A Rapid Construction Technique for Bridge Abutments Using Controlled Low Strength Materials (CLSM)

The required time for building bridge abutments is one of the key obstacles facing rapid bridge construction. For typical span bridges, this can be remedied by using Controlled Low Strength Materials (CLSM) as backfill materials placed behind full-height precast concrete panels that are integrated with the CLSM backfill via steel anchors. CLSM bridge abutments can be constructed in a short time as they do not require heavy machinery for excavation, compaction, and piling equipment. The main objective of this study was to examine the behavior of an instrumented laboratory large-scale CLSM bridge abutment with full-height precast concrete panels that was subjected to a monotonically increasing sill (foundation) pressure. The experiment showed that the CLSM bridge abutment, with a relatively short cure time of 7 days, is capable of carrying typical bridge loads with a reasonably large safety margin, and with minimal deformations. To select a suitable CLSM mixture proportion, several mixtures were developed and tested in the laboratory for engineering properties including flowability, density, compressive strength and stress-strain behavior. The main performance criteria for selection of a potential CLSM mixture were compressive strength to support the bridge loads, excavatability and flowability to fill the entire abutment in one continuous pour. Since it was a critical area of concern in design of the CLSM bridge abutment, the bond strength performance of the CLSM to steel anchors was also investigated. In pullout tests, a CLSM mixture with higher compressive strength resulted in higher bond strength and more brittle slippage. A numerical simulation of pullout tests indicated that the bond strength decreases with increase in bar size and embedment length. Finite element method (FEM) of analysis was implemented to simulate and explore the performance of CLSM bridge abutments based on bearing pressure capacity, displacements, and the developed axial force in anchors, and to provide an assessment of safety of the design. The accuracy of the finite element results for the response and failure behavior of a CLSM mass was evaluated by a comparison with the experimental results. Good agreement was obtained between the numerical and experimental results. The validated finite element (FE) model was then used for conducting a series of parametric studies to define the effects of CLSM compressive strength, curing age, environment temperature and construction details on response of the abutments. It was also learned that the computed and measured lateral displacements for the facing panels were negligible up to about 70% of the bearing pressure capacity of the abutment when a longitudinal crack developed in the CLSM backfill close to the facing wall

A User-aware Intelligent Refactoring for Discrete and Continuous Software Integration

Successful software products evolve through a process of continual change. However, this process may weaken the design of the software and make it unnecessarily complex, leading to significantly reduced productivity and increased fault-proneness. Refactoring improves the software design while preserving overall functionality and behavior, and is an important technique in managing the growing complexity of software systems. Most of the existing work on software refactoring uses either an entirely manual or a fully automated approach. Manual refactoring is time-consuming, error-prone and unsuitable for large-scale, radical refactoring. Furthermore, fully automated refactoring yields a static list of refactorings which, when applied, leads to a new and often hard to comprehend design. In addition, it is challenging to merge these refactorings with other changes performed in parallel by developers. In this thesis, we propose a refactoring recommendation approach that dynamically adapts and interactively suggests refactorings to developers and takes their feedback into consideration. Our approach uses Non-dominated Sorting Genetic Algorithm (NSGAII) to find a set of good refactoring solutions that improve software quality while minimizing the deviation from the initial design. These refactoring solutions are then analyzed to extract interesting common features between them such as the frequently occurring refactorings in the best non-dominated solutions. We combined our interactive approach and unsupervised learning to reduce the developer’s interaction effort when refactoring a system. The unsupervised learning algorithm clusters the different trade-off solutions, called the Pareto front, to guide the developers in selecting their region of interests and reduce the number of refactoring options to explore. To reduce the interaction effort, we propose an approach to convert multi-objective search into a mono-objective one after interacting with the developer to identify a good refactoring solution based on their preferences. Since developers may want to focus on specific code locations, the ”Decision Space” is also important. Therefore, our interactive approach enables developers to pinpoint their preference simultaneously in the objective (quality metrics) and decision (code location) spaces. Due to an urgent need for refactoring tools that can support continuous integration and some recent development processes such as DevOps that are based on rapid releases, we propose, for the first time, an intelligent software refactoring bot, called RefBot. Our bot continuously monitors the software repository and find the best sequence of refactorings to fix the quality issues in Continous Integration/Continous Development (CI/CD) environments as a set of pull-requests generated after mining previous code changes to understand the profile of developers. We quantitatively and qualitatively evaluated the performance and effectiveness of our proposed approaches via a set of studies conducted with experienced developers who used our tools on both open source and industry projects.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154775/1/Vahid Alizadeh Final Dissertation.pdfDescription of Vahid Alizadeh Final Dissertation.pdf : Dissertatio

(6,6′-Dimethyl-2,2′-bipyridine-κ2 N,N′)diiodidomercury(II)

In the title complex, [HgI2(C12H12N2)], the HgII atom has a distorted tetra­hedral coordination formed by two N atoms of the 6,6′-dimethyl-2,2′-bipyridine ligand and two terminal I atoms [N—Hg—N = 70.1 (2) and I—Hg—I = 130.59 (3)°]. The crystal packing features π–π contacts between the pyridine rings of adjacent mol­ecules [centroid–centroid distance = 3.773 (5) Å] and also between a pyridine ring of one mol­ecule and the five-membered chelate ring of an adjacent mol­ecule [centroid–centroid distance = 3.668 (4) Å]

30 Years of Software Refactoring Research: A Systematic Literature Review

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155872/4/30YRefactoring.pd

Interactive Refactoring via Clustering-Based Multi-objective Search

http://deepblue.lib.umich.edu/bitstream/2027.42/153328/1/ASE2018_Clustering_The_Pareto_Optimal_Solutions__Copy_DeepBlue.pd

The Factors Predicting Quality of Life in Elderly People in Kerman Using PRECEDE Model

Background: As life expectancy and the population of old people increases, quality of life in elderly people becomes more important. The aim of this study was to determine the factors predicting quality of life in elderly people using PRECEDE model. Methods: This is a cross-sectional descriptive-analytical study. Data were collected using the World Health Organization Quality of Life (WHOQOL)–BREF questionnaire and another questionnaire including 67 questions according to the PRECEDE model components, which its reliability and validity were approved. Multi-stage random sampling method was used in two healthcare centers in Kerman and 80 elderly people were selected. Date were analyzed using linear regression and statistical indices via SPSS 15. Results: The mean age of participants was 67.7±7.1 years old, and most of them were female and married. The quality of life of elderly people was moderate. Among components of the PRECEDE model, knowledge (a component of predisposing factors) and enabling factors were the most significant factors predicting quality of life among the participants (R2 =0.46). Conclusion: According to the results, enabling factors and knowledge (a component of predisposing factors) were identified as the most important factors predicting quality of life. Therefore, more focus on these factors in educational programs for elderly people is recommended

(6,6′-Dimethyl-2,2′-bipyridine-κ2 N,N′)diiodidozinc(II)

The complete mol­ecule of the title compound, [ZnI2(C12H12N2)], is generated by crystallograpic twofold symmetry, with the ZnII atom lying on the rotation axis. The ZnII atom is coordinated by the N,N-bidentate 6,6′-dimethyl-2,2′-bipyridine ligand and two iodide ions, resulting in a distorted ZnN2I2 tetra­hedral geometry for the metal. In the crystal, there are weak π–π contacts between the pyridine rings [centroid–centroid distance = 3.978 (3) Å]