Modeling of a heavy duty diesel engine to ease complex optimization decisions

Engine optimization becomes more difficult every day, more and more limits regarding emissions of noxious components have to be met. Considering heavy duty marine engines such as the 6DZC from ABC there are several important instances: IMO III reduces NOx by 75% from 2021, EPA reduces NOx by 70% from 2016. Therefore very complex systems are implemented, which each have multiple calibration or working parameters. Some are fixed, some can change depending on engine load and speed. An example of a fixed parameter is compression ratio, it can only be changed while building the engine. Other fixed parameters include: choice of injection nozzle (#holes, hole-diameter), bore, stroke, etc. Exhaust gas recirculation (EGR) is a parameter that can be changed continuously during operation of the engine. Typically there are other parameters present: Variable Valve Timing, injection timing, injection duration, injection pressure, secondary injection, wastegate setting(s), etc. Ideally these parameters are configured in a way that the engine emits very little harmful components and fuel consumption is very low. The most straightforward approach would be to test every parameter combination, record emission components and fuel consumption and choose the optimal parameter combination. This has to be repeated for every speed and load of the engine, which results in an engine map. This method becomes more and more expensive, both in time as in fuel consumption because every additional operating parameter increases the amount of tests exponentially. This is why engine simulation becomes inevitable. Accurate engine simulation is able to exclude regions of parameter values that are clearly infeasible and can give a good indication where engine tests are more interesting

Gamified cognitive control training for remitted depressed individuals : user requirements analysis

Background: The high incidence and relapse rates of major depressive disorder demand novel treatment options. Standard treatments (psychotherapy, medication) usually do not target cognitive control impairments, although these seem to play a crucial role in achieving stable remission. The urgent need for treatment combined with poor availability of adequate psychological interventions has instigated a shift toward internet interventions. Numerous computerized programs have been developed that can be presented online and offline. However, their uptake and adherence are oftentimes low. Objective: The aim of this study was to perform a user requirements analysis for an internet-based training targeting cognitive control. This training focuses on ameliorating cognitive control impairments, as these are still present during remission and can be a risk factor for relapse. To facilitate uptake of and adherence to this intervention, a qualitative user requirements analysis was conducted to map mandatory and desirable requirements. Methods: We conducted a user requirements analysis through a focus group with 5 remitted depressed individuals and individual interviews with 6 mental health care professionals. All qualitative data were transcribed and examined using a thematic analytic approach. Results: Results showed mandatory requirements for the remitted sample in terms of training configuration, technological and personal factors, and desirable requirements regarding knowledge and enjoyment. Furthermore, knowledge and therapeutic benefits were key requirements for therapists. Conclusions: The identified requirements provide useful information to be integrated in interventions targeting cognitive control in depression

ArcAid interactive archery assistant

This paper describes the design process of a bow aiming system, called ArcAid, which is an interactive archery assistant. The main goal of ArcAid is to introduce a way for beginner Robin Hoods to learn the art of archery to its fullest. In order to achieve this goal, our smartphone-based design focuses on a fun and interactive learning process that gives constant feedback to the user on how to hit a certain goal. A SPIKE high- end laser sensor is used for the distance measurement and the smartphone’s accelerometer is used to define the angle of inclination. To measure the force on the arrow and the displacement of the string, a flex sensor is attached upon one of the arcs of the bow. All sensor data is processed in an Arduino Nano microprocessor and feedback to the user is given by a dedicated smartphone app. In this paper, we mainly focus on the construction, mechanics and electronics of the ArcAid bow and on the design of the mobile app, which is the game controller. Furthermore, we briefly discuss some future development ideas

The effect of smoking on survival and bone loss of implants with a fluoride-modified surface: a 2-year retrospective analysis of 1106 implants placed in daily practice

Aim: To compare survival and peri-implant bone loss of implants with a fluoride-modified surface in smokers and nonsmokers. Materials and Methods: Patient files of all patients referred for implant treatment from November 2004 to 2007 were scrutinized. All implants were placed by the same experienced surgeon (BC). The only inclusion criterion was a follow-up time of at least 2 years. Implant survival and bone loss were assessed by an external calibrated examiner (SV) comparing digital peri-apical radiographs taken during recall visits with the post-operative ones. Implant success was determined according to the international success criteria (Albrektsson et al. 1986). Survival of implants installed in smokers and nonsmokers were compared using the log-rank test. Both non-parametric tests and fixed model analysis were adopted to evaluate bone loss in smokers and nonsmokers. Results: 1106 implants in 300 patients (186 females; 114 males) with a mean follow-up of 31 months (SD 7.15; range 24-58) were included. 19 implants in 17 patients failed, resulting in an overall survival rate of 98.3% on implant level and 94.6% on patient level. After a follow-up period of 2 years, the CSR was 96.7% and 99.1% with the patient and implant as statistical unit respectively. Implant survival was significantly higher for nonsmokers compared to smokers (implant level p = 0.025; patient level p = 0.017). The overall mean bone loss was 0.34 mm (n = 1076; SD 0.65; range 0.00-7.10). Smokers lost significantly more bone compared to nonsmokers in the maxilla (0.74 mm; SD 1.07 vs 0.33 mm; SD 0.65; p < 0.001), but not in the mandible (0.25mm; SD 0.65 vs 0.22mm; SD 0.50; p = 0.298). Conclusion: The present study is the first to compare peri-implant bone loss in smokers and nonsmokers from the time of implant insertion (baseline) to at least 2 years of follow-up. Implants with a fluoride-modified surface demonstrated a high survival rate and limited bone loss. However, smokers are at higher risk to experience implant failure and more prone to show peri-implant bone loss in the maxilla. Whether this bone loss is predicting future biological complications remains to be evaluated