This collection of short stories features characters who face unexpected situations arising from ordinary circumstances. Most of the characters find themselves compelled to react in ways that may even surprise themselves. A young woman finds her first feeling of joy in a long time in the face of her mother’s possible death. Best friends recall their years spent doing drugs and ignoring responsibility. When a woman confronts her fear of sex, she finds herself literally in another world. Rather than sticking with one form, several stories depart from traditional structures. One flash fiction piece is told in the first-person collective voice; another story evolves into magical realism; two are linked, and one story is told as an elegy. What matters are the characters, their struggles, and their relationships with one another.

The purpose of this study was to look at mission driven school district leadership in Massachusetts public schools and attempt to identify any relationship, or lack thereof, between district mission statements and student achievement. In this study, 288 Massachusetts public school districts are ranked according to their 2011 high school graduation rate and their 2011 tenth grade Massachusetts Comprehensive Assessment System (MCAS) results. From the 288 districts, a sample of the top thirty and the bottom thirty were selected. All district wide mission statements were obtained from the websites of the top 30 and bottom 30 school districts with a 100% return rate. The mission statements were then coded using Bebell and Stemler's 2011 coding rubric. Bebell and Stemler's coding rubric contains 11 themes with 42 subcategories. The 11 themes are: academic/cognitive, social development, emotional development, civic development, physical development, vocational preparation, integrate into local community, integrate into global community, integrate into spiritual community, safe nurturing environment, and challenging environment. The 42 subcategories are indicators composed of key words and phrases for each of the eleven themes in Bebell and Stemler's 2011 coding rubric. Results from the present study found the academic/cognitive theme occurred more than any other theme in both the top and bottom public school districts in Massachusetts. Statistical differences did appear for two of Bebell and Stemler's themes: civic development and vocational preparation. The civic development theme was correlated with the top 30 school districts, while the vocational theme was correlated with the bottom 30 school districts. Subcategories of the civic theme include productive, responsible, contributing members of society involved in public service and character education, while vocational subcategories include competition in the workforce and marketable skills. This study is limited in size and scope and more research is suggested. This study is unique because it is the first time that mission driven leadership in Massachusetts school districts is being analyzed to see if there is a connection with student achievement. The present study would be of interest to policy makers and practitioners who are interested in mission driven leadership and student achievement.

In this era of high stakes accountability, assessments are not only used as diagnostic tools, but they are also used to determine the effectiveness of school programs and personnel. Of the utmost importance is how principals use data to make instructional, intervention and planning decisions. The purpose of the current study was to determine principals’ perceptions regarding the importance, availability and utility of multiple sources of data in making their decisions and to determine their self-efficacy in M practices. This study was guided by 7 research questions and utilized 2 research designs. Descriptive research was used to answer research questions 1 through 6. Questions 1 through 3 sought to determine what data were available, used and important. Question 4 sought to determine the extent to principals relied on data to make decisions. Question 5 sought to determine the importance of different types of support for the effective use of data in decision-making. Question 6 sought to determine principals’ perceived self-efficacy in terms of effective data use. Question 7 was answered using correlational research to determine if principals’ measures of data use self-efficacy was related to student achievement. Overall, results showed that data surrounding student grades, attendance and discipline were most highly utilized in decision-making. All participating principals indicated that they either used data to a moderate degree or great degree when making decisions regarding development/revision of school improvement plan; inform parents of students’ progress/status/test scores; assignments of students to remedial programs; and improve classroom instruction. Data analysis further showed that principals indicated that school personnel trained in data analysis, sufficient time for data-analysis and staff development in the data analysis process are extremely important. Further analysis revealed that participating principals had high measures of data use self-efficacy and were highly certain that they could effectively use data. In the final analysis of the study, A Pearson’s r correlation coefficient was computed to assess the relationship between principals’ self-efficacy scores and student achievement. It was determined that there is no relationship between measures of principals’ data use perceived self-efficacy and student achievement. The study concludes with recommendations for future research.

