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Relational Care - with Mary Larkin and Manik Deepak-Gopinath [Podcast]
No full textWhat is 'relational care' and how can it improve the day-to-day experience of carers and those they care for? What are its implications for relationships between staff and service users in care settings? And how does the concept of relational care enable us to re-imagine the role of place and space in the experience of care? These are some of the questions we explore in this episode with Mary Larkin and Manik Deepak-Gopinath who recently completed a research project on the value and practice of relational care with older people.
Mary is Professor of Care, Carers and Caring at The Open University in the UK, where her research has focused on carers and caring and adult social care. She is the co-author, most recently of Family Carers and Caring, published in 2023 by Emerald. Manik is a Lecturer in Ageing, also at The Open University, and is a critical gerontologist with interests in the intersection of ageing, place and wellbeing, and in the intimate and family ties of older adults
My Name Is Deepak
No full textThis chapter looks at the author's responses to being given a nickname by his co-workers: Tupac. They do it in a friendly manner, but the author doesn’t understand the connection with the American rapper. It makes him think about who he is, his identity, and how people see him in his adopted country.</p
Cancer detection in human tissue samples using a fiber-tip pH probe
No full textAbstract not availableErik P. Schartner, Matthew R. Henderson, Malcolm Purdey, Deepak Dhatrak, Tanya M. Monro, P. Grantley Gill, and David F. Calle
Identification of vitamin D(3) target genes in human breast cancer tissue
No full textAvailable online 17 October 2015Abstract not availableLei Sheng, Paul H. Anderson, Andrew G. Turner, Kathleen I. Pishas, Deepak J. Dhatrak, Peter G. Gill, Howard A. Morris, David F. Calle
Sideffective - system to mine patient reviews: sentiment analysis
No full textSideffective is the system to crawl, rank and analyze patient testimonials about side ffeects from common medications. Since the wealth of any mining model is the Data corpus, the data collection phase involved extensive crawling of massive medical websites comprised of user forums from the internet. Subsequently, the raw files were subjected to certain site-specific parsing routines, yielding outputs conforming to a well-defined data model. Currently, the system holds close to 400,000 user testimonials pertaining to more than 2500 drugs/medicines. Sideffective aims at gathering and aggregating this wealth of information, build useful associations and present interesting observations and numeric validations, all in a user-friendly interface. The important issues that we have tried to tackle are: Extracting side effects without relying on pre-built lists, aggregating distribution of different side effect for a give drug, site-specific search, ranking and determining the negativity of reviews. The system has been jointly built by Deepak Yalamanchi and Sangeetha Rajagopalan under the guidance of Prof. Tomasz Imielinski. This thesis focuses mainly on Sentiment Analysis of patient reviews. While most existing sentiment analysis systems are predicated by POS (parts of speech) tagging or Bayesian sentiment analysis methods, the same cannot be applied to medical reviews as they generally carry a negative flavor in them. We thereby approached the problem by identifying the features in the sentence and calibrating the sentiment on a Negativity Meter based on their relation to sentiment words. A feature, as defined for the purpose of this thesis, can be a medicine, a side effect or a symptom. The sentiment of each feature is determined by the aggregate of all its polarities with respect to each sentiment word, where the polarity is determined by an inverse relation to the distance of the feature from the sentiment word. Each sentence is then evaluated by the cumulative polarity of all the features contained in it. Sentiment of a review is determined by individually determining the sentiment of each sentence and then getting a weighted sum score of all the sentences in the review. The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. Experimental results, involving human reviewers (extracted from site: www.askapatient.com) and correlating them back to the negativity rating of each review yield conclusive results, demonstrating the effectiveness of the technique. We have also implemented a customized Lucene search on the data using a multi-review summarization approach and a ranking scheme based on the feature-list. Ranking priority is given to the review that has the largest feature list size.M.S.Includes bibliographical referencesby Deepak Yalamanch
Febrile infection-related epilepsy syndrome is not caused by SCN1A mutations
No full textAbstract not availableDaniel Carranza Rojo, A. Simon Harvey, Xenia Iona, Leanne M. Dibbens, John A. Damiano, Todor Arsov, Deepak Gill, Jeremy L. Freeman, Richard J. Leventer, Angela Vincent, Samuel F. Berkovic, Jacinta M. McMahon, Ingrid E. Scheffe
Building Thermal Performance Varies During Extreme Heat within Cities
No full textabstract: This document has been superseded by our peer-reviewed publication:
Building Thermal Performance, Climate Change, and Urban Heat Vulnerability, Matthew Nahlik, Mikhail Chester, Stephanie Pincetl, David Eisenman, Deepak Sivaraman, and Paul English, 2017, ASCE Journal of Infrastructure Systems, 23(3), doi:10.1061/(ASCE)IS.1943-555X.0000349
The publication is available at: http://dx.doi.org/10.1061/(ASCE)IS.1943-555X.0000349
The leading source of weather-related deaths in the United States is heat, and future projections show that the frequency, duration, and intensity of heat events will increase in the Southwest. Presently, there is a dearth of knowledge about how infrastructure may perform during heat waves or could contribute to social vulnerability. To understand how buildings perform in heat and potentially stress people, indoor air temperature changes when air conditioning is inaccessible are modeled for building archetypes in Los Angeles, California, and Phoenix, Arizona, when air conditioning is inaccessible is estimated. An energy simulation model is used to estimate how quickly indoor air temperature changes when building archetypes are exposed to extreme heat. Building age and geometry (which together determine the building envelope material composition) are found to be the strongest indicators of thermal envelope performance. Older neighborhoods in Los Angeles and Phoenix (often more centrally located in the metropolitan areas) are found to contain the buildings whose interiors warm the fastest, raising particular concern because these regions are also forecast to experience temperature increases. To combat infrastructure vulnerability and provide heat refuge for residents, incentives should be adopted to strategically retrofit buildings where both socially vulnerable populations reside and increasing temperatures are forecast
