The aviation industry needs led to an increase in the number of aircraft in the sky. When the number of flights within an airspace increases, the chance of a mid-air collision increases. Systems such as the Traffic Alert and Collision Avoidance System (TCAS) and Airborne Collision Avoidance System (ACAS) are currently used to alert pilots for potential mid-air collisions. The TCAS and the ACAS use algorithms to perform Aircraft Trajectory Predictions (ATPs) to detect potential conflicts between aircrafts. In this paper, three different aircraft trajectory prediction algorithms named Deep Neural Network (DNN), Random Forest (RF) and Extreme Gradient Boosting were implemented and evaluated in terms of their accuracy and robustness to predict the future aircraft heading. These algorithms were as well evaluated in the case of adversarial samples. Adversarial training is applied as defense method in order to increase the robustness of ATPs algorithms against the adversarial samples. Results showed that, comparing the three algorithm’s performance, the extreme gradient boosting algorithm was the most robust against adversarial samples and adversarial training may benefit the robustness of the algorithms against lower intense adversarial samples. The contributions of this paper concern the evaluation of different aircraft trajectory prediction algorithms, the exploration of the effects of adversarial attacks, and the effect of the defense against adversarial samples with low perturbation compared to no defense mechanism.
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For long flights, the cruise is the longest phase and where the largest amount of fuel is consumed. An in-cruise optimization method has been implemented to calculate the optimal trajectory that reduces the flight cost. A three-dimensional grid has been created, coupling lateral navigation and vertical navigation profiles. With a dynamic analysis of the wind, the aircraft can perform a horizontal deviation or change altitudes via step climbs to reduce fuel consumption. As the number of waypoints and possible step climbs is increased, the number of flight trajectories increases exponentially; thus, a genetic algorithm has been implemented to reduce the total number of calculated trajectories compared to an exhaustive search. The aircraft’s model has been obtained from a performance database, which is currently used in the commercial flight management system studied in this paper. A 5% average flight cost reduction has been obtained.
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BACKGROUND: Patients' self-care behaviour is still suboptimal in many heart failure (HF) patients and underlying mechanisms on how to improve self-care need to be studied.AIMS: (1) To describe the trajectory of patients' self-care behaviour over 1 year, (2) to clarify the relationship between the trajectory of self-care and clinical outcomes, and (3) to identify factors related to changes in self-care behaviour.METHODS: In this secondary analysis of the COACH-2 study, 167 HF patients (mean age 73 years) were included. Self-care behaviour was assessed at baseline and after 12 months using the European Heart Failure Self-care Behaviour scale. The threshold score of ⩾70 was used to define good self-care behaviour.RESULTS: Of all patients, 21% had persistent poor self-care behaviour, and 27% decreased from good to poor. Self-care improved from poor to good in 10%; 41% had a good self-care during both measurements. Patients who improved self-care had significantly higher perceived control than those with persistently good self-care at baseline. Patients who decreased their self-care had more all-cause hospitalisations (35%) and cardiovascular hospitalisations (26%) than patients with persistently good self-care (2.9%, p < 0.05). The prevalence of depression increased at 12 months in both patients having persistent poor self-care (0% to 21%) and decreasing self-care (4.4% to 22%, both p < 0.05).CONCLUSION: Perceived control is a positive factor to improve self-care, and a decrease in self-care is related to worse outcomes. Interventions to reduce psychological distress combined with self-care support could have a beneficial impact on patients decreasing or persistently poor self-care behaviour.
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The author reflects on the need for a comprehensive assessment of the structure and quality of the family or social network given that relationships are affected after the diagnosis of a cardiovascular disease. He points out that families may experience changing needs for support during the disease trajectory and emotional support may be necessary to cope with changing roles. He advocates for a family-oriented approach for patients with heart failure and their families.
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It is suggested that older patients waiting for an elective surgical procedure have a poor nutritional status and low physical activity level. It is unknown if this hypothesis is true and if these conditions improve after a medical procedure. We aimed to determine the trajectory of both conditions before and after transcatheter aortic valve implantation (TAVI). Included patients (n = 112, age 81 ± 5 years, 58% male) received three home visits (preprocedural, one and six months postprocedural). Nutritional status was determined with the mini nutritional assessment-short form (MNA-SF) and physical activity using an ankle-worn monitor (Stepwatch). The median MNA-SF score was 13 (11-14), and 27% of the patients were at risk of malnutrition before the procedure. Physical activity was 6273 ± 3007 steps/day, and 69% of the patients did not meet the physical activity guidelines (&gt;7100 steps/day). We observed that nutritional status and physical activity did not significantly change after the procedure (β 0.02 [95% CI -0.03, 0.07] points/months on the MNA-SF and β 16 [95% CI -47, 79] steps/month, respectively). To conclude, many preprocedural TAVI patients should improve their nutritional status or activity level. Both conditions do not improve naturally after a cardiac procedure.
