ISM Code implementation: an investigation of safety issues in the shipping industry

Acknowledging that shipping safety continues to be a major topic of interest in the maritime community, the present study attempts to identify the critical factors of International Safety Management (ISM) Code implementation, to identify the dimensions that describe whether the ISM Code has fulfilled its intended purpose as well as to assess their interrelationships. Moreover, the present study aims to explore whether the fulfillment of the Code’s intended purpose is associated with accident reduction. Evidence was drawn from a sample of 63 masters and chief officers who have hands-on onboard experience on ISM Code implementation and data were analyzed through the use of Exploratory Factor Analysis (EFA), correlation analysis, and binomial logistic regression. Findings indicate that the critical factors of ISM Code implementation can be categorized in the crew-related and the company-related dimensions. Also, the fulfillment of the intended purpose of the ISM Code consists of the factors of safety management system efficiency and increased safety, which were also found to be significantly associated with the crew-related dimension of ISM Code implementation. Finally, results showed that both safety management system efficiency and increased safety increase the probability of accident reduction at the highest level.     

Trip misreporting forecast using count data model in a GPS enhanced travel survey

As Global Positioning System (GPS) technology advances, it has been increasingly used to supplement traditional self-reported travel surveys due to its promising features in capturing travel data with better accuracy and reliability. Realizing the limitations of diary-based surveys, this paper presents a study that directly accounts for trip misreporting behavior in trip generation models. Travel data were obtained from prompted-recall assisted GPS survey along with a diary-based survey. Negative Binomial models for count data were developed to accommodate misreporting behavior by introducing interaction effects of the sample-indicator variable with various personal and household variables. The interaction effects indicate how the impacts of the socioeconomic and demographic variables on trip-making vary across the two samples. Assuming that the GPS sample represents the ground truth, the interaction effects actually capture the likelihood and the extent of trip misreporting by detailed personal and household characteristics. The model results reveal significant interaction effects of several personal and household variables, indicating misreporting behavior associated with these attributes. The addition of interaction coefficients to the main effect model represents the real impacts of the independent variables, after compensating for trip misreporting behavior, if any.     

Maritime law issues related to the operation of unmanned autonomous cargo ships

The paper analyses the issue of unmanned autonomous ships from the perspective of international maritime law with a particular focus on crew related conventions and specifically SOLAS, STCW, and MLC. Our primary aim is to identify potential operational difficulties that could deter shipowners from investing or adopting the new technology. Our analysis indicates that there are several areas of ambiguity that would create impediments to a positive investment decision and deter shipowners from adopting the autonomous ship design. With these findings in mind, suggestions are put forward that could alleviate the problems identified.     

Detecting Premature Ventricular Contraction in Children with Deep Learning

Premature ventricular contractions (PVCs) are abnormal heart beats that indicate potential heart diseases. Diagnosis of PVCs is made by physicians examining long recordings of electrocardiogram (ECG), which is onerous and time-consuming. In this study, deep learning was applied to develop models that can detect PVCs in children automatically. This computer-aided diagnosis model achieved high accuracy while sustained stable performance. It could save time and repeated efforts for physicians, enabling them to focus on more complicated tasks.This study is a first step toward children’s PVC auto-detection in clinics. Further study will improve the model’s performance with optimized structure and more data in different sources, while facing the challenges of the variety and uncertainty of children’s ECG with heart diseases.     

Research on Improved Low-Energy Adaptive Clustering Hierarchy Protocol in Wireless Sensor Networks

In wireless sensor networks (WSNs), due to the limited battery power of the sensor nodes, the communication energy consumption is the main factor to affect the lifetime of the networks. A reasonable design of the communication protocol can effectively reduce the energy consumption of the network system. Based on low-energy adaptive clustering hierarchy (LEACH), an improved LEACH protocol in WSNs is proposed. In order to optimize the cluster head (CH) election in the cluster setup phase, the improved LEACH takes into account a number of factors, including energy consumption of communication between nodes, remaining energy of the nodes, and the distance between nodes and base station (BS). In the steady phase, one-hop routing and multiple-hop routing are combined to transmit data between CHs to improve energy efficiency. The forward CH is selected as relay node according to the values of path cost. The simulation results show that the proposed algorithm performs better in balancing network energy consumption, and it can effectively improve the data transmission efficiency and prolong the network lifetime, as compared with LEACH, LEACH-C (LEACH-centralized) and NDAPSO-C (an adaptive clustering protocol based on improved particle swarm optimization) algorithms.     

