Assessing the efficiency of maintenance operators: a case study of turning railway wheelsets on an under-floor wheel lathe

The present paper assesses the technical efficiency of different operators turning railway wheelsets on a under-floor wheel lathe. This type of lathe is a Computer Numerical Control (CNC) machine used to turn wheelsets in-situ on the train. As railway wheels are turned, a certain amount of the wheel diameter is lost to restore the tread profile and full flange thickness of the wheel. The technical efficiencies of the different wheel lathe operators are assessed using a Stochastic Frontier Analysis (SFA), whilst controlling for other explaining variables such as the flange thickness and the occurrence of rolling contact fatigue (RCF) defects, wheel flats and cavities. Different model specifications for the SFA are compared with Linear Mixed Model (LMM) specifications, showing that the SFA model exhibits a better Akaike Information Criterion (AIC)

The CEDAR Project

The LHC project at CERN requires both the handling of a huge amount of engineering information and the control of the coherence of this information as the design work evolves on the machine and the experiments. A commercial Engineering Data Management System, (EDMS), is being implemented to manage data for the design, construction, installation and maintenance of both the accelerator and the experiments. This CERN-wide project is called CEDAR The World Wide Web is used to make the information accessible at CERN and in the external collaborating laboratories around the world. In this paper we describe the objectives of the CEDAR project, the different subprojects in the machine and the experiments as well as the first results of the implementation work

A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin

Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-source satellite-based precipitation products (SPPs) with a suitable resolution provide alternative options in these data-scarce regions, which are typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine copula-based quantile regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). The DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the multivariate linear (MLQR) and the Bayesian model averaging (BMAQ) techniques, respectively, and with two traditional merging methods – the simple modeling average (SMA) and the one-outlier-removed average (OORA) – using descriptive and categorical statistics. Four SPPs have been considered in this study, namely, Tropical Applications of Meteorology using SATellite (TAMSAT v3.1), the Climate Prediction Center MORPHing Product Climate Data Record (CMORPH-CDR), Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG v06), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR). The bilinear (BIL) interpolation technique was applied to downscale SPPs from a coarse to a fine spatial resolution (1 km). The rugged-topography region of the upper Tekeze–Atbara Basin (UTAB) in Ethiopia was selected as the study area. The results indicate that the precipitation data estimates with the DVQR, MLQR, and BMAQ models and with traditional merging methods outperform the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. The DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over the UTAB. The DVQR model substantially improved all of the statistical metrics (CC = 0.80, NSE = 0.615, KGE = 0.785, MAE = 1.97 mm d−1, RMSE = 2.86 mm d−1, and PBIAS = 0.96 %) considered compared with the BMAQ and MLQR models. However, the DVQR model did not outperform the BMAQ and MLQR models with respect to the probability of detection (POD) and false-alarm ratio (FAR), although it had the best frequency bias index (FBI) and critical success index (CSI) among all of the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over regions with rugged topography such as the UTAB.</p

Steps to improve gender diversity in the fields of coastal geosciences and engineering

Robust data are the base of effective gender diversity policy. Evidence shows that gender inequality is still pervasive in science, technology, engineering and mathematics (STEM). Coastal geoscience and engineering (CGE) encompasses professionals working on coastal processes, integrating expertise across physics, geomorphology, engineering, planning and management. The article presents novel results of gender inequality and experiences of gender bias in CGE, and proposes practical steps to address it. It analyses the gender representation in 9 societies, 25 journals, and 10 conferences in CGE and establishes that women represent 30% of the international CGE community, yet there is under-representation in prestige roles such as journal editorial board members (15% women) and conference organisers (18% women). The data show that female underrepresentation is less prominent when the path to prestige roles is clearly outlined and candidates can self-nominate or volunteer instead of the traditional invitation-only pathway. By analysing the views of 314 survey respondents (34% male, 65% female, and 1% ‘‘other’’), we show that 81% perceive the lack of female role models as a key hurdle for gender equity, and a significantly larger proportion of females (47%) felt held back in their careers due to their gender in comparison with males (9%). The lack of women in prestige roles and senior positions contributes to 81% of survey respondents perceiving the lack of female role models in CGE as a key hurdle for gender equality. While it is clear that having more women as role models is important, this is not enough to effect change. Here seven practical steps towards achieving gender equity in CGE are presented: (1) Advocate for more women in prestige roles; (2) Promote high-achieving females; (3) Create awareness of gender bias; (4) Speak up; (5) Get better support for return to work; (6) Redefine success; and, (7) Encourage more women to enter the discipline at a young age. Some of these steps can be successfully implemented immediately (steps 1–4), while others need institutional engagement and represent major societal overhauls. In any case, these seven practical steps require actions that can start immediately

The academy for future science faculty:randomized controlled trial of theory-driven coaching to shape development and diversity of early-career scientists

Background: Approaches to training biomedical scientists have created a talented research community. However, they have failed to create a professional workforce that includes many racial and ethnic minorities and women in proportion to their representation in the population or in PhD training. This is particularly true at the faculty level. Explanations for the absence of diversity in faculty ranks can be found in social science theories that reveal processes by which individuals develop identities, experiences, and skills required to be seen as legitimate within the profession. Methods/Design: Using the social science theories of Communities of Practice, Social Cognitive Career Theory, identity formation, and cultural capital, we have developed and are testing a novel coaching-based model to address some of the limitations of previous diversity approaches. This coaching intervention (The Academy for Future Science Faculty) includes annual in-person meetings of students and trained faculty Career Coaches, along with ongoing virtual coaching, group meetings and communication. The model is being tested as a randomized controlled trial with two cohorts of biomedical PhD students from across the U.S., one recruited at the start of their PhDs and one nearing completion. Stratification into the experimental and control groups, and to coaching groups within the experimental arms, achieved equal numbers of students by race, ethnicity and gender to the extent possible. A fundamental design element of the Academy is to teach and make visible the social science principles which highly influence scientific advancement, as well as acknowledging the extra challenges faced by underrepresented groups working to be seen as legitimate within the scientific communities. Discussion: The strategy being tested is based upon a novel application of the well-established principles of deploying highly skilled coaches, selected and trained for their ability to develop talents of others. This coaching model is intended to be a complement, rather than a substitute, for traditional mentoring in biomedical research training, and is being tested as such

Once the shovel hits the ground : Evaluating the management of complex implementation processes of public-private partnership infrastructure projects with qualitative comparative analysis

Much attention is being paid to the planning of public-private partnership (PPP) infrastructure projects. The subsequent implementation phase – when the contract has been signed and the project ‘starts rolling’ – has received less attention. However, sound agreements and good intentions in project planning can easily fail in project implementation. Implementing PPP infrastructure projects is complex, but what does this complexity entail? How are projects managed, and how do public and private partners cooperate in implementation? What are effective management strategies to achieve satisfactory outcomes? This is the fi rst set of questions addressed in this thesis. Importantly, the complexity of PPP infrastructure development imposes requirements on the evaluation methods that can be applied for studying these questions. Evaluation methods that ignore complexity do not create a realistic understanding of PPP implementation processes, with the consequence that evaluations tell us little about what works and what does not, in which contexts, and why. This hampers learning from evaluations. What are the requirements for a complexity-informed evaluation method? And how does qualitative comparative analysis (QCA) meet these requirements? This is the second set of questions addressed in this thesis