The purpose of this research is to provide an extensive evaluation of neural network models for the prediction and the simulation of some key air pollutants in Hamilton, Ontario, Canada. Hamilton experiences one of Canada's highest air pollution exposures because of the dual problem associated with continuing industrial emission and gradual increase of traffic related emissions along with the transboundary air pollutions from heavily industrialized neighboring north-eastern and mid-western US cities. These factors combined with meteorology, cause large degradation of Hamilton's air quality. Hence an appropriate and robust method is of most importance in order to get an early notification of the future air quality situation. Data driven methods such as neural networks (NNs) are becoming very popular due to their inherent capability to capture the complex non-linear relationships between pollutants, climatic and other non-climatic variables such as traffic variables, emission factors, etc. This study investigates dynamic neural networks, namely time lagged feed-forward neural network (TLFN), Bayesian neural network (BNN) and recurrent neural network (RNN) for short term forecasting. The results are being compared with the benchmark static multilayer perceptron (MLP) models. The analysis shows that TLFN model with its time delay memory and RNN with its adaptive memory has outperformed the static MLP models in ground level ozone (O_3) forecasting for up to 12 hours ahead. Furthermore the model developed using the annual database is able to map the variations in the seasonal concentrations. On the other hand, MLP model was quite competitive for nitrogen dioxide (NO_2) prediction when compared to the dynamic NN based models. The study further assesses the ability of the neural network models to generate pollutant concentrations at sites where sampling has not been done. Using these neural network models, data values were generated for total suspended particulate (TSP) and inhalable particulates (PM_10) concentrations. The obtained results show promising potential. Although there were under-predictions and over-predictions on some occasions, the neural network models, in general were able to generate the missing information and to obtain air quality situation in the study area. / Thesis / Master of Applied Science (MASc)

The design of effective quality management strategies in groundwater systems is crucial, as clean water is essential for livelihood, health, and development. Colloids represent a class of contaminants that might be pathogenic or benign. Colloids can also enhance or inhibit the transport of dissolved contaminants in groundwater, which has inspired the use of benign colloids in the remediation of contaminated aquifers. Reliable modelling of colloid behavior is therefore essential for the design of effective remediation strategies, both those employing benign colloids and those aiming at the removal of pathogenic colloids. While colloid transport is controlled by groundwater velocity, colloid retention is governed by the physical and chemical properties of the aquifer together with those of the colloid. The present study aims at enhancing the reliability of modelling colloid behavior in fractured aquifers through: i) developing a synchronization-based framework that can effectively identify hydraulic connections within the aquifer; ii) developing a mathematical model for the relationship between the fraction of colloids retained along a fracture (Fr) and the parameters describing the aquifer’s physical and chemical properties; iii) developing an analytical model for the relationship between Fr and the coefficient describing irreversible colloid deposition in single fractures; and, iv) developing a numerical technique that can efficiently simulate colloid behavior in single fractures and fracture networks under different physical, chemical, and matrix conditions. The performance of the synchronization-based framework, mathematical and analytical models, and the numerical technique was assessed separately for different verification cases, and the corresponding efficacy was confirmed. Coupling the tools developed in the present study enables the reliable prediction of colloid behavior in response to changes in the groundwater-colloid-fracture system’s physical and chemical properties, which can aid in understanding how to manipulate the system’s properties for the effective design of groundwater quality management and remediation strategies. / Thesis / Doctor of Philosophy (PhD) / Microorganisms, microplastics, clay, and fine silt are classified as colloids within the spectrum of contaminants that might exist in groundwater. Although some colloids are benign (e.g., clay and fine silt), they can still affect the groundwater quality and aquifer porosity. Colloids can also enhance or inhibit the migration of other contaminants in groundwater because of their high adsorption capacity. Several remediation strategies are being envisioned to remove pathogenic colloids and eliminate other contaminants adsorbed onto benign colloids, where effective design of such strategies requires reliable models of colloid behavior. The present study aims at enhancing the reliability of simulating colloid behavior in fractured aquifers through: i) developing models that capture the effects of the aquifer’s physical and chemical properties on colloid behavior; and, ii) designing a framework that improves the reliability of aquifer conceptualization. Effective remediation strategies can then be designed for contaminated fractured aquifers based on the developed tools.

