Multi-stage scheduling problems with precedence constraints

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Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies

Energy Conservation Measure (ECM) project selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. Many of these energy efficient retrofit projects should be viewed as a series of investments with annual returns for these traditionally risk-averse agencies. Given a list of ECMs available, federal, state and local agencies must determine how to implement projects at lowest costs. The most common methods of implementation planning are suboptimal relative to cost. Federal, state and local agencies can obtain greater returns on their energy conservation investment over traditional methods, regardless of the implementing organization. This dissertation outlines several approaches to improve the traditional energy conservations models. Any public buildings in regions with similar energy conservation goals in the United States or internationally can also benefit greatly from this research. Additionally, many private owners of buildings are under mandates to conserve energy e.g., Local Law 85 of the New York City Energy Conservation Code requires any building, public or private, to meet the most current energy code for any alteration or renovation. Thus, both public and private stakeholders can benefit from this research. The research in this dissertation advances and presents models that decision-makers can use to optimize the selection of ECM projects with respect to the total cost of implementation. A practical application of a two-level mathematical program with equilibrium constraints (MPEC) improves the current best practice for agencies concerned with making the most cost-effective selection leveraging energy services companies or utilities. The two-level model maximizes savings to the agency and profit to the energy services companies (Chapter 2). An additional model presented leverages a single congressional appropriation to implement ECM projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of “of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency’s traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework.

Multi-resistance states in the electrical switching behavior of amorphous Si15Te75Ge10 thin films: Possibility of multi-bit storage

Electrical switching studies on amorphous Si15Te75Ge10 thin film devices reveal the existence of two distinct, stable low-resistance, SET states, achieved by varying the electrical input to the device. The multiple resistance levels can be attributed to multi-stage crystallization, as observed from temperature dependant resistance studies. The devices are tested for their ability to be RESET with minimal resistance degradation; further, they exhibit a minimal drift in the SET resistance value even after several months of switching. (c) 2013 Elsevier B.V. All rights reserved.

Performance of balanced two-stage empirical predictors of realized cluster latent values from finite populations: A simulation study

Predictors of random effects are usually based on the popular mixed effects (ME) model developed under the assumption that the sample is obtained from a conceptual infinite population; such predictors are employed even when the actual population is finite. Two alternatives that incorporate the finite nature of the population are obtained from the superpopulation model proposed by Scott and Smith (1969. Estimation in multi-stage surveys. J. Amer. Statist. Assoc. 64, 830-840) or from the finite population mixed model recently proposed by Stanek and Singer (2004. Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 1119-1130). Predictors derived under the latter model with the additional assumptions that all variance components are known and that within-cluster variances are equal have smaller mean squared error (MSE) than the competitors based on either the ME or Scott and Smith`s models. As population variances are rarely known, we propose method of moment estimators to obtain empirical predictors and conduct a simulation study to evaluate their performance. The results suggest that the finite population mixed model empirical predictor is more stable than its competitors since, in terms of MSE, it is either the best or the second best and when second best, its performance lies within acceptable limits. When both cluster and unit intra-class correlation coefficients are very high (e.g., 0.95 or more), the performance of the empirical predictors derived under the three models is similar. (c) 2007 Elsevier B.V. All rights reserved.

Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks

This paper presents a multi-stage algorithm for the dynamic condition monitoring of a gear. The algorithm provides information referred to the gear status (fault or normal condition) and estimates the mesh stiffness per shaft revolution in case that any abnormality is detected. In the first stage, the analysis of coefficients generated through discrete wavelet transformation (DWT) is proposed as a fault detection and localization tool. The second stage consists in establishing the mesh stiffness reduction associated with local failures by applying a supervised learning mode and coupled with analytical models. To do this, a multi-layer perceptron neural network has been configured using as input features statistical parameters sensitive to torsional stiffness decrease and derived from wavelet transforms of the response signal. The proposed method is applied to the gear condition monitoring and results show that it can update the mesh dynamic properties of the gear on line.

The research pathway to developing a health promotion intervention for the high school curriculum

Introduction: Schools provide the opportunity to reach a large number of adolescents in a systematic way however there are increasing demands on curriculum providing challenges for health promotion activities. This paper will describe the research processes and strategies used to design an injury prevention program.----- Methods: A multi-stage process of data collection included: (1) Surveys on injury-risk behaviours to identify targets of change (examining behaviour and risk/ protective factors among more than 4000 adolescents); (2) Focus groups (n= 30 high-risk adolescents) to understand and determine risk situations; (3) Hospital emergency outpatients survey to understand injury types/ situations; (4) Workshop (n= 50 teachers/ administrators) to understand the target curriculum and experiences with injury-risk behaviours; (5) Additional focus groups (students and teachers) regarding draft material and processes.----- Results: Summaries of findings from each stage are presented particularly demonstrating the design process. The baseline data identified target risk and protective factors. The following qualitative study provided detail about content and context and with the hospital findings assisted in developing ways to ensure relevance and meaning (e.g. identifying high risk situations and providing insights into language, culture and development). School staff identified links to school processes with final data providing feedback on curriculum fit, feasibility and appropriateness of resources. The data were integrated into a program which demonstrated reduced injury.----- Conclusions: A comprehensive research process is required to develop an informed and effective intervention. The next stage of a cluster randomised control trial is a major task and justifies the intensive and comprehensive development.

