Probability Density Function for Predicting Productivity in Masonry Construction Based on the Compatibility of a Crew

During the different phases of a masonry project, contractors collect detailed information about the labor productivity of its workers and the factors that influence productivity. Information includes quantitative data such as hours, activities, and tasks, and qualitative data such as ratings and personality factors. Personality factors have been found to be a key aspect that influences the compatibility of a crew and the productivity in masonry construction. This paper proposes a mathematical framework to determine how the compatibility between the workers in a crew can be used to predict productivity. A standard method for quantifying personality is used to determine the compatibility of a crew and empirically define a probability density to predict productivity. The probability density determines, for a given compatibility, the average productivity for a crew. The most interesting part of this probability density is that it accounts for variations in the productivity, resulting from the interaction and the relationships between the workers in a crew. The proposed probability distribution can be used to make more realistic predictions, by calculating confidence intervals, of the productivity of masonry crews and to better estimate times of construction, avoid crew conflicts, and find practical ways to increase production

Compatibility of Personality and Productivity: An Analysis of the Relationship with Construction Crews

The labor productivity of a crew depends on how efficiently workers are used in the construction process. Skills, capabilities, resources, and even personality affect the efficiency of the workers and may have an impact on the productivity of their crew. This paper illustrates how the personality profiles of the workers in a crew can be used to determine the relationship between compatibility of personality and productivity. Masons working in eight live construction projects completed the big five of personality to indicate their personality traits. Based on the personality traits, the compatibility of the crews was calculated. Productivity at the task-level was measured to determine the performance of the crews. Various statistical analyses are performed to establish the relationship between compatibility and crew productivity and the true value of the coefficient (and its likeliness). The results indicate that there is a high positive correlation between compatibility of personality and productivity at the task-level (rs = 0.758). Results also indicate that in the worst case scenario, there is a moderate correlation between compatibility and productivity (rs > 0.3; probability: 0.728). The implications of the relationship for managing crews in construction projects is discussed

Does Compatibility of Personality Affect Productivity? Exploratory Study with Construction Crews

Crew productivity is a function of how efficiently labor is utilized in the construction process. However, previous research in construction has not comprehensively investigated the relationship between personality and crew productivity. This paper uses personality profiles to investigate a new fundamental concept, the relationship between compatibility of personality and crew productivity at the task level. Twenty-eight masons completed a revised questionnaire of the Big Five to indicate their personality. Personality scores were used to calculate compatibility in each of the 20 participating two-mason crews working on eight projects. Regression analysis was performed to establish the relationship between compatibility and crew productivity. Results show that that there is a high positive correlation between compatibility and crew productivity. Compatibility accounts for more than half of the predictable variance in productivity. This paper makes four major contributions: it proposes a new metric to measure compatibility of personality among workers in a crew; it reveals how personality factors affect productivity; it provides rigorous methods to analyze correlations (using confidence intervals and Bayesian inference) for construction experiments; and it provides theoretical contributions to advancing the theory of personality and productivity in construction projects

Predicting construction productivity with machine learning approaches

Machine learning (ML) is a purpose technology already starting to transform the global economy and has the potential to transform the construction industry with the use of data-driven solutions to improve the way projects are delivered. Unrealistic productivity predictions cause increased delivery cost and time. This study shows the application of supervised ML algorithms on a database including 1,977 productivity measures that were used to train, test, and validate the approach. Deep neural network (DNN), k-nearest neighbours (KNN), support vector machine (SVM), logistic regression, and Bayesian networks are used for predicting productivity by using a subjective measure (compatibility of personality), together with external and site conditions and other workforce characteristics. A case study of a masonry project is discussed to analyse and predict task productivity

Common variants of the TCF7L2 gene are associated with increased risk of type 2 diabetes mellitus in a UK-resident South Asian population

Background Recent studies have implicated variants of the transcription factor 7-like 2 (TCF7L2) gene in genetic susceptibility to type 2 diabetes mellitus in several different populations. The aim of this study was to determine whether variants of this gene are also risk factors for type 2 diabetes development in a UK-resident South Asian cohort of Punjabi ancestry. Methods We genotyped four single nucleotide polymorphisms (SNPs) of TCF7L2 (rs7901695, rs7903146, rs11196205 and rs12255372) in 831 subjects with diabetes and 437 control subjects. Results The minor allele of each variant was significantly associated with type 2 diabetes; the greatest risk of developing the disease was conferred by rs7903146, with an allelic odds ratio (OR) of 1.31 (95% CI: 1.11 – 1.56, p = 1.96 × 10-3). For each variant, disease risk associated with homozygosity for the minor allele was greater than that for heterozygotes, with the exception of rs12255372. To determine the effect on the observed associations of including young control subjects in our data set, we reanalysed the data using subsets of the control group defined by different minimum age thresholds. Increasing the minimum age of our control subjects resulted in a corresponding increase in OR for all variants of the gene (p ≤ 1.04 × 10-7). Conclusion Our results support recent findings that TCF7L2 is an important genetic risk factor for the development of type 2 diabetes in multiple ethnic groups

