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Travel time prediction is vital to the development and maintainence of advanced intelligent transportation system technologies. The travel time on a road segment is dependent on various factors like dynamic traffic demands, incidents, weather conditions, and geometric factors. However, uncertainties associated with prediction performance consistency may reduce the effectiveness of such systems. To tackle these challenges, this paper proposes a hybrid deep learning algorithm-based methodology by integrating variational mode decomposition, multivariate long short-term memory, and quantile regression to predict estimates of travel time ranges instead of single-point predictions. Travel time data collected from loop detectors on motorways near the city of Dublin, Republic of Ireland were modeled. The proposed method was evaluated using various design scenarios and was found to perform efficiently in comparison with conventional deep learning algorithms.

Heavy Duty Vehicles (HDV) produce significant vehicular emissions, which are expected to increase in Ireland due to economic growth and the associated increase in freight transport. Ireland has adopted the European Low Emission Mobility Strategy and the national target of achieving 20% energy efficiency by 2020. From these targets comes the imminent need to address the issue of vehicle emissions from the Irish HDV fleet. The main objective of the MAP-HDV*⁎http://maphdv.ie/project was to investigate emission-mitigating measures to reduce emissions from the Irish HDV sector. In MAP-HDV project the Vehicle Energy Consumption Calculation Tool (VECTO) tool has been effectively used and for the first time to analyse Irish HDV. This paper shows the results of investigating the impact of speed, acceleration, loading factor and alternative fuels on fuel consumption and CO2emissions using the VECTO tool.

As research into intelligent transportation systems, it is essential to understand and examine the different factors that will impact using and accepting autonomous vehicles. To address this issue, a model of user acceptance for autonomous vehicles was adopted from existing Technology Acceptance Model (TAM) for generic technologies. A 29-item questionnaire is developed according to the model and survey conducted to evaluate autonomous vehicle owners, willing to own autonomous vehicles and vehicle owners. In the survey (N = 350) participants where (n = 220) are level 2 to 4 automation based on Society of Automobile Engineers (SAE) vehicle owners. Results showed that adopting autonomous vehicles as part of the transportation system would enhance individualised work performance besides reducing the accident rate. Based on findings, a number of implications for developers and suggestions for future research are recommended.

In recent years, assisted driving and self-driving have captured the imagination of manufacturers, designers, technology providers, and the general public with the expectation of a sustainable, safer, and intelligent mobility in the near future. Self-driving or assisted driving vehicles are complex systems that integrate environmental perception, intelligent planning and decision-making, tracking, and control. With the increasing intelligence of vehicles, personalized design is an inevitable trend. A design that is in line with the driver’s personality can bring a better driving experience to the driver. Thus, classifying driving types while driving in a self-driving environment may play an important role in the construction of trajectory planning algorithms. This paper uses the motion-in-mind model from game refinement theory to model driver behavior. Further, a classification of the model parameters into three categories helped in distinguishing cautious, aggressive, and average drivers. The results showed that the self-driving environment can be successfully modeled as a game and adaptation to match the riders’ driving skills may improve satisfaction.

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An interprofessional conference that addressed basic palliative care knowledge and skills was implemented annually at a large, academic healthcare institution. The conference significantly influenced participant’s self-perceived competencies in the delivery of quality palliative care. This conference led to a sustainable program of palliative care initiatives consisting of a yearly workshop for nurses focused on palliative care core competencies and a biennial interprofessional conference designed to facilitate networking and address significant topics in palliative care.

Air pollution has been linked to 491,000 deaths in Europe annually and diesel vehicles are one of the major sources of two deadly air pollutants, PM2.5(Particulate Matter) and NOx (Oxides of Nitrogen). Ireland has the highest number of newly registered diesel vehicles in Europe with a share of 43.57% in the overall fleet which is expected to increase to 73.9% by 2025. This will have significant health and financial impacts, especially in the urban areas. The present study quantifies the environmental, financial and health burdens from cars, Light Commercial Vehicles (LCVs) and buses in Dublin under the existing conditions and estimates the potential impact of the increasing numbers of diesel cars, LCVs and buses following the current trend projected over the next decade. As a preventative measure, the present study examines the impact of banning diesel vehicles older than 20 years from 2018 through the initiation of a phase-out policy. Furthermore, the impacts of a policy to ban new diesel vehicle sales from the year 2025 is also estimated. In both future scenarios, the preventative measures show significant savings in terms of reductions in NOx and PM2.5emissions which were found to be 47% and 52% respectively in the year 2030 compared to the year 2015 levels. Additionally, this reduction will amount to savings of 300 DALYs and €43.8 million. Consequently, the results of this study make a strong case for policies and investments aimed at reducing the environmental impact caused by urban transportation and improving public health.

