301 research outputs found
Precision Health and AI: improving health for everyone - Arjun Panesar (DDM Health)
No full textThis video is the twelfth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 14/09/2022.
Precision Health and AI: improving health for everyone - Arjun Panesar (DDM Health)
Bio: Arjun Panesar is the founder of DDM Health, providers of clinically-validated digital health solutions to over 1.8 million people. Benefiting from almost two decades of experience in big data, AI and AI ethics, Arjun leads the development of evidence-based digital innovations that harness the power of machine learning to provide precision medicine to patients, health services, and governments. Arjun’s work has received international recognition featuring in the Forbes, New Scientist, BBC and The Times. Arjun is a best-selling author on the topics of healthcare and AI, authoring two editions of Machine Learning and AI in Healthcare, and contributing to Handbook of Global Health, a major reference work. Arjun is an advisor to the Information School, University of Sheffield, Fellow to the NHS Innovation Accelerator, visiting lecturer at University of Warwick Medical School, and was recognised by Imperial College as an Alumni Leader for his contribution and impact to society.
Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/
This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1
YouTube Link: https://youtu.be/clPmdeLP5_
Financial Management of Globalization of Developing Countries
No full texthuman development, economic growth, globalization, inequality, poverty
Freedom of choice or force of circumstance? : Eastern European sex-workers in the Republic of Cyprus ; paper for the conference 'Alltag der Globalisierung. Perspektiven einer transnationalen Anthropologie', January 16-18, 2003, Institute of Cultural Anthropology and European Ethnology, Johann Wolfgang Goethe University, Frankfurt am Main
No full textThis paper focuses on Eastern European migrants who, since the beginning of the 1990s, are entering the Republic Cyprus as “artistes”. This is a visa permit status as well as an euphemism for short-term work permits in the local sex industry. In addition to exploring the migrational experiences of these women and their living and working conditions in the Republic of Cyprus, the paper reconstructs, empirically and analyt ically, the connection between immigration and the local sex industry. Here, several categories of social actors and institutions in Cyprus are actively involved. The rhetoric of government representatives, entrepreneurs and clients in the sex business on the one hand is contrasted with the discourse of local NGO representatives concerned with immigrants’ rights on the other hand. The paper comes to the conclusion that all of these discursive positions ultimately do not do justice to the complex process of decisionmaking that women undergo who migrate into the sex industry. Either, freedom of choice is emphasized – such as by entrepreneurs and the government – or the domination of women – as in the public statements of the NGO. In order to analyze the ambivalent tension between freedom of choice and submission to force by which the women’s decision is characterized, the author employs Michel Foucault’s concept of governmentality, which describes forms of political regulation that use the individual’s freedom of action as an instrument to exercise power
Fine-tuning generative models
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 73-76).Deep generative models have emerged as a powerful modeling paradigm for making sense of large amounts of unlabeled real-world data. In particular, the representations produced by these models have proven to be useful both in improving human understanding of the factors of variation in the original dataset and in downstream tasks such as classification. Most current algorithms, however, require training a bespoke model from scratch, which can be both expensive and time-consuming. Instead, we propose various methods of fine-tuning pre-trained generative models to achieve these goals, and evaluate these methods quantitatively on few-shot classification and interpretability tasks.by Arjun Khandelwal.M. Eng.M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc
Improving parking garage efficiency using reservation optimization techniques
No full textParking in urban and suburban districts is becoming increasingly problematic due the information gap between the parking garages and the average commuter. This thesis describes and evaluates techniques that can be implemented by parking garages to augment parking garage efficiency. The issues studied in this thesis were i) Real-time tracking of car position ii) maximizing the number of reservations made for the parking garage by re-arrangement of existing reservations (Reservation Defragmentation) and iii) maximizing revenue for the parking garage through increased occupancy (Revenue Management). For the tracking problem, in order to be able to track the real-time position of the vehicle inside the parking garage, we have proposed two techniques. The first one involves a high accuracy algorithm that takes as input a higher number of sensor values (high accuracy) and the second one is a lower cost algorithm that takes as input fewer number of sensor values (low cost). We simulated various conditions of sensor failure rate and determined our metric to be number of tracked points as a percentage of the path to the destination. We determined limitations of these algorithms with respect to maximum speed of cars and inter-car distance. For the reservation defragmentation problem, we looked at increasing occupancy efficiency for i) Next day reservations and ii) Current day reservations. For this problem, we implemented three algorithms. For