The measurement of perceived ontological deficiencies of conceptual modeling grammars

Over recent years, many scholars have studied the conceptual modeling of information systems based on a theory of ontological expressiveness. This theory offers four constructs that inform properties of modeling grammars in the form of ontological deficiencies, and their implications for development and use of conceptual modeling in IS practice. In this paper we report on the development of a valid and reliable instrument for measuring the perceptions that individuals have of the ontological deficiencies of conceptual modeling grammars. We describe a multi-stage approach for instrument development that incorporates feedback from expert and user panels. We also report on a field test of the instrument with 590 modeling practitioners. We further study how different levels of modeling experience influence user perceptions of ontological deficiencies of modeling grammars. We provide implications for practice and future research.

Anxiety among high school students in India : comparisons across gender, school type, social strata, and perceptions of quality time with parents

The broad objective of the study was to better understand anxiety among adolescents in Kolkata city, India. Specifically, the study compared anxiety across gender, school type, socio-economic background and mothers’ employment status. The study also examined adolescents’ perceptions of quality time with their parents. A group of 460 adolescents (220 boys and 240 girls), aged 13-17 years were recruited to participate in the study via a multi-stage sampling technique. The data were collected using a self-report semi-structured questionnaire and a standardized psychological test, the State-Trait Anxiety Inventory. Results show that anxiety was prevalent in the sample with 20.1% of boys and 17.9% of girls found to be suffering from high anxiety. More boys were anxious than girls (p<0.01). Adolescents from Bengali medium schools were more anxious than adolescents from English medium schools (p<0.01). Adolescents belonging to the middle class (middle socio-economic group) suffered more anxiety than those from both high and low socio-economic groups (p<0.01). Adolescents with working mothers were found to be more anxious (p<0.01). Results also show that a substantial proportion of the adolescents perceived they did not receive quality time from fathers (32.1%) and mothers (21.3%). A large number of them also did not feel comfortable to share their personal issues with their parents (60.0% for fathers and 40.0% for mothers).

GYAN: A methodology for rule extraction from artificial neural networks

Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system. A multi-stage methodology GYAN is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of GYAN is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.

A measurement instrument for process modeling research : development, test and procedural model

Process modeling is an emergent area of Information Systems research that is characterized through an abundance of conceptual work with little empirical research. To fill this gap, this paper reports on the development and validation of an instrument to measure user acceptance of process modeling grammars. We advance an extended model for a multi-stage measurement instrument development procedure, which incorporates feedback from both expert and user panels. We identify two main contributions: First, we provide a validated measurement instrument for the study of user acceptance of process modeling grammars, which can be used to assist in further empirical studies that investigate phenomena associated with the business process modeling domain. Second, in doing so, we describe in detail a procedural model for developing measurement instruments that ensures high levels of reliability and validity, which may assist fellow scholars in executing their empirical research.

Phosphorylation of Exo1 modulates homologous recombination repair of DNA double-strand breaks

DNA double-strand break (DSB) repair via the homologous recombination pathway is a multi-stage process, which results in repair of the DSB without loss of genetic information or fidelity. One essential step in this process is the generation of extended single-stranded DNA (ssDNA) regions at the break site. This ssDNA serves to induce cell cycle checkpoints and is required for Rad51 mediated strand invasion of the sister chromatid. Here, we show that human Exonuclease 1 (Exo1) is required for the normal repair of DSBs by HR. Cells depleted of Exo1 show chromosomal instability and hypersensitivity to ionising radiation (IR) exposure. We find that Exo1 accumulates rapidly at DSBs and is required for the recruitment of RPA and Rad51 to sites of DSBs, suggesting a role for Exo1 in ssDNA generation. Interestingly, the phosphorylation of Exo1 by ATM appears to regulate the activity of Exo1 following resection, allowing optimal Rad51 loading and the completion of HR repair. These data establish a role for Exo1 in resection of DSBs in human cells, highlighting the critical requirement of Exo1 for DSB repair via HR and thus the maintenance of genomic stability.

