing this fusion technique is an effective method of increasing the accuracy of a semantic segmentation model. Being a lightweight architecture is key for successful deployment on ADAS, as these systems often have resource constraints and need to operate in real-time. Multi-Spectral Fusion Network (MFNet) [1] accomplishes these parameters by leveraging a sensory fusion approach, and as such was selected as the baseline architecture for this research. Many improvements were made upon the baseline architecture by leveraging a variety of techniques. Such improvements include the proposal of a novel loss function categori- cal cross-entropy dice loss, introduction of squeeze and excitation (SE) blocks, addition of pyramid pooling, a new fusion technique, and drop input data augmentation. These improve- ments culminated in the creation of the Fast Thermal Fusion Network (FTFNet). Further improvements were made by introducing depthwise separable convolutional layers leading to lightweight FTFNet variants, FTFNet Lite 1 & 2. 13 The FTFNet family was trained on the Multi-Spectral Road Scenarios (MSRS) and MIL- Coaxials visual/LWIR datasets. The proposed modifications lead to an improvement over the baseline in mean intersection over union (mIoU) of 2.92% and 2.03% for FTFNet and FTFNet Lite 2 respectively when trained on the MSRS dataset. Additionally, when trained on the MIL-Coaxials dataset, the FTFNet family showed improvements in mIoU of 8.69%, 4.4%, and 5.0% for FTFNet, FTFNet Lite 1, and FTFNet Lite 2.