Lean Principles Implementation in Construction Management: A One Team Approach

"Lean in construction is a relatively new approach in delivering construction projects. The Lean approach has been introduced to tackle the phenomenal construction projects delays and budget overruns. This paper shall be discussing how the implementation of the Lean approach in construction management processes can improve construction projects’ delivery. The paper argues that the Lean approach in the construction industry context revolves around people rather than processes as in the manufacturing industry context. Therefore, the cornerstone of the Lean approach in construction is creating a One Team out of the multiple typical rivals in the construction industry: Owner, architect / engineer, operator and contractor with all taking into consideration the end user. While the ‘Lean’ concept is manufacturing tackled processes to achieve the ‘Lean’ goal; in construction the ‘Lean’ approach is tackling the interaction between people and entities. The interaction between people is addressed by the ‘Lean construction’ planning tool: The last planner system; while the interaction between entities is addressed by contractual forms of agreement with the most influential being: Alliance contracting. The paper uses a major infrastructure project ‘Sydney Desalination Plant’ which started in June 2007 and completed in January 2010 as a successful example of Lean approach implementation, although the term ‘Lean’ was never used then by the parties involved in the project. The paper draws on the first-hand experience of the author in the construction industry including the participation in the delivery of the ‘Sydney Desalination Plant’ used as example of a Lean project.

Confusion Matrix in Three-class Classification Problems: A Step-by-Step Tutorial

The confusion matrix is a specific table used in machine learning to describe and assess the performance of a classification model (e.g., an artificial neural network) for a set of test data whose actual distinguishing features are known. The confusion matrix for an n-class classification problem is square, with n rows and n columns. The rows represent the class actual samples (instances), which are the classifier inputs, and the columns represent the class predicted samples, which are the classifier outputs. Binary class classifiers have been presented in a previous paper, where in this paper, we are concerned with three-class classification performance measures. We also clarify the concept with numerical examples to make it close to the reader mind

Confusion Matrix in Binary Classification Problems: A Step-by-Step Tutorial

In the field of machine learning, the confusion matrix is a specific table adopted to describe and assess the performance of a classification model (e.g. an artificial neural network) for a set of test data whose actual distinguishing features are known. The learning algorithm is thus of the supervised learning category. For an n-class classification problem, the confusion matrix is square with n rows and n columns. The rows represent the class actual samples (instances) which are the inputs to the classifier, while the columns represent the class predicted samples, the classifier outputs. (The converse is also valid, i.e. the two dimensions \u27actual\u27 and \u27predicted\u27 can be assigned to columns and rows, respectively). Binary as well as multiple-class classifiers can be dealt with. It is worth noting that the term \u27matrix\u27 here has nothing to do with the theorems of matrix algebra; it is regarded just as an information-conveying table. The descriptive word ‘confusion’ stems from the fact that the matrix clarifies to what extent the model confuses the classes — mislabels one as another. The essential concept was introduced in 1904 by the British statistician Karl Pearson (1857 — 1936)

The Effect of Dynamic Assessment on Adult Learners of Arabic: A Mixed-Method Study at the Defense Language Institute Foreign Language Center

Dynamic assessment (DA) is based on Vygotsky\u27s (1978) sociocultural theory and his Zone of Proximal Development (ZPD). ZPD is the range of abilities bordered by the learner\u27s assisted and independent performances. Previous studies showed promising results for DA in tutoring settings. However, they did not use proficiency-based rubrics to measure students\u27 progress and did not mention the method of using DA practically in classrooms. The literature showed that task-based language instruction (TBLI) is effective in adult classrooms. This study combined DA with TBLI to answer four questions. What is the change in the structural control of Arabic speaking based on DA/TBLI instruction? How do Oral Proficiency Interview (OPI) without DA assistance and OPI with DA assistance compare relative to the evaluation of Arabic speaking? How do the experiences and perceptions of DA/TBLI instruction compare between teacher-researcher and OPI testers? What are the student perceptions of the DA process? The study was conducted in three phases to answer its questions: pre-DA, DA, and post-DA. In the pre-DA phase, 12 volunteers from the Defense Language Institute Foreign Language Center went through unofficial Oral Proficiency Interviews (OPI), intellectual style survey, biographical background questionnaire, and interventionist-DA interviews. During the DA phase, the teacher-researcher used DA/TBLI instruction and Interagency Language Roundtable-based (ILR) rubrics to promote learning and to diagnose students\u27 needs daily. These lessons were observed by certified OPI testers. In the post-DA phase, the six selected participants were reevaluated by OPIs and interventionist-DA interviews. Students and observers were interviewed, but only students responded to a survey. The results of comparing the different evaluations conducted in both the pre- and post-DA phase showed that the structural control of Arabic improved for all participants. There is a parallel coefficient of 1.0 between the OPI with and without DA assistance for evaluating the participants\u27 speaking proficiency. DA/TBLI instruction was practical and successful in making a difference for the participants\u27 learning process. It reflected the success of the ILR-based rubrics in diagnosing accurately the students\u27 inabilities whether in the interventionist-DA interviews or in the daily interactionist DA. The OPI without DA assistance cannot provide accurate diagnostic feedback in details