The effect of minor allele frequency on the likelihood of obtaining false positives

Determining the most promising single-nucleotide polymorphisms (SNPs) presents a challenge in genome-wide association studies, when hundreds of thousands of association tests are conducted. The power to detect genetic effects is dependent on minor allele frequency (MAF), and genome-wide association studies SNP arrays include SNPs with a wide distribution of MAFs. Therefore, it is critical to understand MAF's effect on the false positive rate

Genetic Modulation of Lipid Profiles following Lifestyle Modification or Metformin Treatment: the Diabetes Prevention Program

Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS) based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR) lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P=0.04–1×10$^{−17}$). Except for total HDL particles (r=−0.03, P=0.26), all components of the lipid profile correlated with the GRS (partial |r|=0.07–0.17, P=5×10$^{−5}$–1×10$^{−19}$). The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (β=+0.87, SEE±0.22 mg/dl/allele, P=8×10−5, P$_{interaction}$=0.02) in the lifestyle intervention group, but not in the placebo (β=+0.20, SEE±0.22 mg/dl/allele, P=0.35) or metformin (β=−0.03, SEE±0.22 mg/dl/allele, P=0.90; P$_{interaction}$=0.64) groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (β=+0.30, SEE±0.012 ln nmol/L/allele, P=0.01, P$_{interaction}$=0.01) but not in the placebo (β=−0.002, SEE±0.008 ln nmol/L/allele, P=0.74) or metformin (β=+0.013, SEE±0.008 nmol/L/allele, P=0.12; P$_{interaction}$ = 0.24) groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss

Evaluation of four novel genetic variants affecting hemoglobin A1c levels in a population-based type 2 diabetes cohort (the HUNT2 study)

<p>Abstract</p> <p>Background</p> <p>Chronic hyperglycemia confers increased risk for long-term diabetes-associated complications and repeated hemoglobin A1c (HbA1c) measures are a widely used marker for glycemic control in diabetes treatment and follow-up. A recent genome-wide association study revealed four genetic loci, which were associated with HbA1c levels in adults with type 1 diabetes. We aimed to evaluate the effect of these loci on glycemic control in type 2 diabetes.</p> <p>Methods</p> <p>We genotyped 1,486 subjects with type 2 diabetes from a Norwegian population-based cohort (HUNT2) for single-nucleotide polymorphisms (SNPs) located near the <it>BNC2</it>, <it>SORCS1</it>, <it>GSC </it>and <it>WDR72 </it>loci. Through regression models, we examined their effects on HbA1c and non-fasting glucose levels individually and in a combined genetic score model.</p> <p>Results</p> <p>No significant associations with HbA1c or glucose levels were found for the <it>SORCS1</it>, <it>BNC2</it>, <it>GSC </it>or <it>WDR72 </it>variants (all <it>P</it>-values > 0.05). Although the observed effects were non-significant and of much smaller magnitude than previously reported in type 1 diabetes, the <it>SORCS1 </it>risk variant showed a direction consistent with increased HbA1c and glucose levels, with an observed effect of 0.11% (<it>P </it>= 0.13) and 0.13 mmol/l (<it>P </it>= 0.43) increase per risk allele for HbA1c and glucose, respectively. In contrast, the <it>WDR72 </it>risk variant showed a borderline association with reduced HbA1c levels (<it>β </it>= -0.21, <it>P </it>= 0.06), and direction consistent with decreased glucose levels (<it>β </it>= -0.29, <it>P </it>= 0.29). The allele count model gave no evidence for a relationship between increasing number of risk alleles and increasing HbA1c levels (<it>β </it>= 0.04, <it>P </it>= 0.38).</p> <p>Conclusions</p> <p>The four recently reported SNPs affecting glycemic control in type 1 diabetes had no apparent effect on HbA1c in type 2 diabetes individually or by using a combined genetic score model. However, for the <it>SORCS1 </it>SNP, our findings do not rule out a possible relationship with HbA1c levels. Hence, further studies in other populations are needed to elucidate whether these novel sequence variants, especially rs1358030 near the <it>SORCS1 </it>locus, affect glycemic control in type 2 diabetes.</p

A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies

ContextBoth type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight.ObjectiveWe examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.MethodsWe constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D.ResultsThe T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes.ConclusionIn large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping