Major quantitative techniques for risk analysis in the construction industry : a comparative analysis

Risk analysis is one of the critical functions of the risk management process. It relies on a detailed understanding of risks and their possible implications. Construction projects, because of their large and complex nature, are plagued by a variety of risks which must be considered and responded to in order to ensure project success. This study conducts an extensive comparative analysis of major quantitative risk analysis techniques in the construction industry. The techniques discussed and comparatively analyzed in this report include: Programme Evaluation and Review Technique (PERT), Judgmental Risk Analysis Process (JRAP), Estimating Using Risk Analysis (ERA), Monte Carlo Simulation technique, Computer Aided Simulation for Project Appraisal and Review (CASPAR), Failure Modes and Effects Analysis technique (FMEA) and Advanced Programmatic Risk Analysis and Management model (APRAM). The findings highlight the fact that each risk analysis technique addresses risks in any or all of the following areas – schedule risks, budget risks or technical risks. Through comparative analysis, it has been revealed that a majority of risk analysis techniques focus on schedule or budget risks. Very little has been documented in terms of technical risk analysis techniques. In an era where clients are demanding and expecting higher quality projects and finishes, project managers must endeavor to invest time and resources to ensure that the few existing technical risk analysis techniques are developed and further refined, and that new technical risk analysis techniques are developed to suit the current construction industries requirements.

Dynamics of childhood growth and obesity: development and validation of a quantitative mathematical model

Background
Clinicians and policy makers need the ability to predict quantitatively how childhood bodyweight will respond to obesity interventions.

Methods
We developed and validated a mathematical model of childhood energy balance that accounts for healthy growth and development of obesity, and that makes quantitative predictions about weight-management interventions. The model was calibrated to reference body composition data in healthy children and validated by comparing model predictions with data other than those used to build the model.

Findings
The model accurately simulated the changes in body composition and energy expenditure reported in reference data during healthy growth, and predicted increases in energy intake from ages 5—18 years of roughly 1200 kcal per day in boys and 900 kcal per day in girls. Development of childhood obesity necessitated a substantially greater excess energy intake than for development of adult obesity. Furthermore, excess energy intake in overweight and obese children calculated by the model greatly exceeded the typical energy balance calculated on the basis of growth charts. At the population level, the excess weight of US children in 2003—06 was associated with a mean increase in energy intake of roughly 200 kcal per day per child compared with similar children in 1971—74. The model also suggests that therapeutic windows when children can outgrow obesity without losing weight might exist, especially during periods of high growth potential in boys who are not severely obese.

Interpretation
This model quantifies the energy excess underlying obesity and calculates the necessary intervention magnitude to achieve bodyweight change in children. Policy makers and clinicians now have a quantitative technique for understanding the childhood obesity epidemic and planning interventions to control it.

Funding
Intramural Research Program of the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.

Integrating simulated annealing and delta technique for constructing optimal prediction intervals

This paper aims at developing a new criterion for quantitative assessment of prediction intervals. The proposed criterion is developed based on both key measures related to quality of prediction intervals: length and coverage probability. This criterion is applied as a cost function for optimizing prediction intervals constructed using delta technique for neural network model. Optimization seeks out to minimize length of prediction intervals without compromising their coverage probability. Simulated Annealing method is employed for readjusting neural network parameters for minimization of the new cost function. To further ameliorate search efficiency of the optimization method, parameters of the network trained using weight decay method are considered as the initial set in Simulated Annealing algorithm. Implementation of the proposed method for a real world case study shows length and coverage probability of constructed prediction intervals are better than those constructed using traditional techniques.

Relationship between indices of adiposity obtained by peripheral quantitative computed tomography and dual-energy X-ray absorptiometry in pre-pubertal children

Background/Aim: The study investigated the relationship between indices of adiposity measured by peripheral quantitative computed tomography (pQCT) and dual-energy X-ray absorptiometry (DXA) in pre-pubertal children.

Subjects and methods: DXA-derived per cent body fat (%BF) was measured in 284 boys and 288 girls, aged 7–10 years. Cross-sections of the forearm (n=427) and lower leg (n=560) were obtained by pQCT to measure total cross-sectional area of the limb (Total CSA), Muscle CSA, Fat CSA, %Fat CSA (Fat CSA/Total CSA×100) and muscle density.

Results: Peripheral QCT-derived %Fat CSA in the forearm and lower leg correlated strongly with DXA-derived %BF (r=0.83–0.89, p<0.01) in both boys and girls. However, forearm and lower leg %Fat CSA were higher than whole body %BF by 5% and 10%, respectively. A better prediction of whole-body %BF was achieved by including %Fat CSA, muscle density and height into a hierarchical regression model. Using sex-specific regression equations, 87.7% of the boys and 83.7% of the girls had a predicted %BF within 3% units of the %BF obtained by DXA.

