6,827 research outputs found
Designing of a prototype heat-sealer to manufacture solar water sterilization pouches for use in developing nations
Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.Includes bibliographical references (leaf 23).Water purification proves to be a difficult task in many developing nations. The SODIS (SOlar water DISinfection) process is a method which improves the microbiological quality of water making it safer for drinking and cooking using the UV-A rays and heat from the sun. Even simple processes such as this, require components that are not easily attainable in many rural areas-in this case the recommended two-liter bottle. Amy Smith, an instructor in MIT's Edgerton Center, researched and tested the effectiveness of polypropylene collapsible water pouches in the SODIS process. Thus, a heat-sealing device that can be used in developing nations to manufacture collapsible water pouches is needed. This device is intended to allow individuals in developing countries to take advantage of the SODIS water purification process. The approximately 60 watt prototype of the heat-sealing device is powered by a 12-volt solar deep-cycle battery and is made of simple materials so that it can be used and maintained in a variety of developing nations. A 20 inch nickel chromium strip is used as the heating element and Teflon forms a barrier between the heating element and the material to be sealed. A 4-mil polypropylene sheet is the pouch material of choice.(cont.) It is placed on top of the Teflon strip, before a lever arm is lowered, the device is turned 'on' and the sheet is sealed via the heated nickel chromium strip. Although the alpha prototype presented in this thesis has a number of positive attributes, such as using easily accessible or shippable components and making use of available power sources and/or batteries, there are areas for improvement. Making the device more robust, user friendly and versatile and making the seal strength more consistent and accurate are important characteristics that should be considered when designing a beta prototype.by Saundra S. Quinlan.S.B
Characterization of Power-to-Phase Conversion in High-Speed P-I-N Photodiodes
Fluctuations of the optical power incident on a photodiode can be converted
into phase fluctuations of the resulting electronic signal due to nonlinear
saturation in the semiconductor. This impacts overall timing stability (phase
noise) of microwave signals generated from a photodetected optical pulse train.
In this paper, we describe and utilize techniques to characterize this
conversion of amplitude noise to phase noise for several high-speed (>10 GHz)
InGaAs P-I-N photodiodes operated at 900 nm. We focus on the impact of this
effect on the photonic generation of low phase noise 10 GHz microwave signals
and show that a combination of low laser amplitude noise, appropriate
photodiode design, and optimum average photocurrent is required to achieve
phase noise at or below -100 dBc/Hz at 1 Hz offset a 10 GHz carrier. In some
photodiodes we find specific photocurrents where the power-to-phase conversion
factor is observed to go to zero
Experimental Realization of a Single-Phase Five Level Inverter for PV Applications
voltage-controlled, single-phase, five-level inverter for photovoltaic systems using semiconductor power devices is proposed. Use of a unique, multilevel voltage source configuration allows the production of high voltage, low harmonic distortion AC outputs without using transformers or series-associated synchronized switching devices. The typical role of multi-level inverters is to generate the desired AC voltage from multiple DC voltage rails. Therefore multi-level inverters can provide high power AC outputs with good efficiency. The inverter design proposed here has superior voltage regulation, a low-distortion output and improved efficiency compared to existing multi-level inverters. Complete functionality has been verified using both MATLAB/SIMULINK simulation software and experimental trial
Learning a Static Analyzer from Data
To be practically useful, modern static analyzers must precisely model the
effect of both, statements in the programming language as well as frameworks
used by the program under analysis. While important, manually addressing these
challenges is difficult for at least two reasons: (i) the effects on the
overall analysis can be non-trivial, and (ii) as the size and complexity of
modern libraries increase, so is the number of cases the analysis must handle.
In this paper we present a new, automated approach for creating static
analyzers: instead of manually providing the various inference rules of the
analyzer, the key idea is to learn these rules from a dataset of programs. Our
method consists of two ingredients: (i) a synthesis algorithm capable of
learning a candidate analyzer from a given dataset, and (ii) a counter-example
guided learning procedure which generates new programs beyond those in the
initial dataset, critical for discovering corner cases and ensuring the learned
analysis generalizes to unseen programs.
