49 research outputs found
Modeling of Catastrophes and Estimation of National Catastrophe Fund
Arts and Sciences Research SchoalrshipBill 'H.R. 2555: Homeowners' Defense Act of 2010' was considered by the United States of America (US) Congress to form National Catastrophe Risk Consortium. The bill was introduced on April 27, 2010, but was not enacted. One of the functions of the consortium was to fund a National Catastrophe Fund to help public and insurance companies meet the liability claims from hurricane, fire, and blizzard. The bill has to pass through House Financial Services Committee which takes into account the projected costs, disbursements, and the amount required to be appropriated for the task and its source. In this project the following are accomplished:
Part I: Modeling
• Employed the method of Maximum Likelihood Estimation (MLE) to estimate the parameters such as mean of count of fires, hurricanes and blizzards.
• Modeled the number of acres burnt by fires by Weibull distribution.
• Modeled the economic damages caused by blizzards by Poisson Distribution.
• Employed Poisson distribution to model the number of hurricanes occurring in each hurricane category.
Part II: Estimating
• Estimated 2013 claims for hurricane, fire, and blizzard in United States of America using data for three catastrophes for the last 100 years.
• Carried out Linear regression analysis to compare the results of the new model for acres burnt from fires and economic damage from blizzards.
• Predicted the economic damages from the three disasters in 2013 to be 46.7 billion dollars.
• The National Catastrophe Fund for the liabilities of fire, blizzard, and hurricane needs to be funded by 10% of the estimated economic damages which for 2013 amounts to 4.67 billion dollars.No embargoAcademic Major: Actuarial Scienc
Using Cross-Lingual Explicit Semantic Analysis for Improving Ontology Translation
Semantic Web aims to allow machines to make inferences using the explicit conceptualisations contained in ontologies. By pointing to ontologies, Semantic Web-based applications are able to inter-operate and share common information easily. Nevertheless, multilingual semantic applications are still rare, owing to the fact that most online ontologies are monolingual in English. In order to solve this issue, techniques for ontology localisation and translation are needed. However, traditional machine translation is difficult to apply to ontologies, owing to the fact that ontology labels tend to be quite short in length and linguistically different from the free text paradigm. In this paper, we propose an approach to enhance machine translation of ontologies based on exploiting the well-structured concept descriptions contained in the ontology. In particular, our approach leverages the semantics contained in the ontology by using Cross Lingual Explicit Semantic Analysis (CLESA) for context-based disambiguation in phrase-based Statistical Machine Translation (SMT). The presented work is novel in the sense that application of CLESA in SMT has not been performed earlier to the best of our knowledge
ESTIMATION OF SERUM COPPER AND ZINC LEVEL IN PATIENTS WITH ORAL SUB MUCOUS FIBROSIS AND ORAL SQUAMOUS CELL CARCINOMA
Trace elements are receiving too much attention as they are found to be significantly altered in head and neck, lung and breast carcinomas, and there is need to develop sensitive, specific and faster tests as an aid in the early diagnosis of the primary tumor and its recurrence or malignant transformation in premalignant states.Aim: To estimate serum copper and zinc levels in Oral Submucous Fibrosisand oral squamous cell carcinoma patients. Methodology: Sera of OSCC (n = 10) and OSMF (n = 10) patients and of healthy controls was analysed for the estimation of Cu and Zn using atomic absorption spectrophotometry. Results: There was an increase in sera levels of Cu while those of Zn were decreased in both Oral Submucous fibrosis and Oral Squamous Cell Carcinoma patients as compared to the healthy controls. Conclusion: It could be concluded that there is an alteration of sera levels of these trace elements which can be helpful in early detection and management in OSMF and OSCC patients
Cross-Lingual Linking of News Stories using ESA
In this paper, we describe our approach for Cross-Lingual linking of Indian news stories, submitted for Cross-Lingual Indian News Story Search (CL!NSS) task at FIRE 2012. Our approach consists of two major steps, the reduction of search space by using di�erent features and ranking of the news stories according to their relatedness scores. Our approach uses Wikipedia-based Cross-Lingual Explicit Semantic Analysis (CLESA) to calculate the semantic similarity and relatedness score between two news stories in di�erent languages. We evaluate our approach on CL!NSS dataset, which consists of 50 news stories in English and 50K news stories in Hindi
Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph
In this paper, we describe an embedding-based entity recommendation framework
for Wikipedia that organizes Wikipedia into a collection of graphs layered on
top of each other, learns complementary entity representations from their
topology and content, and combines them with a lightweight learning-to-rank
approach to recommend related entities on Wikipedia. Through offline and online
evaluations, we show that the resulting embeddings and recommendations perform
well in terms of quality and user engagement. Balancing simplicity and quality,
this framework provides default entity recommendations for English and other
languages in the Yahoo! Knowledge Graph, which Wikipedia is a core subset of.Comment: 8 pages, 4 figures, 8 tables. To be appeared in Wiki Workshop 2020,
Companion Proceedings of the Web Conference 2020(WWW 20 Companion), Taipei,
Taiwa
The New Frontier of Cybersecurity: Emerging Threats and Innovations
In today's digitally interconnected world, cybersecurity threats have reached