Projektet handlade om att ta fram ett koncept för att integrera en batteridriven motor med ett hjul till sjukhussängarna som används vid interna patientförflyttningar mellan avdelningarna på Norrlands universitetssjukhus (NUS). Avdelningen för patienttransporter på NUS är ansvariga för de dagliga interna patientförflyttningarna, detta sker cirka 10–40 gångar per dag med varierad sträcka upp till 500 m, och sängarna styrs i dagsläget manuellt. Vikten på sängen inklusive madrass, sjukvårdsutrustning och patienten kan vara upp till 450 kg. Syftet med projektet var att skapa ett affärsförslag för ett koncept som ska underlätta arbetet vid patientförflyttningarna. I slutändan ska detta leda till ett mer ergonomiskt arbetssätt. Målet var att ta fram förslag och designvarianter som ska underlätta patientförflyttningarna för personal som arbetar på patienttransportavdelningen, samt att föreslå de mest optimala komponenterna, till exempel vilken motor, batteri, hjul och fäste som kan användas i konceptet. Projektet genomförde genom att samla fakta, synpunkter och begränsningar från produktanvändaren via möten och telefonsamtal. Det gjordes också marknadsundersökningar via leverantörernas hemsidor och telefonsamtal till dem. Produkt-och designvarianter har skapats med hjälp av CAD-Solid Works. Resultatet visar att konceptets krav vid maximal last på 450 kg uppfylls om en batteridriven DC-motor på 1654 Watt används. Slutsatsen är att det finns andra motordrivna sjukhussängar på marknaden, men den nuvarande lösning har fördelen att den kostnadseffektiv kan integreras i befintliga sängar vid NUS. / The project was concerned with developing a concept to integrating a battery-powered motor with a wheel in the hospital beds that are used for internal patient transfers between departments at the Norrland University Hospital (NUS). The department for patient transport at NUS is responsible for the daily internal patient transfers, which takes place approximately 10–40 times per day with varied distances up to 500 m. The weight of the bed including mattress, healthcare equipment and the patient can be up to 450 kg. The purpose of the project was to create a business case for a concept that will facilitate work during patient transfers. Ultimately, this should lead to a more ergonomic way of working. The goal was to come up with a suggestions and design variants that will facilitate patient movements for staff working at the patient transport department, as well as to propose the most optimal components, for example which motor, battery, wheels and bracket that can be used in the concept. The project was carried out by collecting facts, opinions and limitations from the product user via meetings and telephone calls. Market research was also performed via the suppliers' websites and phone calls to them. Design variants have been created using CAD-Solid Works. The result shows that the concept's requirements are met at a maximum load of 450 kg if a battery-powered DC motor of 1654 Watt is used. The conclusion is that, although there are similar concepts on the market that meet the same goals, the proposed solution can easily and cost-efficiently be integrated in the existing hospital beds at NUS.

We develop a data-driven approach to Pareto optimal control of large-scale systems, where decision makers know only their local dynamics. Using reinforcement learning, we design a control strategy that optimally balances multiple objectives. The proposed method achieves near-optimal performance and scales well with the total dimension of the system. Experimental results demonstrate the effectiveness of our approach in managing multi-area power systems. / Master of Science / We have developed a new way to manage complex systems—like power networks—where each part only knows about its own behavior. By using a type of artificial intelligence known as reinforcement learning, we've designed a method that can handle multiple goals at once, ensuring that the entire system remains stable and works efficiently, no matter how large it gets. Our tests show that this method is particularly effective in coordinating different sections of power systems to work together smoothly. This could lead to more efficient and reliable power distribution in large networks.

