Assessment of different construction and demolition waste management approaches

AbstractThe waste generated from construction and demolition sites is considered one of the most irritating problems in Egypt. In the last 10years some effort has been made toward solving this problem, the most outstanding is the newly issued Egyptian rating system “Green Pyramids Rating System”. It emphasizes on waste management and particularly “site provision and environment” which contributes to 75% of the management category score. However the traditional practice which is limited to dumping all the generated waste is still dominating. The absence of sustainable practices in construction sector in Egypt led to the lack in financial and environmental data. From strategic perspective, the research aims at developing a detailed procedure to evaluate two construction and demolition waste management approaches by means of Decision Matrix technique. A detailed study is introduced for the two approaches; for each approach a flow chart is developed to demonstrate its lifecycle, as well as the cost break down structure and the different stakeholders’ roles. A penetration discussion of the pros and cons for each approach was developed accordingly and came out with sixteen influencing attributes for both approaches. The previous steps paved the ground to construct a Decision Matrix to decide on one of the approaches from a strategic environmentally oriented perspective. The study relied on the detailed and deep demonstration of the two approaches to justify the assigned weight for attributes and scores for corresponding approach. From a strategic perspective, the decision came out in favor of the more environmentally friendly approach

Noise generation from interacting high speed axisymmetric jet flows Semiannual status report, 1 Jun. 1968 - 31 Dec. 1969

Far field noise generation from interacting coaxial jet flows, and nozzle operational mode

Active Learning for Data Streams under Concept Drift and concept evolution.

Data streams classification is an important problem however, poses many challenges. Since the length of the data is theoretically infinite, it is impractical to store and process all the historical data. Data streams also experience change of its underlying dis-tribution (concept drift), thus the classifier must adapt. Another challenge of data stream classification is the possible emergence and disappearance of classes which is known as (concept evolution) problem. On the top of these challenges, acquiring labels with such large data is expensive. In this paper, we propose a stream-based active learning (AL) strategy (SAL) that handles the aforementioned challenges. SAL aims at querying the labels of samples which results in optimizing the expected future error. It handles concept drift and concept evolution by adapting to the change in the stream. Furthermore, as a part of the error reduction process, SAL handles the sampling bias problem and queries the samples that caused the change i.e., drifted samples or samples coming from new classes. To tackle the lack of prior knowledge about the streaming data, non-parametric Bayesian modelling is adopted namely the two representations of Dirichlet process; Dirichlet mixture models and stick breaking process. Empirical results obtained on real-world benchmarks show the high performance of the proposed SAL method compared to the state-of-the-art methods

Approach for Enneagram personality detection for Twitter text: a case study

Understanding people’s emotions and orientations attracts researchers nowadays. Current personality detection research concentrates on models such as the big five model, the three-factor model. The Enneagram is deeper than these models for providing a comprehensive view. This theory is a unique personality model because it illustrates what drives human behavior. This recognition helps in building smarter recommendation systems and intelligent educational systems. Enneagram personalities are realized through a long questionnaire-based test. People are not concerned about doing a test because it is time-consuming. A proposed case study employs Twitter’s text to detect Enneagram personality because it requires no time or effort. The proposed case study is based on an approach that uses a combination of ontology, lexicon, and statistical technique. This proposed case study uses the biography description text and 40 tweets of a Twitter profile text. The highest probability percentage is peacemaker personality which is 15.58%. This result means that the identified personality is the peacemaker. The outcome is equivalent to the determination of the Enneagram’s specialized people. This result promises more positive outcomes. This is the first automated approach to determine the Enneagram from text

