485 research outputs found
Information in Civil Societies – a multi-faceted approach
DOI: http://dx.doi.org/10.5130/ccs.v5i3.370
Chasing the Antelopes: A Personal Reflection
This article is a personal reflection based on the author\u27s experience of visiting the Ajanta Caves in India and what they mean to the author -- as documents, as evidence, and as social and cultural heritage
From Everyday Information Behaviours to Clickable Solidarity
Digital social media has, in many ways, transformed the way people create, maintain, and sustain their social information networks, and has also influenced their information-related behaviours such as searching, seeking, finding and use of information. This is especially true in technologically-mediated environments. In many ways, social media is the contemporary incarnation of the Internet itself. It is a complex information-and-communication environment, very much analogous to physical environments, but consisting of symbolic matter rather than physical matter. All social situations are information environments and social media is no different. This paper is an inter-disciplinary literature-review essay that examines the social media phenomenon using the lens of selected theories in information science and allied disciplines such as communication and media ecology with a specific focus toward its possible role in civil society using the conceptual framework of spatial metaphors drawn from the study of traditional physical environments. DOI: http://dx.doi.org/10.5130/ccs.v5i3.348
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HUMAN SUSPICIOUS ACTIVITY DETECTION
The detection of suspicious human activity is a crucial aspect of ensuring public safety and security. The aim is to identify suspicious behavior. To accomplish this, we employ the LRCN, a long-term recurrent convolutional network, to detect anomalous activity. It is important to consider the temporal data of the video when classifying suspicious behavior, and the framework uses a combination of CNNs and RNNs to analyze video frames and extract relevant features. The key milestones of this project include conducting research, collecting and pre-processing data, designing and training the model, and evaluating its performance. The resulting detection system can accurately identify suspicious behavior in real-time. To build the model, we used the KTH dataset, which includes 600 frames of walking and running, as well as the Kaggle dataset, which consists of 100 training videos. Our model analysis shows that the system and video can detect suspicious events with an accuracy of 86%, and we anticipate that this accuracy will improve as the dataset size increases
લીલાશુક રચિત કૃષ્ણકર્ણામૃતઃ એક સમીક્ષિત અધ્યયન
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Design, Synthesis, Characterization and Biological Activities of few Heterocyclic Compounds
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Automated deep phenotyping of the cardiovascular system using magnetic resonance imaging
Across a lifetime, the cardiovascular system must adapt to a great range of demands from the body. The individual changes in the cardiovascular system that occur in response to loading conditions are influenced by genetic susceptibility, and the pattern and extent of these changes have prognostic value. Brachial blood pressure (BP) and left ventricular ejection fraction (LVEF) are important biomarkers that capture this response, and their measurements are made at high resolution. Relatively, clinical analysis is crude, and may result in lost information and the introduction of noise. Digital information storage enables efficient extraction of information from a dataset, and this strategy may provide more precise and deeper measures to breakdown current phenotypes into their component parts. The aim of this thesis was to develop automated analysis of cardiovascular magnetic resonance (CMR) imaging for more detailed phenotyping, and apply these techniques for new biological insights into the cardiovascular response to different loading conditions. I therefore tested the feasibility and clinical utility of computational approaches for image and waveform analysis, recruiting and acquiring additional patient cohorts where necessary, and then applied these approaches prospectively to participants before and after six-months of exercise training for a first-time marathon. First, a multi-centre, multi-vendor, multi-field strength, multi-disease CMR resource of 110 patients undergoing repeat imaging in a short time-frame was assembled. The resource was used to assess whether automated analysis of LV structure and function is feasible on real-world data, and if it can improve upon human precision. This showed that clinicians can be confident in detecting a 9% change in EF or a 20g change in LV mass. This will be difficult to improve by clinicians because the greatest source of human error was attributable to the observer rather than modifiable factors. Having understood these errors, a convolutional neural network was trained on separate multi-centre data for automated analysis and was successfully generalizable to the real-world CMR data. Precision was similar to human analysis, and performance was 186 times faster. This real-world benchmarking resource has been made freely available (thevolumesresource.com). Precise automated segmentations were then used as a platform to delve further into the LV phenotype. Global LVEFs measured from CMR imaging in 116 patients with severe aortic stenosis were broken down into ~10 million regional measurements of structure and function, represented by computational three-dimensional LV models for each individual. A cardiac atlas approach was used to compile, label, segment and represent these data. Models were compared with healthy matched controls, and co-registered with follow-up one year after aortic valve replacement (AVR). This showed that there is a tendency to asymmetric septal hypertrophy in all patients with severe aortic stenosis (AS), rather than a characteristic specific to predisposed patients. This response to AS was more unfavourable in males than females (associated with higher NT-proBNP, and lower blood pressure), but was more modifiable with AVR. This was not detected using conventional analysis. Because cardiac function is coupled with the vasculature, a novel integrated assessment of the cardiovascular system was developed. Wave intensity theory was used to combine central blood pressure and CMR aortic blood flow-velocity waveforms to represent the interaction of the heart with the vessels in terms of traveling energy waves. This was performed and then validated in 206 individuals (the largest cohort to date), demonstrating inefficient ventriculo-arterial coupling in female sex and healthy ageing. CMR imaging was performed in 236 individuals before training for a first-time marathon and 138 individuals were followed-up after marathon completion. After training, systolic/diastolic blood pressure reduced by 4/3mmHg, descending aortic stiffness decreased by 16%, and ventriculo-arterial coupling improved by 14%. LV mass increased slightly, with a tendency to more symmetrical hypertrophy. The reduction in aortic stiffness was equivalent to a 4-year reduction in estimated biological aortic age, and the benefit was greater in older, male, and slower individuals. In conclusion, this thesis demonstrates that automating analysis of clinical cardiovascular phenotypes is precise with significant time-saving. Complex data that is usually discarded can be used efficiently to identify new biology. Deeper phenotypes developed in this work inform risk reduction behaviour in healthy individuals, and demonstrably deliver a more sensitive marker of LV remodelling, potentially enhancing risk prediction in severe aortic stenosis
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