Expert System-Based Predictive Cost Model for Building Works: Neural Network Approach

Project managers need accurate estimate of building projects to be able to choose appropriate alternatives for their constructions. Estimated costs of building projects, which hitherto have been based on regression models, are usually left with gaps for high margin of errors and as well, they lack the capacity to accommodate certain intervening variables as construction works progress. Data of past construction projects of the past 2 years were adjusted and used for the study. This model is developed and tested as a predictive cost model for building projects based on Multilayer Perceptron Artificial Neural Networks (ANNs) with Levenberg Marqua. This model is capable of helping professionals save time, make more realistic decisions, and help avoid underestimating and overestimating of project costs. The model is a step ahead of Regression models

Assessment of Parents’ Satisfaction with Paediatric Surgery Services at a Tertiary Hospital in South West Nigeria: A Quality Control Check

Background: Patient satisfaction is an important link in the chain of patient-physician interaction, patient care experience and patient health outcome. Patient satisfaction is relevant in the evaluation of quality of services received in health institutions based in low and middle income countries, and can provide important feedback for service improvement in such resource-poor settings. Aim: This study aimed to examine the patient’s level of satisfaction with pediatric surgery services in a Teaching Hospital. Subjects and Methods: Setting: Paediatric Surgery Unit of the Lagos University Teaching Hospital. Prospective questionnaire based survey. Consenting literate parents of paediatric post-op patients were serially recruited from the pediatric surgery unit of the Lagos University Teaching Hospital. The consent of the Institution’s Research Ethics’ Committee was sought and obtained. Using a general sociodemographic questionnaire and the patient satisfaction with services scale, patient experiences were obtained. Results were expressed as simple percentages and presented in tables. Results: One hundred and thirty-four post-op cases participated in this study. These participants were parents of children with varied surgical conditions such as: hernia (24.6%, 33/134), hydrocoele (8.2%, 11/134), among other conditions. Majority of the cases were follow-up cases (75.4%, 101/134), compared to 24.6% being new cases. Most respondents (parents/guardians) rated the ‘assistance from the records officer’ as good/ very good/excellent (82.1%, 110/134), while 14.9% (20/134) rated it as fair/poor. Respondents were quite satisfied with the ‘amount of information given about the health problem’ with 82.9% (111/134) rating it as good/very good/excellent and 8.2% (11/134) as fair/poor. The ‘suitability of the treatment plan to needs was considered good/very good/excellent by 61.9% and fair/poor by 9.0%. However, the ‘overall quality of care’ was rated as fair/poor in 12.0%, and good/very good/excellent by 88.0% of respondents. Conclusion: In conclusion, the study serves as a useful feedback tool which provides important information on certain aspects of patient satisfaction, it identifies aspects which respondents find less satisfying and as such need improvement

Econometric Entropy - Neural Network-Based Model for Project Cost Adjudication System in Residential Building Project Procurement

The main aim of this research work is to develop a Neural network –Econometric –Entropy-Based Project Adjudication Model for Residential Building Project Procurement. An econometric model which incorporates exigency escalator and inflation buffer was generated in this study, this is accompanied with risk entropy matrix that could aid determination of the extent of risk implication on the project elements at tendering and construction stages of building projects. The model incorporates residential building elemental dichotomies within the context of early and late constructible elements with speculated prediction period, taken into consideration the present value of cost. This attributes would enable a builder or contactor load cost implication of an unseen circumstance even on occasion of deferred cost reimbursemen

Neural Network-Ant Colony Optimization Model of Residential Building Project Cost: Exploratory Approach

Neural network and ant-colony are two important tools that could be used to provide solution in situation of multivariate environment that requires pareto optima solutions. In this study therefore, combination of neural network and ant colony method was used to generate an optimization cost model. Neural network is a conventional method currently being used in cost modeling, given its advantage over traditional regression method. It is based on this, that this study used the combination of neural network and regression method to model cost of residential building projects. One hundred and fifty (150) samples of residential building projects were selected at random and divided into two; one part is used in developing network algorithm for neural network and ant colony, while the second part is used for model validation. Neural network is used to generate which was divided into modules: the data optimization module, criteria selection with initializing and terminating modules. Regression analysis was carried out and model validated with Jackknife re-sampling technique and previously developed ant colony model (MOACO, MOTACO and MAWA). The co-linearity analysis indicates high level of tolerance and -0.0756 lowest variation prediction quotients to 0.8678 highest variation quotients. Also the Regression coefficient (R-square) value for determining the model fitness is 0.069 with standard error of 0.045. These results attests to the fitness of the model generated. The model is flexible in accommodating new data and variables, thus, it allows for continuous updating. Keywords: Expert system, Co-linearity, Informatics, Residential-Building

Modelling Sustainable Construction Workplace Management Practice and Job Satisfaction in Construction Firms in Lagos State