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In deze studie onderzoeken we de ontwikkelingstrajecten van hackers, op basis van zelfgerapporteerde web defacements. Tijdens een web defacement wordt ongewenst de inhoud van een website aangepast. In totaal hebben we 50.330 defacements van websites met een Nederlandse extensie (.nl websites) geanalyseerd, die door 3640 verschillende defacers zijn uitgevoerd tussen januari 2010 en maart 2017. Met behulp van trajectory-modellen kunnen er zes groepen defacers worden onderscheiden in de analyses: twee groepen chronische daders en vier groepen daders die slechts gedurende een korte periode defacements uitvoerden. Deze groepen verschillen ook van elkaar in hun motivaties en modus operandi. De groep hoogfrequente chronische daders bestaat uit minder dan 2% van de daders, maar is verantwoordelijk voor meer dan de helft van alle defacements. Het zou dan ook het meest efficiënt zijn wanneer toekomstige interventies zich met name richten op deze kleine groep chronische daders. Voor vervolgonderzoek zou het interessant zijn om de inhoudelijke boodschap van de web defacements te onderzoeken.
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Mathematics teacher educators in primary teacher education need expert knowledge and skills in teaching in primary school, in subject matter and research. Most starting mathematics teacher educators possess only part of this knowledge and skills. A professional development trajectory for this group is developed and tested, where a design based research is used to evaluate the design. This paper describes the professional development trajectory and design. We conclude that the professional development design should focus on mathematical knowledge for teaching, should refer to both teacher education and primary education, should offer opportunities for cooperative learning, and need to use practice based research as a developmental tool.
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Just what and how eight experienced teachers in four coaching dyads learned during a 1-year reciprocal peer coaching trajectory was examined in the present study. The learning processes were mapped by providing a detailed description of reported learning activities, reported learning outcomes, and the relations between these two. The sequences of learning activities associated with a particular type of learning outcome were next selected, coded, and analyzed using a variety of quantitative methods. The different activity sequences undertaken by the teachers during a reciprocal peer coaching trajectory were found to trigger different aspects of their professional development.
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OBJECTIVE: To identify trajectories of cognitive-affective depressive symptoms among acutely hospitalized older patients and whether trajectories are related to prognostic baseline factors and three-month outcomes such as functional decline, falls, unplanned readmissions, and mortality.METHODS: Prospective multicenter cohort of acutely hospitalized patients aged ≥ 70. Depressive trajectories were based on Group Based Trajectory Modeling, using the Geriatric Depression Scale-15. Outcomes were functional decline, falls, unplanned readmission, and mortality within three months post-discharge.RESULTS: The analytic sample included 398 patients (mean age = 79.6 years; SD = 6.6). Three distinct depressive symptoms trajectories were identified: minimal (63.6%), mild persistent (25.4%), and severe persistent (11.0%). Unadjusted results showed that, compared to the minimal symptoms group, the mild and severe persistent groups showed a significantly higher risk of functional decline (mild: OR = 3.9, p < .001; severe: OR = 3.0, p = .04), falls (mild: OR = 2.0, p = .02; severe: OR = 6.0, p < .001), and mortality (mild: OR = 2.2, p = .05; severe: OR = 3.4, p = .009). Patients with mild or severe persistent symptoms were more malnourished, anxious, and functionally limited and had more medical comorbidities at admission.CONCLUSION: Nearly 40% of the acutely hospitalized older adults exhibited mild to severe levels of cognitive-affective depressive symptoms. In light of the substantially elevated risk of serious complications and the fact that elevated depressive symptoms was not a transient phenomenon identification of these patients is needed. This further emphasizes the need for acute care hospitals, as a point of engagement with older adults, to develop discharge or screening procedures for managing cognitive-affective depressive symptoms.
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Aircraft require significant quantities of fuel in order to generate the power required to sustain a flight. Burning this fuel causes the release of polluting particles to the atmosphere and constitutes a direct cost attributed to fuel consumption. The optimization of various aircraft operations in different flight phases such as cruise and descent, as well as terminal area movements, have been identified as a way to reduce fuel requirements, thus reducing pollution. The goal of this chapter is to briefly explain and apply different metaheuristic optimization algorithms to improve the cruise flight phase cost in terms of fuel burn. Another goal is to present an overview of the most popular commercial aircraft models. The algorithms implemented for different optimization strategies are genetic algorithms, the artificial bee colony, and the ant colony algorithm. The fuel burn aircraft model used here is in the form of a Performance Database. A methodology to create this model using a Level D aircraft research flight simulator is briefly explained. Weather plays an important role in flight optimization, and so this work explains a method for incorporating open source weather. The results obtained for the optimization algorithms show that every optimization algorithm was able to reduce the flight consumption, thereby reducing the pollution emissions and contributing to airlines’ profit margins.
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