Intersectoral and spatial spill-overs of firms’ bankruptcy in Spain

This paper employs provincial data to study the spatial and intersectoral spill-overs in aggregate failure rates in Spain, by using an Integrated Nested Laplace Approximation. The analysis is based on NUTS3 data over the time span 2005Q1-2013Q4. By speculating on the effects of the Spanish financial crisis, we document empirical evidence of the presence of spatial spill-overs among neighboring counties. Furthermore, some intersectoral spill-overs are also detected: we observe that Industry and Agriculture exhibit a positive impact on the Service sector. These results can be useful to design proper policy rules to better manage the spread of bankruptcies over time and space.

Increased failure rates after the introduction of the TFNA proximal femoral nail for trochanteric fractures: implant related or learning curve effect?

Background and purpose — Trochanteric fractures are often treated using intramedullary fixation. In our institution, the TFN-Advanced Proximal Femoral Nailing System (TFNA) was introduced as replacement for the Gamma Trochanteric Nail (GTN3) for the treatment of these fractures as a result of a hospital-driven change of trauma implant supplier. We compared trochanteric fracture fixation failure rate between these 2 intramedullary nails. Patients and methods — All trochanteric fractures treated surgically from 2011 to 2019 were retrospectively reviewed for fixation failure. From 2016 only the TFNA was used. Fixation failure was defined as implant cut-out, implant breakage, non-union, malpositioning of the screw/blade requiring reoperation, new fracture around the nail, or miscellaneous. Propensity score matching was used to balance distribution of covariates and to compare failure rates between TFNA and GTN3 groups. Learning curve analyseswere performed. Results — After exclusion, 797 GTN3s (779 patients) and 542 (536 patients) TFNAs were available for analysis. A higher risk of fixation failure was found in the TFNA group (14%) compared with the GTN3 group (7.0%) (hazard ratio [HR] 2.0, 95% confidence interval [CI] 1.2–3.5). This was mainly attributed to a higher risk of cut-out (HR 2.2; CI 0.9–5.7), malpositioning (HR 4.7; CI 0.7–34), and new fracturearound the nail (HR 4.0; CI 1.0–16). Learning curve analyses indicated no clear learning curve effect. Interpretation — Failure of fixation increased after a switch from the GTN3 to the TFNA proximal femoral nail for the treatment of trochanteric fractures. Cut-out and malpositioning of the calcar screw or blade appeared to be the most dominant failure mechanisms. Modifications in implant design may have played a role in this increased risk of failure of fixation. In our institution a new implant device was introduced without solid clinical evidence behind it. This study may help to underline the need for medical doctors with acritical and scientific background to be involved in implant choices.

A Novel Frechet-Type Probability Distribution: Its Properties and Applications

In this article, a new lifetime model, referred to as modified Frechet–Rayleigh distribution (MFRD), is developed by accommodating an additional parameter in Rayleigh distribution on the basis of the modified Frechet method. Numerous statistical properties of the suggested model are derived and discussed. The technique of maximum likelihood (ML) estimation is adopted to get estimates of the parameters. The suggested model is very flexible and has the capability to model datasets having both monotonic and nonmonotonic failure rates. The proposed model is applied on two real datasets for checking its performance in comparison with available well-known models. The suggested model has shown outclass performance in comparison with the available versions of the Rayleigh distribution used in the literature.

Management of Big Data Projects

Big data is an emerging area of research that is of interest to various fields; however, studies in the literature and various sources claim that failure rates for big data projects are considerably high. There are different reasons for failure; varying from management processes to the use of wrong technologies. This study investigates how the project management framework proposed by Project Management Institute (PMI) can be effectively adapted to big data projects to reduce failure rates. The application of processes as mentioned in this study can help to eliminate the causes of failure in the early stages of the project; thus, increasing the successful completion rate of such projects.