Importance of having a sustainable workforce management practice in construction firms cannot be overemphasized, especially when primary objective of an organization is high productivity. The study presents therefore, sustainable human resources management practice that could influence job satisfaction among construction workforce. One hundred and fifty (150) questionnaires were used for the study. Random sampling technique was used in sample selection, SPSS software was utilized in data processing and analysis while results are presented in tables and charts. The study generated a regression model that could be used to recommend sustainable work force practice. The following practices were recommended as a key to good workforce management: priority in training and retraining of workforce, good remuneration system, good occupational health and safety provision among others. It is believed that a good construction workforce management practice would induce high worker's productivit

Neural Network and Econometric-Based Utility Parameter Model for Cost Management of Building Projects

The aim of the study is to develop a project cost centre utility parameter-based econometric model that incorporates econometric parameters using neural network. Construction cost of residential building projects was used in this study. Random sampling technique was used to select projects completed between 2009 and 2011 , and were examined for their cost centres validity. Final construction cost (As-built cost) of selected four hundred (400) projects were further modified with econometric factors like inflation index, cost entropy and entropy factor and were used to form and train neural network Back propagation neural network algorithm used. Probability technique was used to generate risk impact matrix and influence of entropy on the cost centres. In this study a parametric model similar hedonic models was generated using the utility parameters within the early and late elemental dichotomy. The developed model was validated through comparative analysis ofthe econometric loading attributes of the variables involved, using Monte Carlo technique of SPSS software by extracting the resultant contingency coefficient. This attribute would help client, project team and contractor manage cost of construction, also, it would enable a builder or contactor load cost implication of an unseen circumstance even on occasion of deferred cost reimbursement and hel

NuRD suppresses pluripotency gene expression to promote transcriptional heterogeneity and lineage commitment

Transcriptional heterogeneity within embryonic stem cell (ESC) populations has been suggested as a mechanism by which a seemingly homogeneous cell population can initiate differentiation into an array of different cell types. Chromatin remodeling proteins have been shown to control transcriptional variability in yeast and to be important for mammalian ESC lineage commitment. Here we show that the Nucleosome Remodeling and Deacetylation (NuRD) complex, which is required for ESC lineage commitment, modulates both transcriptional heterogeneity and the dynamic range of a set of pluripotency genes in ESCs. In self-renewing conditions, the influence of NuRD at these genes is balanced by the opposing action of self-renewal factors. Upon loss of self-renewal factors, the action of NuRD is sufficient to silence transcription of these pluripotency genes, allowing cells to exit self-renewal. We propose that modulation of transcription levels by NuRD is key to maintaining the differentiation responsiveness of pluripotent cells

Development of Bioinformatics Infrastructure for Genomics Research in H3Africa

Background: Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet’s role has evolved in response to changing needs from the consortium and the African bioinformatics community. Objectives: H3ABioNet set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage, and analysis. Methods and Results: Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrollment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Because H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System, and eBiokits. A set of reproducible, portable, and cloud-scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training. Conclusions: For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities, and training programs. Here, we describe the infrastructure and how it has affected genomics and bioinformatics research in Africa

Development of Bioinformatics Infrastructure for Genomics Research:

Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community

Prevalence of Anxiety and Depression among Outpatients with Type 2 Diabetes in the Mexican Population

Depression and anxiety are common in diabetic patients; however, in recent years the frequency of these symptoms has markedly increased worldwide. Therefore, it is necessary to establish the frequency and factors associated with depression and anxiety, since they can be responsible for premature morbidity, mortality, risk of developing comorbidities, complications, suffering of patients, as well as escalation of costs. We studied the frequency of depression and anxiety in Mexican outpatients with type 2 diabetes and identified the risk factors for depression and anxiety.We performed a study in 820 patients with type 2 diabetes. The prevalence of depression and anxiety was estimated using the Hamilton Depression Rating Scale and the Hamilton Anxiety Rating Scale, respectively. We calculated the proportions for depression and anxiety and, after adjusting for confounding variables, we performed multivariate analysis using multiple logistic regressions to evaluate the combined effect of the various factors associated with anxiety and depression among persons with type 2 diabetes. The rates for depression and anxiety were 48.27% (95% CI: 44.48–52.06) and 55.10% (95% CI: 51.44–58.93), respectively. Occupation and complications in diabetes were the factors associated with anxiety, whereas glucose level and complications in diabetes were associated with depression. Complications in diabetes was a factor common to depression and anxiety (p<0.0001; OR 1.79, 95% CI 1.29–2.4).Our findings demonstrate that a large proportion of diabetic patients present depression and/or anxiety. We also identified a significant association between complications in diabetes with depression and anxiety. Interventions are necessary to hinder the appearance of complications in diabetes and in consequence prevent depression and anxiety