Psychological distress, including anxiety and depression, is commonly reported by patients with implantable left ventricular assist devices (LVADs). Still, the influence of anxiety and depression on global quality of life (QOL) is unknown. Moreover, the empirical data of LVAD patients’ QOL is limited to 24 months. This study examined the differences in anxiety, depression, and QOL based on 3 LVAD implant duration groups (≤12, 13–24, and ≥25 months). We also examined the influence of anxiety and depression on QOL. An observational cross-sectional study design was employed using self-administered demographics, anxiety, depression, and global QOL questionnaires. A total of 100 patients (mean age 52 ± 13.3 years) across the United States completed the study. In the 3 implant duration groups, we found no significant differences in anxiety and depression, but the global QOL scores of patients with LVADs ≥ 13 months were significantly worse than those of patients with LVADs ≤ 12 months. Also, the correlations between QOL and either anxiety or depression were significant: Pearson’s rranged from −0.44 to −0.65 with all pvalues <0.0001. Compared with anxiety, depression is a stronger predictor of QOL. Our data showed that when depression is increased by 1 unit, the average QOL decreased by 0.76 units, assuming anxiety is held constant. This study uncovered relationships among anxiety, depression, and QOL in patients with long-term LVADs. Longitudinal research is needed to confirm our findings and explore factors that may contribute to worse QOL, particularly, those patients supported with LVADs beyond 13 months.

Lack of benchmarks for wind power forecasting models undermine their potential and consequently their implementation for industry applications. Despite the extensive existing literature in the field, a unified framework has not been created yet. Therefore, we propose a benchmark set-up where statistical wind power forecasting models can be tested under standardized criteria with respect to data, time resolution, and prediction horizon, while evaluating them under varied representative operational conditions of wind farms. The utility of this framework is shown with an example case applied to mode decomposition models, which have shown a higher performance compared to other statistical models in recent times. Data collected from two Irish wind farms are used to calculate the accuracy of statistical wind power forecasting models. Their robustness is also examined by providing an assessment of such models under several scenarios of power generation. In the example case of this benchmark, results indicate that the use of variational mode decomposition as decomposition algorithm together with advanced recurrent networks provide the best performance among the evaluated forecasting models.

There is an emerging consensus that personal and societal health benefits in cycling largely outweigh the risks. However, there exists limited research into the health impacts experienced by individuals who take up cycling or the marginal societal benefits resulting from incremental uptake of cycling. This paper models and estimates the health impacts of individuals in Dublin taking up cycling. The paper utilizes the 2011 census data of Ireland and a Burden of Disease (BOD) approach is used to estimate health impacts on the individuals taking up cycling for their regular commute and on the rest of the local population separately. The health impact to an individual changing from private car to cycling ranged from a benefit of 0.033 Disability Adjusted Life Years (DALYs)/year to a loss of 0.003 DALYs/year. The marginal health impact to the local population ranged from no change to a benefit of 0.006 DALYs/year. Increases in cycling have a consistently positive impact on the health of the local population, regardless of the current modal split. The net expected health impacts to the individual cyclists are also positive in most cases. However, for some individuals in the 20–29 age group, the expected health impact may be small to negative, mainly due to a higher traffic collision risk. Where total impacts of scenarios are modelled the potential negative health impacts to some individuals may be masked by the overall positive health benefits of cycling to the local population. When promoting cycling as an alternative to driving to improve population health impacts, the risks to some cyclists should be managed and mitigated through safe road systems approaches.

Background:Educating nurses about palliative and end-of-life (EOL) care is a high priority in health care settings. The purpose of this study was to assess nurses' perceived competency regarding the provision of palliative and EOL care to hospitalized patients.Method:This study surveyed nurses from 25 pediatric and adult acute and intensive care units (ICU; This study surveyed nurses from 25 pediatric and adult acute and intensive care units (ICU; N= 583) Quantitative data analysis was descriptive and correlational. Qualitative data analysis identified themes of participant concerns.Results:Data analysis revealed that perceived competency in palliative and EOL care is significantly higher in the ICU nurses (Data analysis revealed that perceived competency in palliative and EOL care is significantly higher in the ICU nurses (p<.0001). Mean scores were significantly higher when nurses had more than 10 years of experience (p<.0001). Open-ended responses indicated concerns regarding improved communication behaviors, decision making, and facilitation of continuity of care.Conclusion:The results provide guidance for development of palliative and EOL care nursing education programs tailored to address specific unit needs according to staff characteristics, patient population focus of care, and acuity level of care.2017;48(7):329–336.J Contin Educ Nurs.2017;48(7):329–336.