Strategic management practices in the construction industry : a study of Indonesian enterprises

Since the establishment of the first national strategic development plan in the early 1970s, the construction industry has played an important role in terms of the economic, social and cultural development of Indonesia. The industry’s contribution to Indonesia’s gross domestic product (GDP) increased from 3.9% in 1973 to 7.7% in 2007. Business Monitoring International (2009) forecasts that Indonesia is home to one of the fastest-growing construction industries in Asia despite the average construction growth rate being expected to remain under 10% over the period 2006 – 2010. Similarly, Howlett and Powell (2006) place Indonesia as one of the 20 largest construction markets in 2010. Although the prospects for the Indonesian construction industry are now very promising, many local construction firms still face serious difficulties, such as poor performance and low competitiveness. There are two main reasons behind this problem: the environment that they face is not favourable; the other is the lack of strategic direction to improve competitiveness and performance. Furthermore, although strategic management has now become more widely used by many large construction firms in developed countries, practical examples and empirical studies related to the Indonesian construction industry remain scarce. In addition, research endeavours related to these topics in developing countries appear to be limited. This has potentially become one of the factors hampering efforts to guide Indonesian construction enterprises. This research aims to construct a conceptual model to enable Indonesian construction enterprises to develop a sound long-term corporate strategy that generates competitive advantage and superior performance. The conceptual model seeks to address the main prescription of a dynamic capabilities framework (Teece, Pisano & Shuen, 1997; Teece, 2007) within the context of the Indonesian construction industry. It is hypothesised that in a rapidly changing and varied environment, competitive success arises from the continuous development and reconfiguration of firm’s specific assets achieving competitive advantage is not only dependent on the exploitation of specific assets/capabilities, but on the exploitation of all of the assets and capabilities combinations in the dynamic capabilities framework. Thus, the model is refined through sequential statistical regression analyses of survey results with a sample size of 120 valid responses. The results of this study provide empirical evidence in support of the notion that a competitive advantage is achieved via the implementation of a dynamic capability framework as an important way for a construction enterprise to improve its organisational performance. The characteristics of asset-capability combinations were found to be significant determinants of the competitive advantage of the Indonesian construction enterprises, and that such advantage sequentially contributes to organisational performance. If a dynamic capabilities framework can work in the context of Indonesia, it suggests that the framework has potential applicability in other emerging and developing countries. This study also demonstrates the importance of the multi-stage nature of the model which provides a rich understanding of the dynamic process by which asset-capability should be exploited in combination by the construction firms operating in varying levels of hostility. Such findings are believed to be useful to both academics and practitioners, however, as this research represents a dynamic capabilities framework at the enterprise level, future studies should continue to explore and examine the framework in other levels of strategic management in construction as well as in other countries where different cultures or similar conditions prevails.

A free weekly iron-folic acid supplementation and regular deworming program is associated with improved hemoglobin and iron status indicators in Vietnamese women

Background Anemia due to iron deficiency is recognized as one of the major nutritional deficiencies in women and children in developing countries. Daily iron supplementation for pregnant women is recommended in many countries although there are few reports of these programs working efficiently or effectively. Weekly iron-folic acid supplementation (WIFS) and regular deworming treatment is recommended for non-pregnant women living in areas with high rates of anemia. Following a baseline survey to assess the prevalence of anemia, iron deficiency and soil transmitted helminth infections, we implemented a program to make WIFS and regular deworming treatment freely and universally available for all women of reproductive age in two districts of a province in northern Vietnam over a 12 month period. The impact of the program at the population level was assessed in terms of: i) change in mean hemoglobin and iron status indicators, and ii) change in the prevalence of anemia, iron deficiency and hookworm infections. Method Distribution of WIFS and deworming were integrated with routine health services and made available to 52,000 women. Demographic data and blood and stool samples were collected in baseline, and three and 12-month post-implementation surveys using a population-based, stratified multi-stage cluster sampling design. Results The mean Hb increased by 9.6 g/L (95% CI, 5.7, 13.5, p < 0.001) during the study period. Anemia (Hb<120 g/L) was present in 131/349 (37.5%, 95% CI 31.3, 44.8) subjects at baseline, and in 70/363 (19.3%, 95% CI 14.0, 24.6) after twelve months. Iron deficiency reduced from 75/329 (22.8%, 95% CI 16.9, 28.6) to 33/353 (9.3%, 95% CI 5.7, 13.0) by the 12-mnth survey, and hookworm infection from 279/366 (76.2%,, 95% CI 68.6, 83.8) to 66/287 (23.0%, 95% CI 17.5, 28.5) over the same period. Conclusion A free, universal WIFS program with regular deworming was associated with reduced prevalence and severity of anemia, iron deficiency and ho