Studies on human ovarian tumours

These studies on ovarian tumours were largely based on the collection of gynaecological pathology material in the Department of Obstetrics and Gynaecology of the University of Edinburgh. All ovarian tumour cases examined during a period of 10 years, 1954-1963 were reviewed. These amounted to a total of 1734 cases. For the purpose of some studies, the review was extended beyond the 10 year period. The studies included in this thesis fall into three parts. In part I, biostatistical and general aspects of the ovarian tumour series are presented. Part II deals with histopathological studies. In part III, the application of cytogenetic techniques to the study of some problems of ovarian tumours is presented and discussed

Analysis of Petri nets by partitioning: Splitting transitions

AbstractIn this paper, a method of analysis of large Petri nets by partitioning is proposed. This method permits a great saving of computation time and storage. Useless efforts spent in the analysis of large Petri nets are spared by a look to the partitions of interest. It is possible to study the characteristics of the required places by involving them in a partition. It was shown that partitioning preserves the characteristics of the main Petri net. The reachability tree method or the matrix equations approach, which were untractable at the whole net level, may be used at the subnet level to get the needed analysis criteria

A Transfer Learning End-to-End ArabicText-To-Speech (TTS) Deep Architecture

Speech synthesis is the artificial production of human speech. A typical text-to-speech system converts a language text into a waveform. There exist many English TTS systems that produce mature, natural, and human-like speech synthesizers. In contrast, other languages, including Arabic, have not been considered until recently. Existing Arabic speech synthesis solutions are slow, of low quality, and the naturalness of synthesized speech is inferior to the English synthesizers. They also lack essential speech key factors such as intonation, stress, and rhythm. Different works were proposed to solve those issues, including the use of concatenative methods such as unit selection or parametric methods. However, they required a lot of laborious work and domain expertise. Another reason for such poor performance of Arabic speech synthesizers is the lack of speech corpora, unlike English that has many publicly available corpora and audiobooks. This work describes how to generate high quality, natural, and human-like Arabic speech using an end-to-end neural deep network architecture. This work uses just $\langle$ text, audio $\rangle$ pairs with a relatively small amount of recorded audio samples with a total of 2.41 hours. It illustrates how to use English character embedding despite using diacritic Arabic characters as input and how to preprocess these audio samples to achieve the best results

Can Chronic Nitric Oxide Inhibition Improve Liver and Renal Dysfunction in Bile Duct Ligated Rats?

The aims of the present work were to study the effects of chronic NO inhibition on liver cirrhosis and to analyze its relationship with liver and kidney damage markers. Two inhibitors of NO synthesis (inducible NO synthase (iNOS) inhibitor, aminoguanidine (AG), and nonselective NOS inhibitor, L-nitroarginine methyl ester (L-NAME)) were administered for 6 weeks to bile duct ligated (BDL) rats 3 days after surgery. The present study showed that BDL was associated with liver injury and renal impairment. BDL increased liver NO content and myeloperoxidase (MPO) activity. This was corroborated by increased oxidative stress, TNF-α, TGF-1β, and MMP-13 genes overexpression. Although both drugs reduced NO synthesis and TNF-α gene overexpression, only AG improved renal dysfunction and liver damage and reduced liver oxidative stress. However, L-NAME exacerbated liver and renal dysfunction. Both drugs failed to modulate TGF-1β and MMP-13 genes overexpression. In conclusion, inhibition of NO production by constitutive nitric oxide synthase (cNOS) plays a crucial role in liver injury and renal dysfunction while inhibition of iNOS by AG has beneficial effect. TNF-α is not the main cytokine responsible for liver injury in BDL model. Nitric oxide inhibition did not stop the progression of cholestatic liver damage

An Energy Efficient Sleep/Wake up Routing Protocol for Wireless Sensor Networks

In recent years, wireless sensor networks (WSNs) have a rapid development and they take a lot of research attention because of their wide-range applications. A WSN consists of a large number of distributed sensor nodes. These nodes are often deployed in remote or hostile areas to monitor physical or environmental conditions where they send this data to a main location. The most critical parameter in WSNs is network lifetime, so an efficient routing protocol is essential to reduce the energy consumption and to increase the network lifetime. This paper proposes an energy-efficient chain-based cooperative routing protocol based on node sleep/wake-up mechanism for WSNs. We compare this protocol with two efficient protocols; LEACH and CBCCP using MATLAB. Simulation results show that the proposed algorithm achieves better performance and conserves more energy than the other two protocols