Analysis of variability in multi-day GPS imputed activity-travel diaries using multi-dimensional sequence alignment and panel effects regression models

The use of GPS devices and smartphones has made feasible the collection of multi-day activity-travel diaries. In turn, the availability of multi-day travel diary data opens up new avenues for analyzing dynamics of individual travel behavior. This paper addresses the issue of day-to-day variability in activity-travel behavior. The study, which is the first of its kind in China, applies a unique combination of methods to analyze the degree of dissimilarity between travel days using multi-day GPS data. First, multi-dimensional sequence alignment is applied to measure the degree of dissimilarity in individual daily activity-travel sequences between pairs of travel days. Next, a series of panel effects regression models is used to estimate the effects of socio-demographics and days of the week. The models are estimated using multi-day activity-travel patterns imputed from GPS-enabled smartphone data collected in Shanghai, China. Results indicate that (1) days of the week have significant effects on day-to-day variability in activity-travel behavior with weekday activity-travel sequences being more similar and thereby different from weekend sequences; (2) the degree of dissimilarity in activity-travel sequences is strongly influenced by respondent socio-demographic profiles; (3) individuals having more control over and flexibility in their work schedule show greater intra-personal variability. Day-to-day variability in activity-travel behavior of this sample is similar to patterns observed in developed countries in some aspects but different in others. Strict international comparison study based on comparative data collection is required to further distinguish the sources of travel behavior differences between developing countries and developed countries. The paper ends with a discussion of the limitations of this study and the implications of the research findings for future research.     

Long-term trends in domestic US passenger travel: the past 110 years and the next 90

Based upon a long-term historical data set of US passenger travel, a model is estimated to project aggregate transportation trends through 2100. One of the two model components projects total mobility (passenger-km traveled) per capita based on per person GDP and the expected utility of travel mode choices (logsum). The second model component has the functional form of a logit model, which assigns the projected travel demand to competing transportation modes. An iterative procedure ensures the average amount of travel time per person to remain at a pre-specified level through modifying the estimated value of time. The outputs from this model can be used as a first-order estimate of a future benchmark against which the effectiveness of various transportation policy measures or the impact of autonomous behavioral change can be assessed.     

The spatial dimensions of immobility in France

In travel surveys, immobility is often approached as a technical issue that needs to be dealt with in order to measure mobility more accurately. By covering mobility patterns over a full week, the 2008 French Travel Survey allows immobility to be analysed other than as a marginal and random phenomenon. For working days alone, 28.8% of the adults in the survey had experienced one or more immobility episodes. By considering the intensity of immobility, and by introducing latent variables into Structural Equation Modelling, we have been able to propose a model with reasonable explanatory power. Our findings agree with previous studies and also show that within suburban or rural areas, access to shops or the type of local residential fabric are also factors that influence the number of immobile days. In addition, our findings show that the effects of the determinants differ between categories of individuals, notably between working adults and students on the one hand, and between retired and non-working people on the other.     

Performance Evaluation of Short Parabolic Trough Collectors Integrated with a Small-Scale Solar Power and Heating System

An investigation is presented on the performance of a small-scale solar power and heating system with short parabolic trough collectors (PTCs). The steady-state model of the short PTCs is evaluated with outside experiments. The model mainly contains the heat loss of the receiver, the peak optical efficiency and the incident angle factor consisting of incident angle modifier and end loss. It is found that the end loss effect is essential in this model when the length of the PTCs is less than 48 m, especially in the winter. The standard deviation of the steady-state model is 1.4%. Moreover, the potential energy efficiency ratio of the solar power and heating system is considerably larger than the coefficient of performance (COP) of general air-source heat pumps, and increases with the decrease of the condensation temperature. An overall system efficiency of 49% can be reached. Lastly, the existence of a water storage tank improves the flexibility of heating the building, and the volume of the water storage tank decreases with the increase of the heating water temperature.     

All minutes are not equal: travel time and the effects of congestion on commute satisfaction in Canadian cities

Despite decades of research, it is unclear under which circumstances travel is most onerous. While studies have found that some individuals derive positive utility from aspects of commuting, others have shown that traffic congestion can entail important time, monetary, and mental stress costs. Moreover, responses to traffic congestion-related stressors differs by individual characteristics. In response, this research captures how exposure to traffic congestion events, the duration of this exposure, and individual trait susceptibility to congestion affect the utility of commuting. Working through the lens of individual satisfaction with the duration of their commute, we show that not every minute of travel is valued the same by car commuters in Canadian cities. Results suggest a complex relationship between travel time, congestion, and individual predisposition to congestion-related stress. While improvements in travel time matter for increasing commute satisfaction, it is reductions in travel in congested conditions that matter most, particularly among those susceptible to congestion-related stressors.