The steady downscaling of CMOS technology has led to the development of devices with nanometer dimensions. Contemporaneously, maturity in technologies such as chemical self-assembly and DNA scaffolding has influenced the rapid development of non-CMOS nanodevices including vertical carbon nanotube (CNT) transistors and molecular switches. One main problem in manufacturing defect-free nanodevices, both CMOS and non-CMOS, is the inherent variability in nanoscale fabrication processes. Compared to current CMOS devices, nanodevices are also more susceptible to signal noise and thermal perturbations. One approach for developing robust digital systems from such unreliable nanodevices is to introduce defect- and fault-tolerance at the architecture level. Structurally redundant architectures, reconfigurable architectures and architectures that are a hybrid of the previous two have been proposed as potential defect- and fault-tolerant nanoscale architectures. Hence, the design of reliable nanoscale digital systems will require detailed architectural exploration. In this dissertation, we develop probabilistic methodologies and CAD tools to expedite the exploration of defect- and fault-tolerant architectures. These methodologies and tools will provide nanoscale system designers with the capability to carry out trade-off analysis in terms of area, delay, redundancy and reliability. During execution, the next state of a digital system is only dependent on the present state and the digital signals propagate in discrete time. Hence, we have used Markov processes to analyze the reliability of nanoscale digital architectures. Discrete Time Markov Chains (DTMCs) have been used to analyze logic architectures and Markov Decision processes (MDPs) have been used to analyze memory architectures. Since structurally redundant and reconfigurable nanoarchitectures may consist of millions of nanodevices, we have applied state space partitioning techniques and Belief propagation to scale these techniques. We have developed three toolsets based on these Markovian techniques. One of these toolsets has been specifically developed for the architectural exploration of molecular logic systems. The toolset can generate defect maps for isolating defective nanodevices and provide capabilities to organize structurally redundant fault-tolerant architectures with the non-defective devices. Design trade-offs for each of these architectures can be computed in terms of signal delay, area, redundancy and reliability. Another tool called HMAN (Hybrid Memory Analyzer) has been developed for analyzing molecular memory systems. Besides analyzing reliability-redundancy trade-offs using MDPs, HMAN provides a very accurate redundancy-delay trade-off analysis using HSPICE. SETRA (Scalable, Extensible Tool for Reliability Analysis) has been specifically designed for analyzing nanoscale CMOS logic architectures with DTMCs. SETRA also integrates well with current industry-standard CAD tools. It has been shown that multimodal computational models capture the operation of emerging nanoscale devices such as vertical CNT transistors, instead of the bimodal Boolean computational model that has been used to understand the operation of current electronic devices. We have extended an existing multimodal computational model based on Markov Random Fields (MRFs) for analyzing structurally redundant and reconfigurable architectures. Hence, this dissertation develops multiple probabilistic methodologies and tools for performing nanoscale architectural exploration. It also looks at different defect- and fault-tolerant architectures and explores different nanotechnologies. / Ph. D.

A cyber manufacturing system (CMS) is a concept generated from the cyber-physical system (CPS), providing adequate data and computation resources to support efficient and optimal decision making. Examples of these decisions include production control, variation reduction, and cost optimization. A CMS integrates the physical manufacturing equipment and computation resources via Industrial Internet, which provides low-cost Internet connections and control capability in the manufacturing networks. Traditional quality engineering methodologies, however, typically focus on statistical process control or run-to-run quality control through modeling and optimization of an individual process, which makes it less effective in a CMS with many manufacturing systems connected. In addition, more personalization in manufacturing generates limited samples for the same kind of product designs, materials, and specifications, which prohibits the use of many effective data-driven modeling methods. Motivated by Additive Manufacturing (AM) with the potential to manufacture products with a one-of-a-kind design, material, and specification, this dissertation will address the following three research questions: (1) how can in situ data be used to model multiple similar AM processes connected in a CMS (Chapter 3)? (2) How to improve the accuracy of the low-fidelity first-principle simulation (e.g., finite element analysis, FEA) for personalized AM products to validate the product and process designs (Chapter 4) in time? (3) And how to predict the void defect (i.e., unmeasurable quality variables) based on the in situ quality variables. By answering the above three research questions, the proposed methodology will effectively generate in situ process and quality data for modeling multiple connected AM processes in a CMS. The research to quantify the uncertainty of the simulated in situ process data and their impact on the overall AM modeling is out of the scope of this research. The proposed methodologies will be validated based on fused deposition modeling (FDM) processes and selective laser melting processes (SLM). Moreover, by comparing with the corresponding benchmark methods, the merits of the proposed methods are demonstrated in this dissertation. In addition, the proposed methods are inherently developed with a general data-driven framework. Therefore, they can also potentially be extended to other applications and manufacturing processes. / Doctor of Philosophy / Additive manufacturing (AM) is a promising advanced manufacturing process that can realize the personalized products in complex shapes with unprecedented materials. However, there are many quality issues that can restrict the wide deployment of AM in practice, such as voids, porosity, cracking, etc. To effectively model and further mitigate these quality issues, the cyber manufacturing system (CMS) is adopted. The CMS can provide the data acquisition functionality to collect the real-time process data which directly or indirectly related to the product quality in AM. Moreover, the CMS can provide the computation capability to analyze the AM data and support the decision-making to optimize the AM process. However, due to the characteristics of AM process, there are several challenges effectively and efficiently model the AM data. First, there are many one-of-a-kind products in AM, and leads to limited observations for each product that can support to estimate an accurate model. Therefore, in Chapter 3, I would like to discuss how to jointly model personalized products by sharing the information among these similar-but-non-identical AM processes with limited observations. Second, for personalized product realization in AM, it is essential to validate the product and process designs before fabrication quickly. Usually, finite element analysis (FEA) is employed to simulate the manufacturing process based on the first-principal model. However, due to the complexity, the high-fidelity simulation is very time-consuming and will delay the product realization in AM. Therefore, in Chapter 4, I would like to study how to predict the high-fidelity simulation result based on the low-fidelity simulation with fast computation speed and limited capability. Thirdly, the defects of AM are usually inside the product, and can be identified by the X-ray computed tomography (CT) images after the build of the AM products. However, limited by the sensor technology, CT image is difficult to obtain for online (i.e., layer-wise) defect detection to mitigate the defects. Therefore, as an alternative, I would like to investigate how to predict the CT image based on the optical layer-wise image, which can be obtained during the AM process in Chapter 5. The proposed methodologies will be validated based on two types of AM processes: fused deposition modeling (FDM) processes and selective laser melting processes (SLM).

