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

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

The Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes

The measurement of student performance during their progress through university study provides academic leadership with critical information on each student’s likelihood of success. Academics have traditionally used their interactions with individual students through class activities and interim assessments to identify those “at risk” of failure/withdrawal. However, modern university environments, offering easy on-line availability of course material, may see reduced lecture/tutorial attendance, making such identification more challenging. Modern data mining and machine learning techniques provide increasingly accurate predictions of student examination assessment marks, although these approaches have focussed upon large student populations and wide ranges of data attributes per student. However, many university modules comprise relatively small student cohorts, with institutional protocols limiting the student attributes available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. We describe an experiment conducted on a final-year university module student cohort of 23, where individual student data are limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. We found potential for predicting individual student interim and final assessment marks in small student cohorts with very limited attributes and that these predictions could be useful to support module leaders in identifying students potentially “at risk.”.Peer reviewe