Short interval control for the cost estimate baseline of novel high value manufacturing products – a complexity based approach

Novel high value manufacturing products by default lack the minimum a priori data needed for forecasting cost variance over of time using regression based techniques. Forecasts which attempt to achieve this therefore suffer from significant variance which in turn places significant strain on budgetary assumptions and financial planning. The authors argue that for novel high value manufacturing products short interval control through continuous revision is necessary until the context of the baseline estimate stabilises sufficiently for extending the time intervals for revision. Case study data from the United States Department of Defence Scheduled Annual Summary Reports (1986-2013) is used to exemplify the approach. In this respect it must be remembered that the context of a baseline cost estimate is subject to a large number of assumptions regarding future plausible scenarios, the probability of such scenarios, and various requirements related to such. These assumptions change over time and the degree of their change is indicated by the extent that cost variance follows a forecast propagation curve that has been defined in advance. The presented approach determines the stability of this context by calculating the effort required to identify a propagation pattern for cost variance using the principles of Kolmogorov complexity. Only when that effort remains stable over a sufficient period of time can the revision periods for the cost estimate baseline be changed from continuous to discrete time intervals. The practical implication of the presented approach for novel high value manufacturing products is that attention is shifted from the bottom up or parametric estimation activity to the continuous management of the context for that cost estimate itself. This in turn enables a faster and more sustainable stabilisation of the estimating context which then creates the conditions for reducing cost estimate uncertainty in an actionable and timely manner

An approach for selecting cost estimation techniques for innovative high value manufacturing products

This paper presents an approach for determining the most appropriate technique for cost estimation of innovative high value manufacturing products depending on the amount of prior data available. Case study data from the United States Scheduled Annual Summary Reports for the Joint Strike Fighter (1997-2010) is used to exemplify how, depending on the attributes of a priori data certain techniques for cost estimation are more suitable than others. The data attribute focused on is the computational complexity involved in identifying whether or not there are patterns suited for propagation. Computational complexity is calculated based upon established mathematical principles for pattern recognition which argue that at least 42 data sets are required for the application of standard regression analysis techniques. The paper proposes that below this threshold a generic dependency model and starting conditions should be used and iteratively adapted to the context. In the special case of having less than four datasets available it is suggested that no contemporary cost estimating techniques other than analogy or expert opinion are currently applicable and alternate techniques must be explored if more quantitative results are desired. By applying the mathematical principles of complexity groups the paper argues that when less than four consecutive datasets are available the principles of topological data analysis should be applied. The preconditions being that the cost variance of at least three cost variance types for one to three time discrete continuous intervals is available so that it can be quantified based upon its geometrical attributes, visualised as an n-dimensional point cloud and then evaluated based upon the symmetrical properties of the evolving shape. Further work is suggested to validate the provided decision-trees in cost estimation practice

A framework for geometric quantification and forecasting of cost uncertainty for aerospace innovations

Quantification and forecasting of cost uncertainty for aerospace innovations is challenged by conditions of small data which arises out of having few measurement points, little prior experience, unknown history, low data quality, and conditions of deep uncertainty. Literature research suggests that no frameworks exist which specifically address cost estimation under such conditions. In order to provide contemporary cost estimating techniques with an innovative perspective for addressing such challenges a framework based on the principles of spatial geometry is described. The framework consists of a method for visualising cost uncertainty and a dependency model for quantifying and forecasting cost uncertainty. Cost uncertainty is declared to represent manifested and unintended future cost variance with a probability of 100% and an unknown quantity and innovative starting conditions considered to exist when no verified and accurate cost model is available. The shape of data is used as an organising principle and the attribute of geometrical symmetry of cost variance point clouds used for the quantification of cost uncertainty. The results of the investigation suggest that the uncertainty of a cost estimate at any future point in time may be determined by the geometric symmetry of the cost variance data in its point cloud form at the time of estimation. Recommendations for future research include using the framework to determine the “most likely values” of estimates in Monte Carlo simulations and generalising the dependency model introduced. Future work is also recommended to reduce the framework limitations noted

