The need to better understand how to manage the real logistics operations in Schiphol Airport, a strategic hub for the economic development of the Netherlands, created the conditions to develop a project where academia and industry partnered to build a simulation model of the Schiphol Airport Landside operations. This paper presents such a model using discrete-event simulation. A realistic representation of the open road network of the airport as well as the (un)loading dock capacities and locations of the five ground handlers of Schiphol Airport was developed. Furthermore, to provide practitioners with applicable consolidation and truck-dispatching policies, some easy-to-implement rules are proposed and implemented in the model. Preliminary results from this model show that truck-dispatching policies have a higher impact than consolidation policies in terms of both distance travelled by cooperative logistic operators working within the airport and shipments’ average flow time. Furthermore, the approach presented in this study can be used for studying similar megahubs.
Current practice regarding risk assessment contemplates the severity and likelihood of risks and employs the use of matrices where these factors are classified and cross-referenced to evaluate risk levels. Depending on the adequacy and reliability of data, the likelihood is estimated with quantitative or qualitative methods; severity is estimated according to experience from past events. This standard technique for assessing risks has been negatively criticised regarding validity and reliability due to effects of cognitive biases and a deterministic view of the possible consequences of risks. Even more, because of the lack of standardisation in risk matrices, a benchmarking across systems and organisations is not feasible. Taking into account the limitations mentioned, as well as the fact that the classification of hazards/causal factors, risk event(s) and consequences always depend on the analyst's view, this study proposes the Safety Risk Avoidance Capability (SAREAC) metric for a defined system. This metric focus on the prevention of risk events and combines quantitative and qualitative parameters referred in the literature but not yet exploited. SAREAC consists of two parts: the influence of hazards and the remaining effects of hazards after implementing or designing controls. Each of the SAREAC parts is calculated through specific steps which they result in a normalized score that allows more reliable comparisons amongst systems or over time. Data from a published risk assessment case study were used to demonstrate the use of SAREAC.
Objectives Patients who underwent corrective surgery for tetralogy of Fallot (TOF) have increased long-term risk of cardiovascular morbidity and mortality. Yet, limited information is available on how to evaluate the risk in this population. Therefore, the aim of this study was to investigate the prognostic value of aerobic exercise capacity, along with other related parameters, at medium-term follow-up in adult patients with tetralogy of Fallot. Methods and results Between 2000 and 2003, 92 adults (age 26.2 ± 7.8 years; 63 male) with corrected TOF or TOF-type morphology underwent a cardiopulmonary exercise test (CPET) until exhaustion and echocardiography. During a mean follow-up of 7.3 ± 1.2 years (range 0.9 to 9.3 years), 2 patients died and 26 patients required at least 1 cardiac-related intervention at a mean age of 28.9 ± 7.9 years. Event-free survival tended to be higher in patients with the classical type of TOF (P = 0.061). At multivariate Cox analysis, age at CPET [hazard ratio (HR): 1.13, P = 0.006], age at correction (HR: 0.82, P = 0.037), right ventricular (RV) function (HR: 4.94, P = 0.001), QRS duration (HR: 1.02, P = 0.007), percentage of predicted peak oxygen uptake (peak VO2%) (HR: 0.96, P = 0.029) and ventilatory effi ciency slope (VE/VCO2 slope) (HR: 1.13, P = 0.021) were signifi cantly related to the incidence of death/cardiac-related intervention during medium follow-up. Conclusions Early corrective surgery and a well-preserved RV are associated with a better outcome in adults with corrected TOF. Furthermore, CPET provides important prognostic information; peak VO2% and VE/VCO2 slope are independent predictors for event-free survival in patients with corrected TOF.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
Electrohydrodynamic Atomization (EHDA), also known as Electrospray (ES), is a technology which uses strong electric fields to manipulate liquid atomization. Among many other areas, electrospray is used as an important tool for biomedical application (droplet encapsulation), water technology (thermal desalination and metal recovery) and material sciences (nanofibers and nano spheres fabrication, metal recovery, selective membranes and batteries). A complete review about the particularities of this tool and its application was recently published (2018), as an especial edition of the Journal of Aerosol Sciences. One of the main known bottlenecks of this technique, it is the fact that the necessary strong electric fields create a risk for electric discharges. Such discharges destabilize the process but can also be an explosion risk depending on the application. The goal of this project is to develop a reliable tool to prevent discharges in electrospray applications.
