CT reconstruction with priori MRI images through multi-group datasets expansion

Computed tomography (CT) reconstruction with a well-registered priori magnetic resonance imaging (MRI) image can improve reconstruction results with low-dose CT, because well-registered CT and MRI images have similar structures. However, in clinical settings, the CT image of patients does not always match the priori MRI image because of breathing and movement of patients during CT scanning. To improve the image quality in this case, multi-group datasets expansion is proposed in this paper. In our method, multi-group CT-MRI datasets are formed by expanding CT-MRI datasets. These expanded datasets can also be used by most existing CT-MRI algorithms and improve the reconstructed image quality when the CT image of a patient is not registered with the priori MRI image. In the experiments, we evaluate the performance of the algorithm by using multi-group CT-MRI datasets in several unregistered situations. Experiments show that when the CT and priori MRI images are not registered, the reconstruction results of using multi-group dataset expansion are better than those obtained without using the expansion.     

Seafarers’ perceptions of competency in risk assessment and management: an empirical study

Past analysis of accident investigations suggest an absence of or inadequate practices of risk assessment and management on board vessels. Although the International Management Code for the Safe Operation of Ships and for Pollution Prevention (ISM Code) requires that the risk management process must not only be correctly formulated and implemented but also be periodically evaluated in order to verify that these objectives are attained, the correct and effective implementation and application of the process cannot be achieved without active involvement of competent seafarers on board. Due to the limited investigation of seafarers’ competencies in risk management, the reasons for the inadequacies in the process that are leading to accidents were not evident. Hence, using a survey, this paper conducted a pilot study investigating seafarers’ perceptions of competency in the various components of the risk management process conducted on board vessels. The findings of this paper suggest an overall satisfaction with the risk assessment process. However, there is certainly room for improvement as far as seafarers’ performance is concerned. The findings of this paper provided valuable insights into the current training regimes in the area of risk assessment and management paving the way towards enhanced safety procedures on board vessels.     

Visualisation of uncertainty in probabilistic traffic models for policy and operations

This study investigates different methods to visualise uncertainty in static representations of probabilistic traffic models predictions on road-networks. Although various graphical cues may be used to represent uncertainty it is not a priori clear which of them are most suited for this purpose, since their legibility, intelligibility and the degree to which they interfere with other graphical elements in a representation differ widely. Several graphical uncertainty representations were therefore developed and analysed in expert sessions. A selection of the initial set of uncertainty visualisations was further evaluated in a cognitive alternative task-switching experiment. The results show that graphical representations are able to convey uncertainty information relatively accurately, while some uncertainty visualisations outperform others. It depends on the model and scenario which representation is most suited for a given application. This paper presents an overview of possible graphic uncertainty representations and the considerations involved when applying them to uncertainty in traffic model visualisations.     

Automatic outer surface extraction of femoral head in CT images

Computer-aided hip surgery planning and implant design applications require accurate segmentation of femoral head and proximal acetabulum. An accurate outer surface extraction of femoral head using marching cubes algorithm remains challenging due to deformed shapes and extremely narrow inter-bone regions. In this paper, we present an automatic and fast approach for segmentation of femoral head and proximal acetabulum which leads to accurate and compact representation of femoral head using marching cubes algorithm. At first, valley-emphasized images are constructed from original images so that valleys stand out in high relief. Otsu’s multiple thresholding technique is applied to seperate the images into bone and non-bone classes. Region growing method and threedimensional (3D) morphological operations are performed to fill holes in the bone. In the reclassification process, the bone regions are further segmented, and the boundaries of the bone regions are further refined based on Bayes decision rule. Finally, marching cubes algorithm is applied to reconstruct a 3D model and extract the outer surface of femoral head and proximal acetabulum. Experimental results show that this method is an accurate segmentation technique for femoral head and proximal acetabulum and it can be applied as a tool in medical practice.     

Comparison on travel scheduling between driving and walking trips by habitual car users

Research on walking behavior has become increasingly more important in the field of transportation in the past decades. However, the study of the factors influencing the scheduling decisions related to walking trips and the exploration of the differences between travel modes has not been conducted yet. This paper presents a comparison of the scheduling and rescheduling decisions associated with car driving trips and walking trips by habitual car users using a data set collected in Valencia (Spain) in 2010. Bivariate probit models with sample selection are used to accommodate the influence of pre-planning on the decision to execute a travel as pre-planned or not. The explicative variables considered are: socio-economic characteristics of respondents, travel characteristics, and facets of the activity executed at origin and at destination including the scheduling decisions associated with them. The results demonstrate that a significant correlation exists between the choices of pre-planning and rescheduling for both types of trips. Whether for car driving or walking trips, the scheduling decisions associated with the activity at origin and at destination are the most important explicative factors of the trip scheduling and rescheduling decisions. However, the rescheduling of trips is mainly influenced by modifications in the activity at destination. Some interesting differences arise regarding the rescheduling decision processes between travel modes: if pre-planned, walking trips are less likely to be modified than car driving trips, showing a more rigid rescheduling behavior.     