Prevalence of borderline acetabular dysplasia in symptomatic and asymptomatic populations: A systematic review and meta-analysis

Background: Patients with borderline acetabular dysplasia are a controversial patient population in hip preservation, as some have primarily impingement-based symptoms and others have instability-based symptoms. Borderline dysplasia is most commonly defined as a lateral center-edge angle (LCEA) of 20° to 25°. However, its prevalence has not been well established in the literature. Purpose: To (1) define the prevalence of borderline hip dysplasia in the general population as well as in populations presenting with hip pain using a systematic review and meta-analysis of the literature and (2) describe differences between male and female patients as well as differences in prevalence from that of classic acetabular dysplasia. Study Design: Systematic review; Level of evidence, 3. Methods: A systematic review of the literature was performed using search terms to capture borderline dysplasia, or studies reporting prevalence by LCEA. The search yielded 1932 results, of which 11 articles met inclusion criteria and were included in the final systematic review. Studies were grouped by patient cohort as (1) asymptomatic general population, (2) asymptomatic targeted population (eg, athletes in a specific sport), and (3) symptomatic hip pain population. The reporting of prevalence rates by subject or by hip was recorded. In a study, the rates of borderline dysplasia were compared with those of classic acetabular dysplasia (LCEA, \u3c20°). Results: The 11 studies included 19,648 hips (11,754 patients). In the asymptomatic general population, the pooled estimate of the prevalence of borderline dysplasia was 19.8% by subject and 23.3% by hip (range, 16.7%-46.0%). The targeted subpopulation group included 236 athletes with subgroups in ballet, football, hockey, volleyball, soccer, and track and field with prevalence ranging from 17.8% to 51.1%. The prevalence of borderline dysplasia in groups presenting with hip pain was 12.8% (range, 12.6%-16.0%). Borderline acetabular dysplasia was 3.5 times more common than classic acetabular dysplasia in the asymptomatic general population. Conclusion: This study demonstrated a prevalence of borderline dysplasia of 19.8% to 23.3% in the asymptomatic general population. Additionally, an estimated prevalence of 12.8% of hips in symptomatic patients highlights the common decision-making challenges in this population

Dynamics of cost uncertainty for innovative high value manufacturing products - a geometric phenomenon

In practice the forecasting of cost uncertainty for high value manufacturing products is typically a statistical exercise focused on predicting a static cost range at a future point in time. This only leads to robust forecasts if sufficient historical data is available, robust knowledge of cost estimating relationships exists and these relationships do not change in the time between creating the forecast and verifying its accuracy. The more innovative the product is the less likely it however is that these prerequisites are met. Using cost data from the U.K. Ministry of Defence Royal Air Force A400M transport aircraft from 2002 to 2014 as an example, the dynamics of cost estimating relationships over time are examined using a novel non-statistical forecasting approach. The approach considers cost uncertainty as a geometric phenomenon, does not rely on prior information and permits easy identification of patterns in changes of cost estimating relationships over time

On the change of cost risk and uncertainty throughout the life cycle of manufacturing products

In practice cost estimators typically assume that cost risk and uncertainty continuously decrease across the whole product life cycle. Industry case studies and semi-structured interviews indicate that while cost risk and uncertainty decreases between technology readiness levels / stage gates, it increases when technology readiness levels / stage gates change. This increase can lead to cost risk and uncertainty levels above those at previous technology readiness levels / stage gates. This difference between assumptions in practice and evidence from case studies and semi-structured interviews may lead to the over- and / or under-assignment of capital reserves over time, thus resulting in binding project capital unnecessarily and / or the need to increase projects budgets in an unplanned manner. Further research is suggested regarding the scale of changes in cost risk and uncertainty when technology readiness level changes / stage gates are arrived at in order to improve robustness of forecasting effort

Synthesis of HDAC substrate peptidomimetic inhibitors (SPIs) using Fmoc amino acids incorporating zinc-binding groups