next day reservations, we established metrics to determine the efficiency of the algorithm including number of free parking spots created and reduction in lengths of free space in between the reservations. For current day reservations, our metric was the increase in maximum occupancy observed due to defragmentation. For increased revenue management, we suggested the application of two techniques: Booking limits and Overbooking. In booking limits, two-fare class of parking was suggested and the number of spots that need to be reserved for higher class (Capacity of garage - booking limit) was determined for probability distributions of customer arrival such as Poisson distribution. Since the practice of overbooking is done in order to compensate for the no-shows that occur despite reservations made, we have suggested an algorithm to determine the amount of overbooking based on a Gaussian distribution of customer ‘no-shows’. We obtained the following results for the algorithms implemented. In case of the tracking algorithm, as the sensor failure rate increased, the inaccuracy of the two proposed algorithms also increased. For 2% failure rate, we track 0.4% of the incoming cars inaccurately (given that a tracking is marked as correct if 75% or less of all sensors along the path of the car fail). In case of reservation defragmentation, we obtained best results for Recursive First-Fit algorithm. For next day reservation defragmentation, using a mean of 15% cancellation of reservations resulted in 14.6% decrease in occupied parking spots which can then lead to increased occupancy and 46.3% decrease in inter-reservation free space sizes for a 1000 arrival reservation system. The reservations were exponentially distributed with a mean of 20 reservations/hour. For current day reservations, we were able to increase maximum occupancy of the parking garage by 5.5% using Recursive First Fit algorithm. Among other conditions, we have evaluated Poisson arrival distribution with corporate arrival rate 100 cars/hour (Flintsch et al., 2006) [56] and corporate fare twice of leisure fare. For this condition, protection level (number of parking slots reserved for corporate class) is determined to be 20% of garage capacity. We also evaluated Binomial distribution with probability of incoming customer to be corporate customer as 0.5 and corporate fare twice of leisure fare. For this condition, protection level is determined to be 50% of garage capacity. We evaluated overbooking for several combinations of No-show rates, mean and standard deviation values and the highest amount of overbooking we obtained was 1.93 times maximum garage capacity and this implies that permitting this number of reservations for the parking garage would minimize the number of parking spots being under-utilized and increase the revenue of the parking garage operator due to effective use of parking spots. The algorithms have been simulated for different arrival distributions (for Revenue Management), different arrival rates (tracking) as well as variable durations of stay (reservation defragmentation). Besides the problems mentioned, there are certain other aspects, such as generalizing the tracking algorithms for parking garages of arbitrary layouts represents the work that needs to be done in the future.M.S.Includes bibliographical referencesby Arjun Ra
Raw data for "Activation of oligonucleotide polyanions using collisions, electrons and photons in a timsOmni platform"
No full textRaw mass spectral data used to prepare the figures of the article "Activation of oligonucleotide polyanions using collisions, electrons and photons in a timsOmni platform", by Frédéric Rosu,1 Rim Chiba,1 Arjun Mani Mallika,1 Athanasios Smyrnakis,2 Jean-François Greisch,3 Dimitris Papanastasiou,2 Valérie Gabelica1*
1 School of Pharmaceutical Sciences, University of Geneva, 1205 Geneva, Switzerland.
2 Fasmatech Science & Technology, 15232 Chalandri, Athens, Greece.
3 Bruker Switzerland AG, 8117 Fällanden, Switzerland.
* Corresponding author: [email protected]
The data is organized by Table or Figure number as in the manuscript main text and supporting information
Stereo vision-based vehicular proximity estimation
No full textThis thesis describes an innovative and cost effective method to develop a low-cost following distance logging algorithm for volunteer participants which will allow quantitative research in driving behavior. Sparse stereo depth estimation methods along with a license plate localization algorithm has been used in order to achieve this. The depth is estimated by processing the video feeds from the stereo camera setup mounted inside a car looking out of the front window. License plate localization is used as a means to localize the position of the car within the image. Depth is estimated from the disparity, which is calculated using the rectified images of the frames from both the video streams.M.S.Includes bibliographical referencesby Arjun Krishn
Hospital Length of Stay Independently Predicts Mortality in Patients Emergently Admitted for Esophageal Hemorrhage: Sex, Frailty, and Age as Additional Mortality Factors