Key technologies of ship remote control system in inland waterways under ship-shore cooperation conditions北大核心CSCD

［Objective ］ To meet the requirements of remotely controlling ship in curved, narrow and crowded inland waterways, this paper proposes an approach that consists of CNN-based algorithms and knowledge based models under ship-shore cooperation conditions. ［Method］On the basis of analyzing the characteristics of ship-shore cooperation, the proposed approach realizes autonomous perception of the environment with visual simulation at the core and navigation decision-making control based on deep reinforcement learning, and finally constructs an artificial intelligence system composed of image deep learning processing, navigation situation cognition, route steady-state control and other functions. Remote control and short-time autonomous navigation of operating ships are realized under inland waterway conditions, and remote control of container ships and ferries is carried out. ［Results］The proposed approach is capable of replacing manual work by remote orders or independent decision-making, as well as realizing independent obstacle avoidance, with a consistent deviation of less than 20 meters. ［Conclusions］The developed prototype system carries out the remote control operation demonstration of the above ship types in such waterways as the Changhu Canal Shenzhou line and the Yangtze River, proving that a complete set of algorithms with a CNN and reinforcement learning at the core can independently extract key navigation information, construct obstacle avoidance and control awareness, and lay the foundation for inland river intelligent navigation systems. © 2022 Journal of Clinical Hepatology. All rights reserved.     

Hybrid process of fabricating high-quality micro wine-glass fused silica resonators

A new hybrid method, which combines improved glass-blown technology with wet etching, is reported to fabricate micro wine-glass resonators with high-quality fused silica. The optimum placement is compared to achieve the resonators with good shell shape. The typical shell diameter is about 4mm and its thickness covers from dozens to hundreds of micrometers. The etching rates in corrosion solutions with different ratios and at different thicknesses of hemispherical shells are studied. We also conclude how to precisely control the thickness. The corrosion solutions with different ratios of HF solution to NH₄F solution make the spherical shells rougher in different degrees. The best roughness is 0.581 nm in the 1: 8 ratio corrosion solution while the original roughness is 0.537 nm. This fact shows that the resonator remains atomically smooth surface. Based on the glassblowing spherical fused silica structure, the thickness of the resonator is effectively controlled by buffered oxide etch (BOE) technology according to the measured etching rate. The measured resonant frequency of the hemispherical shell at ambient pressure and room temperature is 1.75 kHz of rocking mode which is close to the simulated frequency. Using such a low-cost hybrid approach, we can fabricate high-quality microscale resonators in batch.     

Impacts of urban built environment on empty taxi trips using limited geolocation data

This study identifies the determinants of the empty taxi trip duration (ETTD) by combining three high-resolution databases—geolocation data in New York City, geodatabase of urban planning data, and transportation facilities data. Considering the nature of duration data, hazard-based duration model is proposed to explore the relationships between causal factors and ETTD, coupling with three variations of baseline hazard distribution, i.e., Weibull distribution with heterogeneity, Weibull distribution, and log-logistic. Furthermore, the likelihood ratio test is presented to implement comparisons of three baseline hazard distributions, as well as spatial and temporal transferability of causal factors. The results show significant complementary effects by subway system and competitive effects by city bus and bicycling system, as well as significant impacts of trip length, airport trip, average annual income, and employment rate. Urban built environment, for instance, density of road, public facilities, and recreational sites and ratio of green space, has various impacts on ETTD. The elasticity estimations confirm significant spatial and temporal heterogeneity in impacts on ETTD. In addition, the analysis on elasticity also reveals the considerable impacts of severe traffic congestion on ETTD within Manhattan. The modeling can assist stakeholders in understanding empty taxi movements and measuring taxi system efficiency in urban areas.     

Techno-economic study of LNG diesel power (dual fuel) ship

This article mainly proposed three technically effective alternatives to comply with the emission control regulations and laws in shipping. Liquefied natural gas (LNG)-diesel dual fuel power technology was introduced through feasibility study on several aspects including research development, retrofitting methods, vessel type, safety issues, and other technical characteristics. Based on sample ship and route, economic evaluation was conducted on these three alternatives. Cost-effectiveness of each project was detailed in the calculation of net present value (NPV) and payback time via discount cash flow method. The findings show that LNG-diesel dual fuel power technology performs best among three alternatives. Due to the impact of fuel price, two scenarios were carried out in sensitivity analysis which witnessed a variation of NPV with the fluctuation of fuel price. Further study shows the turning point between project (i) and project (iii) with different discount rate and the interaction between discount rate and fuel price, left project (ii) the least cost-effective solution in three alternatives.     

Reliability Modeling and Maintenance Policy Optimization for Deteriorating System Under Random Shock

Performance degradation and random shock are commonly regarded as two dependent competing risks for system failures. One method based on effective service age is proposed to jointly model the cumulative effect of random shock and system degradation, and the reliability model of degradation system under Nonhomogeneous Poisson processes (NHPP) shocks is derived. Under the assumption that preventive maintenance (PM) is imperfective and the corrective maintenance (CM) is minimal repair, one maintenance policy which combines PM and CM is presented. Moreover, the two decision variables, PM interval and the number of PMs before replacement, are determined by a multi-objective maintenance optimization method which simultaneously maximizes the system availability and minimizes the system long-run expect cost rate. Finally, the performance of the proposed maintenance optimization policy is demonstrated via a numerical example.