Clonal Composition of Human Adrenocortical Neoplasms

The mechanisms of tumorigenesis of adrenocortical neoplasms are still not understood. Tumor formation may be the result of spontaneous transformation of adrenocortical cells by somatic mutations. Another factor stimulating adrenocortical cell growth and potentially associated with formation of adrenal adenomas and, less frequently, carcinomas is the chronic elevation of proopiomelanocortin-derived peptides in diseases like ACTH-dependent Cushing's syndrome and congenital adrenal hyperplasia. To further investigate the pathogenesis of adrenocortical neoplasms, we studied the clonal composition of such tumors using X-chromosome inactivation analysis of the highly polymorphic region Xcen-Xp11.4 with the hybridization probe M27ß, which maps to a variable number of tandem repeats on the X-chromsome. In addition, polymerase chain reaction amplification of a phosphoglycerokinase gene polymorphism was performed. After DNA extraction from tumorous adrenal tissue and normal leukocytes in parallel, the active X-chromosome of each sample was digested with the methylation-sensitive restriction enzyme HpaII. A second digestion with an appropriate restriction enzyme revealed the polymorphism of the region Xcen-Xp11.4 and the phosphoglycerokinase locus. Whereas in normal polyclonal tissue both the paternal and maternal alleles are detected, a monoclonal tumor shows only one of the parental alleles. A total of 21 female patients with adrenal lesions were analyzed; 17 turned out to be heterozygous for at least one of the loci. Our results were as follows: diffuse (n = 4) and nodular (n = 1) adrenal hyperplasia in patients with ACTH-dependent Cushing's syndrome, polyclonal pattern; adrenocortical adenomas (n = 8), monoclonal (n = 7), as well as polyclonal (n = 1); adrenal carcinomas (n = 3), monoclonal pattern. One metastasis of an adrenocortical carcinoma showed a pattern most likely due to tumor-associated loss of methylation. In the special case of a patient with bilateral ACTH-independent macronodular hyperplasia, diffuse hyperplastic areas and a small nodule showed a polyclonal pattern, whereas a large nodule was monoclonal. We conclude that most adrenal adenomas and carcinomas are monoclonal, whereas diffuse and nodular adrenal hyperplasias are polyclonal. The clonal composition of ACTH-independent massive macronodular hyperplasia seems to be heterogeneous, consisting of polyclonal and monoclonal areas

Human-Centred Design for Intelligent Environments: Preface to the proceedings of the workshop on Human Centred Design for Intelligent Environments.

Preface to the proceedings of the workshop on “Human Centred Design for Intelligent Environments” organised in conjunction with the 2016 BCS British HCI Conference held at Bournemouth University, July 11th-15th 2016

Duplex DNA from Sites of Helicase-Polymerase Uncoupling Links Non-B DNA Structure Formation to Replicative Stress

BACKGROUND: Replication impediments can produce helicase-polymerase uncoupling allowing lagging strand synthesis to continue for as much as 6 kb from the site of the impediment. MATERIALS AND METHODS: We developed a cloning procedure designed to recover fragments from lagging strand near the helicase halt site. RESULTS: A total of 62% of clones from a p53-deficient tumor cell line (PC3) and 33% of the clones from a primary cell line (HPS-19I) were within 5 kb of a G-quadruplex forming sequence. Analyses of a RACK7 gene sequence, that was cloned multiple times from the PC3 line, revealed multiple deletions in region about 1 kb from the cloned region that was present in a non-B conformation. Sequences from the region formed G-quadruplex and i-motif structures under physiological conditions. CONCLUSION: Defects in components of non-B structure suppression systems (e.g. p53 helicase targeting) promote replication-linked damage selectively targeted to sequences prone to G-quadruplex and i-motif formation

Deep Online Hierarchical Dynamic Unsupervised Learning for Pattern Mining from Utility Usage Data

While most non-intrusive load monitoring (NILM) work has focused on supervised algorithms, unsupervised approaches can be more interesting and practical. Specifically, they do not require labelled training data to be acquired from the individual appliances and can be deployed to operate on the measured aggregate data directly. We propose a fully unsupervised novel NILM framework based on Dynamic Bayesian hierarchical mixture model and Deep Belief network (DBN). The deep network learns, in unsupervised fashion, low-level generic appliance-specific features from the raw signals of the house utilities usage, then the hierarchical Bayesian model learns high-level features representing the consumption patterns of the residents captured by the correlations among the low-level features. The temporal ordering of the high-level features is captured by the Dynamic Bayesian Model. Using this architecture, we overcome the computational complexity that would occur if temporal modelling was directly applied to the raw data or even to the constructed features. The computational efficiency is crucial as our application involves massive data from different utilities usage. Moreover, we develop a novel online inference algorithm to cope with this big data. Finally, we propose different evaluation methods to analyse the results which show that our algorithm finds useful patterns