Search results for: sparse representation

Authors: Enwin Anthony Dornubari, Visigah Kpobari Peter

Abstract:

This research has developed a framework for the provision of sustainable and affordable housing to accommodate the low-income population of Greater Port Harcourt City. The objectives of this study among others, were to: examine UN-Habitat guidelines for acceptable and sustainable social housing provision, describe past efforts of the Rivers State Government and the Federal Government of Nigeria to provide housing for the poor in the Greater Port Harcourt City area; obtain a profile of prospective beneficiaries of the social housing proposed by this research as well as perceptions of their present living conditions, and living in the proposed self-sustaining social housing development, based on the initial simulation of the proposal; describe the nature of the framework, guideline and management of the proposed social housing development and explain the modalities for its implementation. The study utilized the mixed methods research approach, aimed at triangulating findings from the quantitative and qualitative paradigms. Opinions of professional of the built environment; Director, Development Control, Greater Port Harcourt City Development Authority; Directors of Ministry of Urban Development and Physical Planning; Housing and Property Development Authority and managers of selected Primary Mortgage Institutions were sought and analyzed. There were four target populations for the study, namely: members of occupational sub-groups for FGDs (Focused Group Discussions); development professionals for KIIs (Key Informant Interviews), household heads in selected communities of GPHC; and relevant public officials for IDI (Individual Depth Interview). Focus Group Discussions (FGDs) were held with members of occupational sub-groups in each of the eight selected communities (Fisherfolk). The table shows that there were forty (40) members across all occupational sub-groups in each selected community, yielding a total of 320 in the eight (8) communities of Mgbundukwu (Mile 2 Diobu), Rumuodomaya, Abara (Etche), Igwuruta-Ali(Ikwerre), Wakama(Ogu-Bolo), Okujagu (Okrika), Akpajo (Eleme), and Okoloma (Oyigbo). For key informant interviews, two (2) members were judgmentally selected from each of the following development professions: urban and regional planners; architects; estate surveyors; land surveyors; quantity surveyors; and engineers. Concerning Population 3-Household Heads in Selected Communities of GPHC, a stratified multi-stage sampling procedure was adopted: Stage 1-Obtaining a 10% (a priori decision) sample of the component communities of GPHC in each stratum. The number in each stratum was rounded to one whole number to ensure representation of each stratum. Stage 2-Obtaining the number of households to be studied after applying the Taro Yamane formula, which aided in determining the appropriate number of cases to be studied at the precision level of 5%. Findings revealed, amongst others, that poor implementation of the UN-Habitat global shelter strategy, lack of stakeholder engagement, inappropriate locations, undue bureaucracy, lack of housing fairness and equity and high cost of land and building materials were the reasons for the failure of past efforts towards social housing provision in the Greater Port Harcourt City area. The study recommended a public-private partnership approach for the implementation and management of the framework. It also recommended a robust and sustained relationship between the management of the framework and the UN-Habitat office and other relevant government agencies responsible for housing development and all investment partners to create trust and efficiency.

Keywords: development, framework, low-income, sustainable, social housing

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Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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Authors: Charles W. MacQuarrie, Rachel R. Tatro Duarte

Abstract:

This paper explores the representation of medieval identity within the world of games such as Dragon Age, Elden Ring, Hellblade: Senua’s sacrifice, fantasy role-playing games that draw effectively and problematically on Celtic and Norse mythologies. Focusing on tattoos, onomastics, and accent as visual and oral markers of status and ethnicity, this study analyzes how the game's interplay between mythology, character narratives, and visual storytelling enriches the themes and offers players an immersive, but sometimes baldly ahistorical, connection to ancient mythologies and contemporary digital storytelling. Dragon Age is a triple a game series, Hellblade Senua’s Sacrifice, and Elden Ring of gamers worldwide with its presentation of an idealized medieval world, inspired by the lore of Celtic and Norse mythologies. This paper sets out to explore the intricate relationships between tattoos, accent, and character narratives in the game, drawing parallels to themes,heroic figures and gods from Celtic and Norse mythologies. Tattoos as Mythic and Ethnic Markers: This study analyzes how tattoos in Dragon Age visually represent mythological elements from both Celtic and Norse cultures, serving as conduits of cultural identity and narratives. The nature of these tattoos reflects the slave, criminal, warrior associations made in classical and medieval literature, and some of the episodes concerning tattoos in the games have either close analogs or sources in literature. For example the elvish character Solas, in Dragon Age Inquisition, removes a slave tattoo from the face of a lower status elf in an episode that is reminiscent of Bridget removing the stigmata from Connallus in the Vita Prima of Saint Bridget Character Narratives: The paper examines how characters' personal narratives in the game parallel the archetypal journeys of Celtic heroes and Norse gods, with a focus on their relationships to mythic themes. In these games the Elves usually have Welsh or Irish accents, are close to nature, magically powerful, oppressed by apparently Anglo-Saxon humans and Norse dwarves, and these elves wear facial tattoos. The Welsh voices of fairies and demons is older than the reference in Shakespeare’s Merry Wives of Windsor or even the Anglo-Saxon Life of Saint Guthlac. The English speaking world, and the fantasy genre of literature and gaming, undoubtedly driven by Tolkien, see Elves as Welsh speakers, and as having Welsh accents when speaking English Comparative Analysis: A comparative approach is employed to reveal connections, adaptations, and unique interpretations of the motifs of tattoos and narrative themes in Dragon Age, compared to those found in Celtic and Norse mythologies. Methodology: The study uses a comparative approach to examine the similarities and distinctions between Celtic and Norse mythologies and their counterparts in video games. The analysis encompasses character studies, narrative exploration, visual symbolism, and the historical context of Celtic and Norse cultures. Mythic Visuals: This study showcases how tattoos, as visual symbols, encapsulate mythic narratives, beliefs, and cultural identity, echoing Celtic and Norse visual motifs. Archetypal Journeys: The paper analyzes how character arcs mirror the heroic journeys of Celtic and Norse mythological figures, allowing players to engage with mythic narratives on a personal level. Cultural Interplay: The study discusses how the game's portrayal of tattoos and narratives both preserves and reinterprets elements from Celtic and Norse mythologies, fostering a connection between ancient cultures and modern digital storytelling. Conclusion: By exploring the interconnectedness of tattoos and character narratives in Dragon Age, this paper reveals the game series' ability to act as a bridge between ancient mythologies and contemporary gaming. By drawing inspiration from Celtic heroes and Norse gods and translating them into digital narratives and visual motifs, Dragon Age offers players a multi-dimensional engagement with mythic themes and a unique lens through which to appreciate the enduring allure of these cultures.

Keywords: comparative analysis, character narratives, video games and literature, tattoos, immersive storytelling, character development, mythological influences, Celtic mythology, Norset mythology

Procedia PDF Downloads 56