Search results for: mesenteric lymph node

Authors: Nabamita Nath, Sanghamitra Sarkar

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Urban development in traditional neighbourhoods of cities is undergoing a sea change due to imposition of irregular development patterns on a predominantly inclusive urban fabric. In recent times, traditional neighbourhoods of Kolkata have experienced irregular urban development which has resulted in transformation of its immediate urban character. The goal is to study and analyse impact of new urban developments within traditional neighbourhoods of Kolkata and establish development guidelines to balance the old with the new. Various cities predominantly in third world countries are also experiencing similar development patterns in their traditional neighbourhoods. Existing literature surveys of development patterns in such neighbourhoods have established 9 major parameters viz. edge, movement, node, landmark, size-density, pattern-grain-texture, open spaces, urban spaces, urban form and views-vistas of the neighbourhood. To evaluate impact of urban development in traditional neighbourhoods of Kolkata, 3 different areas have been chronologically selected based on their settlement patterns. Parameters established through literature surveys have been applied to the selected areas to study and analyse the existing patterns of development. The main sources of this study included extensive on-site surveys, academic archive, census data, organisational records and informational websites. Applying the established parameters, 5 major conclusions were derived. Firstly, it was found that pedestrian friendly neighbourhoods of the city were becoming more car-centric. This has resulted in loss of interactive and social spaces which defined the cultural heritage of Kolkata. Secondly, the urban pattern which was composed of dense and compact fabric is gradually losing its character due to incorporation of new building typologies. Thirdly, the new building typologies include gated communities with private open spaces which is a stark departure from the existing built typology. However, these open spaces have not contributed in creation of inclusive public places for the community which are a significant part of such heritage neighbourhood precincts. Fourthly, commercial zones that primarily developed along major access routes have now infiltrated within these neighbourhoods. Gated communities do not favour formation of on-street commercial activities generating haphazard development patterns. Lastly, individual residential buildings that reflected Indo-saracenic and Neo-gothic architectural styles are converting into multi-storeyed residential apartments. As a result, the axis that created a definite visual identity for a neighbourhood is progressively following an irregular pattern. Thus, uniformity of the old skyline is gradually becoming inconsistent. The major issue currently is threat caused by irregular urban development to heritage zones and buildings of traditional neighbourhoods. Streets, lanes, courtyards, open spaces and buildings of old neighbourhoods imparted a unique cultural identity to the city that is disappearing with emerging urban development patterns. It has been concluded that specific guidelines for urban development should be regulated primarily based on existing urban form of traditional neighbourhoods. Such neighbourhood development strategies should be formulated for various cities of third world countries to control irregular developments thereby balancing heritage and development.

Keywords: heritage, Kolkata, traditional neighbourhood, urban development

Procedia PDF Downloads 153

Authors: Michelle E. Taylor, Clare E. Morrall

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Microplastics are small particles produced for industrial purposes or formed by breakdown of anthropogenic debris. Caribbean nations import large quantities of plastic products. The Caribbean region is vulnerable to natural disasters and Climate Change is predicted to bring multiple additional challenges to island nations. Microplastics have been found in an array of marine environments and in a diversity of marine species. Occurrence of microplastic in the intestinal tracts of marine fish is a concern to human and ecosystem health as pollutants and pathogens can associate with plastics. Studies have shown that the incidence of microplastics in marine fish varies with species and location. Prevalence of microplastics (≤ 5 mm) in fish species from Grenadian waters (representing pelagic, semi-pelagic and demersal lifestyles) harvested for human consumption have been investigated via gut analysis. Harvested tissue was digested in 10% KOH and particles retained on a 0.177 mm sieve were examined. Microplastics identified have been classified according to type, colour and size. Over 97% of fish examined thus far (n=34) contained microplastics. Current and future work includes examining the invasive Lionfish (Pterois spp.) for microplastics, investigating marine invertebrate species as well as examining environmental sources of microplastics (i.e. rivers, coastal waters and sand). Owing to concerns of pollutant accumulation on microplastics and potential migration into organismal tissues, we plan to analyse fish tissue for mercury and other persistent pollutants. Despite having ~110,000 inhabitants, the island nation of Grenada imported approximately 33 million plastic bottles in 2013, of which it is estimated less than 5% were recycled. Over 30% of the imported bottles were ‘unmanaged’, and as such are potential litter/marine debris. A revised Litter Abatement Act passed into law in Grenada in 2015, but little enforcement of the law is evident to date. A local Non-governmental organization (NGO) ‘The Grenada Green Group’ (G3) is focused on reducing litter in Grenada through lobbying government to implement the revised act and running sessions in schools, community groups and on local media and social media to raise awareness of the problems associated with plastics. A local private company has indicated willingness to support an Anti-Litter Campaign in 2018 and local awareness of the need for a reduction of single use plastic use and litter seems to be high. The Government of Grenada have called for a Sustainable Waste Management Strategy and a ban on both Styrofoam and plastic grocery bags are among recommendations recently submitted. A Styrofoam ban will be in place at the St. George’s University campus from January 1st, 2018 and many local businesses have already voluntarily moved away from Styrofoam. Our findings underscore the importance of continuing investigations into microplastics in marine life; this will contribute to understanding the associated health risks. Furthermore, our findings support action to mitigate the volume of plastics entering the world’s oceans. We hope that Grenada’s future will involve a lot less plastic. This research was supported by the Caribbean Node of the Global Partnership on Marine Litter.