Many European cities are becoming increasingly dependent on motorized transportation, with impacts ranging from traffic congestion and greenhouse gas emissions to sedentary lifestyles and an increased risk of noncommunicable diseases. The promotion of active modes of travel in urban environments has the potential to mitigate the external costs of motorized transportation and improve the physical and mental well-being of transport users. The present study considered a modal shift to active travel in commuter trips in Dublin, Ireland, and quantified the resultant benefits and detriments to individual transport users and to society. The total impact was found to be strongly positive: the health benefits of increased physical activity dominated the individual benefits, and reductions in noise and congestion were the most significant external benefits. The benefits were partially offset by an increase in the cost of road traffic injuries, particularly nonfatal cyclist injuries. Women, and to a lesser extent men, in the age group 25 to 34 years old were identified as those who made the most driving trips that could be taken on foot or bicycle instead. Although some uncertainties remained, these results made a strong case for policies and investments that make cycling and walking in cities more attractive to alleviate the environmental damage caused by urban transportation and to improve public health.

Many European cities are becoming increasingly dependent on motorized transportation, with impacts ranging from traffic congestion and greenhouse gas emissions to sedentary lifestyles and an increased risk of noncommunicable diseases. The promotion of active modes of travel in urban environments has the potential to mitigate the external costs of motorized transportation and improve the physical and mental well-being of transport users. The present study considered a modal shift to active travel in commuter trips in Dublin, Ireland, and quantified the resultant benefits and detriments to individual transport users and to society. The total impact was found to be strongly positive: the health benefits of increased physical activity dominated the individual benefits, and reductions in noise and congestion were the most significant external benefits. The benefits were partially offset by an increase in the cost of road traffic injuries, particularly nonfatal cyclist injuries. Women, and to a lesser extent men, in the age group 25 to 34 years old were identified as those who made the most driving trips that could be taken on foot or bicycle instead. Although some uncertainties remained, these results made a strong case for policies and investments that make cycling and walking in cities more attractive to alleviate the environmental damage caused by urban transportation and to improve public health.

In this article the multibody simulation software package MADYMO for analysing and optimizing occupant safety design was used to model crash tests for Normal Containment barriers in accordance with EN 1317. The verification process was carried out by simulating a TB31 and a TB32 crash test performed on vertical portable concrete barriers and by comparing the numerical results to those obtained experimentally. The same modelling approach was applied to both tests to evaluate the predictive capacity of the modelling at two different impact speeds. A sensitivity analysis of the vehicle stiffness was also carried out. The capacity to predict all of the principal EN1317 criteria was assessed for the first time: the acceleration severity index, the theoretical head impact velocity, the barrier working width and the vehicle exit box. Results showed a maximum error of 6% for the acceleration severity index and 21% for theoretical head impact velocity for the numerical simulation in comparison to the recorded data. The exit box position was predicted with a maximum error of 4°. For the working width, a large percentage difference was observed for test TB31 due to the small absolute value of the barrier deflection but the results were well within the limit value from the standard for both tests. The sensitivity analysis showed the robustness of the modelling with respect to contact stiffness increase of ±20% and ±40%. This is the first multibody model of portable concrete barriers that can reproduce not only the acceleration severity index but all the test criteria of EN 1317 and is therefore a valuable tool for new product development and for injury biomechanics research.

ABSTRACTThis paper proposes High Dynamic Range (HDR) imaging as a protocol for nondestructive-testing for the first time. HDR imaging protocol has been experimentally validated on images of pitting corrosion in this paper and has been further applied to isolate a range of image backgrounds that arise out of a number of environmental conditions. The superiority of HDR imaging over a standard imaging process has been demonstrated. The results indicate that the proposed protocol is not limited by the examples considered in this paper and can readily be applied to a number of infrastructure maintenance management related applications. The protocol complements and improves standard visual inspection techniques and expert opinions. This approach immediately lends itself to further mathematical and statistical analyses, both qualitative and quantitative.