We implemented and instantiated our approach to the task of learning
JavaScript static analysis rules for a subset of points-to analysis and for
allocation sites analysis. These are challenging yet important problems that
have received significant research attention. We show that our approach is
effective: our system automatically discovered practical and useful inference
rules for many cases that are tricky to manually identify and are missed by
state-of-the-art, manually tuned analyzers
Predicting bloc support in Irish general elections 1951–2020: A political history model
Election forecasting is a growing enterprise. Structural models relying on “fundamental” political and economic variables, principally to predict government performance, are popular in political science. Conventional wisdom though is these standard structural models fall short in predicting individual blocs’ performance and their applicability to multiparty systems is restricted. We challenge this by providing a structural forecast of bloc performance in Ireland, a case primarily overlooked in the election forecasting literature. Our model spurns the economic and performance variables conventionally associated with structural forecasting enterprises and instead concentrates on Ireland’s historical party and governance dynamics in the vein of testing whether these patterns alone offer solid predictions of election outcomes. Using Seemingly Unrelated Regression (SUR), our approach, comprising measures of incumbency, short-term party support, and political and economic shocks, offers reasonable predictions of the vote share performance of four blocs: Ireland's two major parties, Fianna Fáil and Fine Gael, Independents, and the Left bloc combined across 20 elections spanning 60 years
A political economy forecast of Ireland’s 2020 general election: government seat losses less than assumed?
Ireland votes in a general election on Saturday, 8 February. Michael S. Lewis-Beck and Stephen Quinlan explain how a new forecast model suggests that Leo Varadkar’s Fine Gael will lose seats, but perhaps fewer than opinion polls currently suggest
Strengthening Collegiality to Enhance Teaching, Research, and Scholarly Practice: An Untapped Resource for Faculty Development
Collegiality lies at the intersection of various aspects of academic practice, including teaching as well as research. As such, assisting junior faculty in learning to build their collegial networks becomes a powerful point of intervention for faculty developers, even for those who focus on teaching development. Data from interviews with faculty engaged in both teaching and research, plus our experiences in conducting a series of career building initiatives are analyzed to identify junior faculty perceptions of the role of collegiality and barriers to establishing collegial ties. Two main barriers are identified: 1) knowing that collegiality and networking is important, and 2) knowing how to go about establishing oneself as a colleague. Recommendations are then offered to faculty developers for working with junior faculty to help address each of those barriers, drawing on the authors’ experiments with various workshops and forums
The Merging History of Massive Black Holes
We investigate a hierarchical structure formation scenario describing the
evolution of a Super Massive Black Holes (SMBHs) population. The seeds of the
local SMBHs are assumed to be 'pregalactic' black holes, remnants of the first
POPIII stars. As these pregalactic holes become incorporated through a series
of mergers into larger and larger halos, they sink to the center owing to
dynamical friction, accrete a fraction of the gas in the merger remnant to
become supermassive, form a binary system, and eventually coalesce. A simple
model in which the damage done to a stellar cusps by decaying BH pairs is
cumulative is able to reproduce the observed scaling relation between galaxy
luminosity and core size. An accretion model connecting quasar activity with
major mergers and the observed BH mass-velocity dispersion correlation
reproduces remarkably well the observed luminosity function of
optically-selected quasars in the redshift range 1<z<5. We finally asses the
potential observability of the gravitational wave background generated by the
cosmic evolution of SMBH binaries by the planned space-born interferometer
LISA.Comment: 4 pages, 2 figures, Contribute to "Multiwavelength Cosmology",
Mykonos, Greece, June 17-20, 200
A Non-Sequential Representation of Sequential Data for Churn Prediction
We investigate the length of event sequence giving best predictions
when using a continuous HMM approach to churn prediction from sequential
data. Motivated by observations that predictions based on only the few most recent
events seem to be the most accurate, a non-sequential dataset is constructed
from customer event histories by averaging features of the last few events. A simple
K-nearest neighbor algorithm on this dataset is found to give significantly
improved performance. It is quite intuitive to think that most people will react
only to events in the fairly recent past. Events related to telecommunications occurring
months or years ago are unlikely to have a large impact on a customer’s
future behaviour, and these results bear this out. Methods that deal with sequential
data also tend to be much more complex than those dealing with simple nontemporal
data, giving an added benefit to expressing the recent information in a
non-sequential manner
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