Keywords: Caribbean, microplastics, pollution, small island developing nation

Procedia PDF Downloads 184

Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

Procedia PDF Downloads 94

Authors: Ho-Won Jung, Donghun Lee, Hyung-Jin Kim

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Predictive analytics (data analysis) uses a subset of measurements (the features, predictor, or independent variable) to predict another measurement (the outcome, target, or dependent variable) on a single person or unit. It applies empirical methods in statistics, operations research, and machine learning to predict the future, or otherwise unknown events or outcome on a single or person or unit, based on patterns in data. Most analyses of metabolic syndrome are not predictive analytics but statistical explanatory studies that build a proposed model (theory building) and then validate metabolic syndrome predictors hypothesized (theory testing). A proposed theoretical model forms with causal hypotheses that specify how and why certain empirical phenomena occur. Predictive analytics and explanatory modeling have their own territories in analysis. However, predictive analytics can perform vital roles in explanatory studies, i.e., scientific activities such as theory building, theory testing, and relevance assessment. In the context, this study is to demonstrate how to use our predictive analytics to support theory building (i.e., hypothesis generation). For the purpose, this study utilized a big data predictive analytics platform TM based on a co-occurrence graph. The co-occurrence graph is depicted with nodes (e.g., items in a basket) and arcs (direct connections between two nodes), where items in a basket are fully connected. A cluster is a collection of fully connected items, where the specific group of items has co-occurred in several rows in a data set. Clusters can be ranked using importance metrics, such as node size (number of items), frequency, surprise (observed frequency vs. expected), among others. The size of a graph can be represented by the numbers of nodes and arcs. Since the size of a co-occurrence graph does not depend directly on the number of observations (transactions), huge amounts of transactions can be represented and processed efficiently. For a demonstration, a total of 13,254 metabolic syndrome training data is plugged into the analytics platform to generate rules (potential hypotheses). Each observation includes 31 predictors, for example, associated with sociodemographic, habits, and activities. Some are intentionally included to get predictive analytics insights on variable selection such as cancer examination, house type, and vaccination. The platform automatically generates plausible hypotheses (rules) without statistical modeling. Then the rules are validated with an external testing dataset including 4,090 observations. Results as a kind of inductive reasoning show potential hypotheses extracted as a set of association rules. Most statistical models generate just one estimated equation. On the other hand, a set of rules (many estimated equations from a statistical perspective) in this study may imply heterogeneity in a population (i.e., different subpopulations with unique features are aggregated). Next step of theory development, i.e., theory testing, statistically tests whether a proposed theoretical model is a plausible explanation of a phenomenon interested in. If hypotheses generated are tested statistically with several thousand observations, most of the variables will become significant as the p-values approach zero. Thus, theory validation needs statistical methods utilizing a part of observations such as bootstrap resampling with an appropriate sample size.

Keywords: explanatory modeling, metabolic syndrome, predictive analytics, theory building

Procedia PDF Downloads 247

Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

Procedia PDF Downloads 42

Authors: Dimitrios Tzilopoulos, Panagiotis Symeonidis, Michael Loufakis, Dimosthenis Ioannidis, Dimitrios Tzovaras

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The domestic film industry in Greece has traditionally relied heavily on state support. While film productions are crucial for the country's economy, it has not fully capitalized on attracting and promoting foreign productions. The lack of motivation, organized state support for attraction and licensing, and the absence of location scouting have hindered its potential. Although recent legislative changes have addressed the first two of these issues, the development of a comprehensive location database and a search engine that would effectively support location scouting at the pre-production location scouting is still in its early stages. In addition to the expected benefits of the film, television, marketing, and multimedia industries, a location-scouting service platform has the potential to yield significant financial gains locally and nationally. By promoting featured places like cultural and archaeological sites, natural monuments, and attraction points for visitors, it plays a vital role in both cultural promotion and facilitating tourism development. This study introduces LOCATION3, an internet platform revolutionizing film production location management. It interconnects location providers, film crews, and multimedia stakeholders, offering a comprehensive environment for seamless collaboration. The platform's central geodatabase (PostgreSQL) stores each location’s attributes, while web technologies like HTML, JavaScript, CSS, React.js, and Redux power the user-friendly interface. Advanced functionalities, utilizing deep learning models, developed in Python, are integrated via Node.js. Visual data presentation is achieved using the JS Leaflet library, delivering an interactive map experience. LOCATION3 sets a new standard, offering a range of essential features to enhance the management of film production locations. Firstly, it empowers users to effortlessly upload audiovisual material enriched with geospatial and temporal data, such as location coordinates, photographs, videos, 360-degree panoramas, and 3D location models. With the help of cutting-edge deep learning algorithms, the application automatically tags these materials, while users can also manually tag them. Moreover, the application allows users to record locations directly through its user-friendly mobile application. Users can then embark on seamless location searches, employing spatial or descriptive criteria. This intelligent search functionality considers a combination of relevant tags, dominant colors, architectural characteristics, emotional associations, and unique location traits. One of the application's standout features is the ability to explore locations by their visual similarity to other materials, facilitated by a reverse image search. Also, the interactive map serves as both a dynamic display for locations and a versatile filter, adapting to the user's preferences and effortlessly enhancing location searches. To further streamline the process, the application facilitates the creation of location lightboxes, enabling users to efficiently organize and share their content via email. Going above and beyond location management, the platform also provides invaluable liaison, matchmaking, and online marketplace services. This powerful functionality bridges the gap between visual and three-dimensional geospatial material providers, local agencies, film companies, production companies, etc. so that those interested in a specific location can access additional material beyond what is stored on the platform, as well as access production services supporting the functioning and completion of productions in a location (equipment provision, transportation, catering, accommodation, etc.).

Keywords: deep learning models, film industry, geospatial data management, location scouting

Procedia PDF Downloads 47