Syntheses of Fmoc amino acids having zinc-binding groups were prepared and incorporated into substrate inhibitor H3K27 peptides using Fmoc/tBu solid-phase peptide synthesis (SPPS). Peptide 11, prepared using Fmoc-Asu(NHOtBu)-OH, is a potent inhibitor (IC50 = 390 nM) of the core NuRD corepressor complex (HDAC1–MTA1–RBBP4). The Fmoc amino acids have the potential to facilitate the rapid preparation of substrate peptidomimetic inhibitor (SPI) libraries in the search for selective HDAC inhibitors

Genetic disorders of nuclear receptors.

Following the first isolation of nuclear receptor (NR) genes, genetic disorders caused by NR gene mutations were initially discovered by a candidate gene approach based on their known roles in endocrine pathways and physiologic processes. Subsequently, the identification of disorders has been informed by phenotypes associated with gene disruption in animal models or by genetic linkage studies. More recently, whole exome sequencing has associated pathogenic genetic variants with unexpected, often multisystem, human phenotypes. To date, defects in 20 of 48 human NR genes have been associated with human disorders, with different mutations mediating phenotypes of varying severity or several distinct conditions being associated with different changes in the same gene. Studies of individuals with deleterious genetic variants can elucidate novel roles of human NRs, validating them as targets for drug development or providing new insights into structure-function relationships. Importantly, human genetic discoveries enable definitive disease diagnosis and can provide opportunities to therapeutically manage affected individuals. Here we review germline changes in human NR genes associated with "monogenic" conditions, including a discussion of the structural basis of mutations that cause distinctive changes in NR function and the molecular mechanisms mediating pathogenesis

Multistep prediction of dynamic uncertainty under limited data

Engineering systems are growing in complexity, requiring increasingly intelligent and flexible methods to account for and predict uncertainties in service. This paper presents a framework for dynamic uncertainty prediction under limited data (UPLD). Spatial geometry is incorporated with LSTM networks to enable real-time multistep prediction of quantitative and qualitative uncertainty over time. Validation is achieved through two case studies. Results demonstrate robust prediction of trends in limited and dynamic uncertainty data with parallel determination of geometric symmetry at each time unit. Future work is recommended to explore alternative network architectures suited to limited data scenarios.Engineering and Physical Sciences Research Council (EPSRC): 194431

A cluster randomized trial comparing deltamethrin and bendiocarb as insecticides for indoor residual spraying to control malaria on Bioko Island, Equatorial Guinea.

BACKGROUND: Indoor residual spraying (IRS) has been used on Bioko for malaria control since 2004. In 2013 the insecticide was changed from bendiocarb to deltamethrin. Shortly after this change, there was a marked increase in malaria prevalence on the island. This trial was carried out to compare the effectiveness of bendiocarb and deltamethrin for use in IRS on Bioko. METHODS: Twenty-four clusters of houses were randomized to receive IRS with either bendiocarb or deltamethrin. Approximately 3 months after the intervention, the prevalence of malaria and levels of haemoglobin were measured in children aged 2-14 years in each cluster. RESULTS: Prevalence of malaria in 2-14 year olds was lower in the bendiocarb arm (16.8, 95 % CI 11.1-24.7, N = 1374) than in the deltamethrin arm (23.2, 95 % CI 16.0-32.3, N = 1330) but this difference was not significant (p = 0.390), even after adjusting for covariates (p = 0.119). Mean haemoglobin in children was marginally higher in the bendiocarb clusters (11.6 g/dl, 95 % CI 11.5-11.8, N = 1326) than in the deltamethrin clusters (11.5 g/dl, 95 % CI 11.3-11.7, N = 1329). This difference was borderline significant after adjusting for covariates (p = 0.049). CONCLUSIONS: The results are suggestive of bendiocarb being more effective at preventing malaria on Bioko although evidence for this was weak. The results are likely due to the fact that local vectors remain fully susceptible to bendiocarb whereas subsequent tests have shown resistance to deltamethrin