Environmental testing is essential to certify systems to withstand the harsh dynamic loads they may experience in their service environment or during transport. For example, satel- lites are subjected to large vibration and acoustic loads when transported into orbit and need to be certified with tests that are representative of the anticipated loads. However, tra- ditional certification testing specifications can consist of sequential uniaxial vibration tests, which have been found to severely over- and under-test systems needing certification. The recently developed Impedance-Matched Multi-Axis Test (IMMAT) has been shown in the literature to improve upon traditional environmental testing practices through the use of multi-input multi-output testing and impedance matching. Additionally, with the use of numerical models, predictive simulations can be performed to determine optimal testing pa- rameters. Developing an accurate numerical model, however, requires precise knowledge of the system's dynamic characteristics, such as boundary conditions or material properties. These characteristics are not always available and would also require additional testing for verification. Furthermore, some systems may be extremely difficult to model using numerical methods because they contain millions of finite elements requiring impractical times scales to simulate or because they were fabricated before mainstream use of computer aided drafting and finite element analysis but are still in service. An alternative to numerical modeling is data-driven modeling, which does not require knowledge of a system's dynamic characteris- tics. The Continuous Residue Interpolation (CRI) method has been recently developed as a novel approach for building data-driven models of dynamical systems. CRI builds data- driven models by fitting smooth, continuous basis functions to a subset of frequency response function (FRF) measurements from a dynamical system. The resulting fitted basis functions can be sampled at any geometric location to approximate the expected FRF at that location. The research presented in this thesis explores the use of CRI-derived data-driven models in predictive simulations for the IMMAT performed on a Euler-Bernoulli beam. The results of the simulations reveal that CRI-derived data-driven models of a Euler-Bernoulli beam achieve similar performance when compared to a finite element model and make similar decisions when deciding the excitation locations in an IMMAT. / Master of Science / In the field of vibrations testing, environmental tests are used to ensure that critical devices or structures can withstand harsh vibration environments. For example, satellites experience harsh vibrations and damaging acoustics that are transferred from it's rocket transport vehicle. Traditional environmental tests would require that the satellite be placed on a vibration table and sequentially vibrated in multiple orientations for a specified duration and intensity. However, these traditional environmental tests do not always produce vibrations that are representative of the anticipated transport or operational environment. Newly developed methods, such as the Impedance-Matched Multi-Axis Test (IMMAT) methods achieves representative test results by matching the mounting characteristics of the structure during it's transport or operational environment and vibrating the structure in multiple directions simultaneously. An IMMAT can also be optimized by using finite element models (FEM), which approximate the device to be tested with a discrete number of small volumes whose physics are described by fundamental equations of motion. However, an FEM can only be used if it's dynamic characteristics are sufficiently similar to the structure undergoing testing. This can only be achieved with precise knowledge of the dynamical properties of the structure, which is not always available. An alternate approach to an FEM is to use a data-driven model. Because data-driven models are made using data from the system it is supposed to describe, dynamical properties of the device are pre-built in the model and is not necessary to approximate them. Continuous Residue Interpolation (CRI) is a recently developed data-driven modeling scheme that approximates a structure's dynamic properties with smooth, continuous functions updated with measurements of the input-output response dynamics of the device. This thesis presents the performance of data-driven models generated using CRI when used in predictive simulations of an IMMAT. The results show that CRI- derived data-driven models perform similarly to FEMs and make similar predictions for optimal input vibration locations.