Conclusion: In pre-pubertal children, pQCT measures of adiposity are strongly associated with whole-body per cent body fat. This reproducible method could be an alternative technique to estimate body composition in this population.

Quantitative determination of fatty acid compositions in micro-encapsulated fish-oil supplements using Fourier transform infrared (FTIR) spectroscopy

The research describes a rapid method for the determination of fatty acid (FA) contents in a micro-encapsulated fish-oil (μEFO) supplement by using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic technique and partial least square regression (PLSR) analysis. Using the ATR-FTIR technique, the μEFO powder samples can be directly analysed without any pre-treatment required, and our developed PLSR strategic approach based on the acquired spectral data led to production of a good linear calibration with R^₂ = 0.99. In addition, the subsequent predictions acquired from an independent validation set for the target FA compositions (i.e., total oil, total omega-3 fatty acids, EPA and DHA) were highly accurate when compared to the actual values obtained from standard GC-based technique, with plots between predicted versus actual values resulting in excellent linear fitting (R² ⩾ 0.96) in all cases. The study therefore demonstrated not only the substantial advantage of the ATR-FTIR technique in terms of rapidness and cost effectiveness, but also its potential application as a rapid, potentially automated, online monitoring technique for the routine analysis of FA composition in industrial processes when used together with the multivariate.

Integrated RNA extraction and RT-PCR for semi-quantitative gene expression studies on a microfluidic device

This paper describes the development of a microfluidic methodology, using RNA extraction and reverse transcription PCR, for investigating expression levels of cytochrome P450 genes. Cytochrome P450 enzymes are involved in the metabolism of xenobiotics, including many commonly prescribed drugs, therefore information on their expression is useful in both pharmaceutical and clinical settings. RNA extraction, from rat liver tissue or primary rat hepatocytes, was performed using a silica-based solid-phase extraction technique. Following elution of the purified RNA, amplification of target sequences for the housekeeping gene, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and the cytochrome P450 gene CYP1A2, was carried out using a one-step reverse transcription PCR. Once the microfluidic methodology had been optimized, analysis of control and 3-methylcholanthrene-induced primary rat hepatocytes were used to evaluate the system. As expected, GAPDH was consistently expressed, whereas CYP1A2 levels were found to be raised in the drug-treated samples. The proposed system offers an initial platform for development of both rapid throughput analyzers for pharmaceutical drug screening and point-of-care diagnostic tests to aid provision of drug regimens, which can be tailor-made to the individual patient.

New opportunities for quantitative and time efficient 3D MRI of liquid and solid electrochemical cell components: Sectoral Fast Spin Echo and SPRITE.

The ability to image electrochemical processes in situ using nuclear magnetic resonance imaging (MRI) offers exciting possibilities for understanding and optimizing materials in batteries, fuel cells and supercapacitors. In these applications, however, the quality of the MRI measurement is inherently limited by the presence of conductive elements in the cell or device. To overcome related difficulties, optimal methodologies have to be employed. We show that time-efficient three dimensional (3D) imaging of liquid and solid lithium battery components can be performed by Sectoral Fast Spin Echo and Single Point Imaging with T1 Enhancement (SPRITE), respectively. The former method is based on the generalized phase encoding concept employed in clinical MRI, which we have adapted and optimized for materials science and electrochemistry applications. Hard radio frequency pulses, short echo spacing and centrically ordered sectoral phase encoding ensure accurate and time-efficient full volume imaging. Mapping of density, diffusivity and relaxation time constants in metal-containing liquid electrolytes is demonstrated. 1, 2 and 3D SPRITE approaches show strong potential for rapid high resolution (7)Li MRI of lithium electrode components.

Quantitative secondary electron imaging for work function extraction at atomic level and layer identification of graphene

Two-dimensional (2D) materials usually have a layer-dependent work function, which require fast and accurate detection for the evaluation of their device performance. A detection technique with high throughput and high spatial resolution has not yet been explored. Using a scanning electron microscope, we have developed and implemented a quantitative analytical technique which allows effective extraction of the work function of graphene. This technique uses the secondary electron contrast and has nanometre-resolved layer information. The measurement of few-layer graphene flakes shows the variation of work function between graphene layers with a precision of less than 10 meV. It is expected that this technique will prove extremely useful for researchers in a broad range of fields due to its revolutionary throughput and accuracy.