unprecedented levels, presenting a pressing concern for individuals,
organizations, and governments. This study employs a qualitative research
approach to comprehensively examine the diverse threats of cybersecurity and
their impacts across various sectors. Four primary categories of threats are
identified and analyzed, encompassing malware attacks, social engineering
attacks, network vulnerabilities, and data breaches. The research delves into
the consequences of these threats on individuals, organizations, and society at
large. The findings reveal a range of key emerging threats in cybersecurity,
including advanced persistent threats, ransomware attacks, Internet of Things
(IoT) vulnerabilities, and social engineering exploits. Consequently, it is
evident that emerging cybersecurity threats pose substantial risks to both
organizations and individuals. The sophistication and diversity of these
emerging threats necessitate a multi-layered approach to cybersecurity. This
approach should include robust security measures, comprehensive employee
training, and regular security audits. The implications of these emerging
threats are extensive, with potential consequences such as financial loss,
reputational damage, and compromised personal information. This study
emphasizes the importance of implementing effective measures to mitigate these
threats. It highlights the significance of using strong passwords, encryption
methods, and regularly updating software to bolster cyber defenses.Comment: 6 pages, 2 Table
Ensemble Fractional Sensitivity: A Quantitative Approach to Neuron Selection for Decoding Motor Tasks
A robust method to help identify the population of neurons used for decoding motor tasks is developed. We use sensitivity analysis to develop a new metric for quantifying the relative contribution of a neuron towards the decoded output, called “fractional sensitivity.” Previous model-based approaches for neuron ranking have been shown to largely depend on the collection of training data. We suggest the use of an ensemble of models that are trained on random subsets of trials to rank neurons. For this work, we tested a decoding algorithm on neuronal data recorded from two male rhesus monkeys while they performed a reach to grasp a bar at three orientations (45°, 90°, or 135°). An ensemble approach led to a statistically significant increase of 5% in decoding accuracy and 25% increase in identification accuracy of simulated noisy neurons, when compared to a single model. Furthermore, ranking neurons based on the ensemble fractional sensitivities resulted in decoding accuracies 10%–20% greater than when randomly selecting neurons or ranking based on firing rates alone. By systematically reducing the size of the input space, we determine the optimal number of neurons needed for decoding the motor output. This selection approach has practical benefits for other BMI applications where limited number of electrodes and training datasets are available, but high decoding accuracies are desirable
Local Bupivacaine Infiltration to Reduce Pain after Tonsillectomy: A Low Cost Approach
Introduction
Tonsillectomy is one of the most commonly performed surgical procedures worldwide, with the major drawback of significant post operative pain.There is no consensus regarding topical application or local infiltration of anesthetics post operatively to reduce pain. The present study was performed to evaluate the effect of bupivacaine infiltration in the tonsillar fossae after tonsillectomy.
Materials and Methods
A double-blinded clinical trial was performed on 75 patients undergoing tonsillectomy between January 2019 and January 2020. All patients underwent tonsillectomy under general anesthesia and were then randomly divided into 3 groups of 25 patients each. For Group I, a swab soaked in normal saline was applied to the tonsillar fossae for 5 minutes just before extubation. In Group II, a swab soaked in 5 ml of 0.5% bupivacaine was placed for 5 minutes, while in Group III, 5ml of 0.5% bupivacaine was infiltrated in the tonsillar fossae. The intensity of pain for each group was measured in the immediate post op period, at6 hours, 24 hours and 1 week by Wong Baker Faces Pain Rating Scale.
Results
There was a significant difference in the mean level of pain between groups I and III in the immediate post op period, at 6 hours and 24 hours. Although the average pain scores of group III were better than those of group II, the results were significant only in the 6 hour post op period.
Conclusion
To reduce post-tonsillectomy pain,0.5% bupivacaine can be infiltrated into the tonsillar fossa
Preparation and Evaluation of Nano-vesicles of Brimonidine Tartrate as an Ocular Drug Delivery System
The objective of the present investigation was to design a vesicular formulation of brimonidine tartrate and evaluate its ability to reduce the dosing frequency and improve the therapeutic efficacy of the drug. Nano-vesicles of brimonidine tartrate were prepared by film hydration method. The prepared vesicles were evaluated for photomicroscopic characteristics, entrapment efficiency, in vitro, and ex-in vitro drug release and in vivo intraocular pressure (IOP) lowering activity. The methods employed for preparation of vesicles produced nano vesicles of acceptable shape and size. The in vitro, and ex-in vitro drug release studies showed that there was slow and prolonged release of the drug, which followed zero-order kinetics. The IOP-lowering activity of nano vesicles was determined and compared with that of pure drug solution and showed that the IOP-lowering action of nano-vesicles sustained for a longer period of time. Stability studies revealed that the vesicle formulations were stable at the temperature range of 2-8°C, with no change in shape and drug content. The results of the study indicate that it is possible to develop a safe and physiologically effective topical formulation that is also convenient for patients