Building and processing a dataset containing articles related to food adulteration
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 69).In this thesis, I explored the problem of building a dataset containing news articles related to adulteration, and curating this dataset in an automated fashion. In particular, we looked at food-adulterant co-existence detection, query reforumulation, and entity extraction and text deduplication. All proposed algorithms were implemented in Python, and performance was evaluated on multiple datasets. Methods described in this thesis can be generalized to other applications as well.by Deepak Narayanan.M. Eng
Digital front end for base-station RF
No full textThe digital front-end (DFE) is the most critical stage in a wireless base-station. The DFE along with the analog to digital converter (ADC) is responsible for bridging the analog RF and IF processing on one side and the digital baseband processing on the other side. The most important reason for replacing analog with digital signal processing is the ability to softly reconfigure the channels in the base station RF in real time, thus allowing for the implementation of various signal conditioning, compensation and mitigation channel non-linear responses. Once tested, these algorithms can be implemented on a proprietary CMOS vector processor and commercial FPGA hardware platforms. In this thesis, we attempt to minimize the design efforts and lower the cost involved in the usage of analog electronics by using sophisticated digital signal processing (DSP) for restoring and enhancing the quality of the wireless channels. This thesis presents a versatile Digital Front-End architecture, which has been simulated using MATLAB/Simulink. The architecture includes the design of robust Digital Up-Conversion (DUC) blocks in the transmit downlink and Digital Down-Conversion (DDC) blocks present in the receiver uplink paths in a wireless base station RF. Crest factor reduction (CFR) schemes help reduce the Peak to Average Power Ratio (PAPR)of the signal entering the base-station and have been implemented widely for code division multiple access (CDMA) and Long Term Evolution (LTE) systems, this is important because if the signal with the high PAPR is allowed to pass through the power amplifier(PA) it will result in the amplifier operating in its nonlinear region creating non-linear distortions in amplitude and phase, and the only other way to avoid this is to back off the signal to the linear region of the amplifier thus reducing its efficiency. The selection and design of the DUC and DDC filters has been compared and optimized to match to the spectral mask requirements mentioned in the 3GPP standards. Crest factor reduction has also been studied in detail and a computationally efficient algorithm for meeting the desired PAPR in accordance with the 3GPP standards will be presented. By using the CFR algorithm, the PAPR of the LTE signal was reduced from 10.8 dB to 7 dB and from 10.5 dB to 8 dB for a WCDMA signal. Finally, we implement Digital Predistortion (DPD) which is a method by which one first stimulates a non-linear power amplifier (PA) with baseband samples and then observes the result of that stimulus at its output. Without this process we will need to use a power amplifier with a higher input power rating which needs to be backed off to operate in its linear region thus reducing the efficiency of the PA used and increasing its cost. The process involves the use of a digital predistorter which creates an expanding nonlinearity which when used in cascade with the PA nullifies the compressing nonlinear characteristics of the PA thus enabling its use in its linear region up to its saturating point. A Look-Up Table (LUT) type Adaptive Digital Pre-Distortion (ADPD) is presented; here we developed an algorithm where the output signal of the PA is used as a reference signal. This reference signal is then used to update the coefficients of the LUT, so that the non-linear responses of the PA will not the affect the amplified signals. In addition, we investigated methods such as the nonlinear auto-regressive moving average (NARMA) and the memory polynomial models. In the latter, the predistorter parameters are calculated from the output signal obtained from the PA through the adaptive functions obtained using the memory polynomial. From these parameters, the predistorted signal is reconstructed and fed to the input of the PA. By using the DPD algorithm the nonlinear distortions of the PA came down by 60 dB when a WCDMA signal was used and by around 40 dB when LTE signal was used. As the PA is the heart of the base-station RF, we show that the main function of the DFE is to ensure a PA linearized output with a high efficiency.M.S.Includes bibliographical referencesby Deepak Gop
Will income inequality cause a middle-income trap in Asia? Bruegel Working Paper 2013/06, 10 October 2013
No full textThe Asian economy is expected to realise favourable growth during the first half of this century, but there is no guarantee. There is a discussion about a ‘middle-income trap’, which refers to a country that has realised rapid growth to become a middle-income country but is unable to grow further. A middle-income trap could occur not only if there is a delay in shifting the economy toward a productivity-driven structure, but also if there is a worsening of income distribution.We consider this in line with the theories of development economics and through a quantitative analysis. The relationship between income inequality and the trap can be explained by the Kuznets hypothesis and the basic-needs approach. Our quantitative analysis supports the Kuznets hypothesis, and indicates that,although a low-income country can accelerate its economic growth with the worsening of income distribution as an engine, a middle income country would experience a decreasing growth rate if it fails to narrow the income gap between the top and bottom income groups. The results also show that the basic-needs approach is also applicable in practice, and imply that the improvement of access to secondary education is important.
A sensitivity analysis for three Asian upper-middle-income countries(China, Malaysia and Thailand) also shows that the situation related to a middle-income trap is worse than average in China and Malaysia. These two countries, according to the result of the sensitivity analysis, should urgently improve access to secondary education and should implement income redistribution measures to develop high-tech industries, before their demographic dividends expire. Income redistribution includes the narrowing of rural urban income disparities, benefits to low-income individuals, direct income transfers, vouchers or free provision of education and health-care, and so on, but none of these are simple to implement
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