No full textINTRODUCTION: Upper gastrointestinal bleeding results in greater than $7.6 billion of in-hospital economic burden in the United States yearly. With a worldwide incidence between 40-100/100,000 individuals and a mortality rate of approximately 2-10%, upper gastrointestinal bleeding represents a major source of mortality and morbidity. The goal of this study was to describe mortality risk factors in patients emergently admitted with esophageal hemorrhage, the second most common etiology of upper gastrointestinal bleeding. MATERIALS AND METHODS: Patients emergently admitted with esophageal hemorrhage between 2005-2014 were evaluated using the National Inpatient Sample database. Patient characteristics, clinical outcomes, and therapeutic trends were obtained. Relationships between morality and all other variables were determined via univariable and multivariable logistic regression analyses. RESULTS: In total, 4,607 patients were included, of which 2,045 (44.4%) were adults, 2,562 (55.6%) were elderly, 2,761 (59.9%) were males, and 1,846 (40.1%) were females. The average age of adult and elderly patients were 50.1 and 78.7 years, respectively. The multivariable logistic regression analysis revealed, for every additional day of hospitalization, the odds of mortality for nonoperatively treated adult and elderly patients increased by 7.5% (p=\u3c0.001) and 6.6% (p=\u3c0.001), respectively. Every additional year of age was associated with a 5.4% (p=0.012) increase in mortality odds for nonoperatively managed adult patients. Frailty increased the odds of mortality by 31.1% (p=0.009) in nonoperatively treated elderly patients. Undergoing invasive diagnostic procedures in conservatively treated adults reduced mortality significantly (odds ratio=0.400, p=0.021). Frailty, age, and hospital length of stay demonstrated no significant association with mortality in surgically managed adult and elderly patients. CONCLUSION: Nonoperatively managed patients emergently admitted for esophageal hemorrhage with longer hospital length of stay and higher modified frailty index exhibited higher odds of mortality. Invasive diagnostic procedures were negatively correlated with mortality in nonoperatively treated adult patients. Age is only associated with higher mortality rates in adults, while elderly patients revealed no association between age and mortality
C4 the original pre-workout & explosive force
No full textC4 pre-workout can potentially increase force generated through its properties in the beverage, this has resulted in billions of servings of C4 pre-workout sold worldwide. Caffeine that is ingested 30 to 90 minutes prior to exercise has been shown to result in performance increases of up to 6% in events lasting from a few minutes to several hours. (Glaister & Gissane, 2018) Caffeine enhances peak power production, improves cognitive performance and enhances readiness to invest physical effort. (Duncan et al., 2019) Purpose: To determine if there is a significant difference in force when the C4 supplement is used vs. when our placebo is used.Not peer reviewedStudent Research Day Poster (2019
An Analysis of Coordination Mechanisms to Address Drought and Heatwave Climate Risks in the Beer Industry - A Heineken Case Study
No full textAmidst the burgeoning challenges of climate change, certain sectors, like the beer industry, find themselves at the forefront due to their dependence on climate-sensitive agricultural inputs. The changing climatic conditions not only influence the flavor profile but also the beer's accessibility, by affecting the quality and yield of essential ingredients such as barley and hops. This research delves into the beer industry's supply chains, which span regions increasingly vulnerable to climatic disruptions, thereby subjecting companies to notable physical risks like water shortages, crop failures, and transportation hindrances.The pertinence of this study extends beyond the beer industry, revealing broader implications for various business sectors. As supply chains of numerous sectors traverse climate-sensitive zones, they're endangered by hazards like droughts and heatwaves. A prominent challenge exacerbating these threats is poor coordination, evidenced by inconsistent communication, delayed responses to disruptions, supply chain opaqueness, and misaligned adaptive strategies.To address the overarching research question on effective coordination measures that the beer supply chain can adopt to mitigate vulnerabilities and bolster climate resilience, the study integrates both primary and secondary data. Primary data is sourced through semi-structured interviews, capturing insights from within the beer industry, while secondary data is derived from academic and industry repositories. The combined data offers a comprehensive perspective on the existing challenges and potential solutions for the beer supply chain.A deep dive into the industry's supply chain relationships elucidates the inherent complexities, informing subsequent analyses on viable coordination mechanisms to combat climate vulnerabilities. Heineken, an industry stalwart, plays a crucial role in this exploration, with the Institutional Analysis and Development (IAD) framework assisting in structuring the problem analysis. This framework is invaluable in scrutinizing institutional setups and their resultant impacts, while Heineken's strategic initiatives are evaluated using action situation analysis.One of the central themes emerging from the findings is the potential applicability of Elinor Ostrom's design principles. Originally conceived for effective common-pool resource management, these principles, emphasizing clear demarcation, rule-local condition congruence, and responsible governance, could be instrumental for the beer industry. They present a well-structured approach for devising coordination mechanisms, crucial in confronting collective environmental challenges.In summation, this research underscores the imperative for the beer industry, and by extension, other sectors, to consider Ostrom's principles. Their adoption could pave the way for resilient and sustainable supply chains, proficient in circumventing climate-induced risks. Such proactive measures are not merely pivotal for sustaining business operations but paramount in ushering an era of sustainable, climate-resilient business practices.Complex Systems Engineering and Management (CoSEM
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