Rapid first-stage tests of on-ship infection

Protection against on-ship infectious disease—whether due to mishap or to harmful purpose—faces special situational problems. Sometimes, when infection levels on board have reached threshold levels, emergency actions are required. Often, the most thorough strategies for responding to threat are not feasible. A rapid first-stage test (RFT) is a fast, minimally invasive procedure used to rule out from possible infection a large percentage of an infection-threatened group. Prevention and control of on-ship infection need to combine various interconnected tactics. When timely criterion tests are not possible, the medical team must adopt fast alternative measures. The methods used to summarize protection against on-ship infectious agents included a scientific literature review and a web search. The fields of the review were maritime, health, and technology sources. Special attention was paid to material dealing with risks and threats of on-ship penetration by infectious agents, on-ship infection prevalence thresholds, and rapid diagnostic screens. The Bayes rule and the law of large numbers were applied to the analysis, for large on-ship populations, of RFT indications of crossing of an infection prevalence threshold. The increasing risk of serious on-ship infection—either accidental or purposeful—calls for a multi-layered protection approach. RFTs are a key part of the outer layer of such a defense. Well-designed and well-administered RFTs provide several advantages for defense against on-ship infection: low-cost, non-invasive, fast, and focuses on a drastically smaller number of infection possibilities.     

Brittleness analysis and important nodes discovery in large time-evolving complex networks

The brittleness analysis and important nodes detection have been a hot spot in the complex networks. How to get the overall feature of the whole network and how to find out some important nodes are requisites to solve these problems. In this paper, we adopt the trace of the adjacency matrix and the centrality of the complex networks to give a quantitative and qualitative analysis of networks being studied. Results show that the k-shell plays a more important role than the degree centrality and the betweenness in finding important nodes, and it can also be used to give direction on the immunization and maintenance of complex networks.     

Magnetic field tuning characteristics of bimodal ultrasonic motor stator

The magnetic field tuning characteristics of an ultrasonic motor (USM) stator are discussed. The stator consists of two piezoelectric ceramic transducer (PZT) plates and one sandwiched-in Terfenol-D plate. The dimensions of the stator are carefully adjusted to specifically discuss the influence of the magnetic field on the frequency difference between the longitudinal and bending modes of the stator. The frequency difference discussed in this paper is usually small and mainly caused by uneven materials, machining errors and changes in external conditions (temperature, pre-stress or load). The longitudinal and bending modes of the stator are simultaneously excited by an external electric field to generate the elliptic motion trajectories of the driving points. A direct current (DC) magnetic field is applied to decrease the difference between the two mode frequencies of the fabricated stator. In experiments, the dependences of the two mode frequencies and their difference on DC magnetic fields are all investigated. The experimental results indicate that the difference between the longitudinal and bending mode frequencies of the PZT/Terfenol-D/PZT composite stator can be tuned by changing the intensity of the external DC magnetic field.     

The cost saving potential of carsharing in a US context

Carsharing is a vehicle sharing service for those with occasional need of private transportation. Transportation planners are beginning to see great potential for carsharing in helping to create a more diversified and sustainable transport system. While it has grown quickly in the US in recent years, it is still far from the level where it can deliver significant aggregate benefits. A key element to the potential growth of carsharing is its ability to provide cost savings to those who adopt it in favor of vehicle ownership. This research seeks to quantify these potential cost savings. The costs of carsharing and vehicle ownership are compared based on actual vehicle usage patterns from a large survey of San Francisco Bay Area residents. The results of this analysis show that a significant minority of Bay Area households own a vehicle with a usage pattern that carsharing could accommodate at a lower cost. Further research is required to indentify how these cost savings translate to the adoption of carsharing.     

Certificateless message recovery signatures providing girault’s level-3 security

A digital signature with message recovery is a signature that the message itself (or partial of the message) is not required to be transmitted together with the signature. It has the advantage of small data size of communication comparing with the traditional digital signatures. In this paper, combining both advantages of the message recovery signatures and the certificateless cryptography, we propose the first certificatelss signature scheme with message recovery. The remarkable feature of our scheme is that it can achieve Girault’s Level-3 security while the conventional certificateless signature scheme only achieves Level-2 security. The security of the scheme is rigorously proved in the random oracle model based on the hardness of the k bilinear Diffie-Hellman inverse (k-BDHI) problem.     

Joint user and antenna selection for multiuser MIMO downlink with block diagonalization

User selection is necessary for multiuser multiple-input multiple-output (MIMO) downlink systems with block diagonalization (BD) due to the limited free spatial transmit dimensions. The pure user selection algorithms can be improved by performing receive antenna selection (RAS) to increase sum rate. In this paper, a joint user and antenna selection algorithm, which performs user selection for sum rate maximization in the first stage and then performs antenna selection in the second stage, is proposed. The antenna selection process alternately drops one antenna with the poorest channel quality based on maximum determinant ranking (MDR) from the users selected during the first stage and activates one antenna with the maximum norm of projected channel from the remaining users. Simulation results show that the proposed algorithm significantly outperforms the algorithm only performing user selection as well as the algorithm combining user selection with MDR receive antenna selection in terms of sum rate.     

3D human motion tracking by using interactive multiple models

Of different model-based methods in vision based human tracking, many state of the art works focus on the stochastic optimization method to search in a very high dimensional space and try to find the optimal solution according to a proper likelihood function. Seldom works perform a framework of interactive multiple models (IMM) to track a human for challenging problems, such as uncertainty of motion styles, imprecise detection of feature points and ambiguity of joint location. This paper presents a two-layer filter framework based on IMM to track human motion. First, a method of model based points location is proposed to detect key feature points automatically and the filter in the first layer is performed to estimate the undetected points. Second, multiple models of motion are learned by the prior motion data with ridge regression and the IMM algorithm is used to estimate the quaternion vectors of joints rotation. Finally, experiments using real images sequences, simulation videos and 3D voxel data demonstrate that this human tracking framework is efficient.     

Characteristics of premium transit services that affect mode choice

This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical solutions to practitioners on representing and distinguishing these characteristics in travel demand forecasting models. The principal findings were that the representation of awareness of transit services is significantly different than the underlying assumption of mode choice and forecasting models that there is perfect awareness and consideration of all modes. Furthermore, inclusion of non-traditional transit attributes and attitudes can improve mode choice models and reduce bias constants. Additional methods and analyses are necessary to bring these results into practice. The work is being conducted in two phases. This paper documents the results of Phase I, which included data collection for one case study city (Salt Lake City), research and analysis of non-traditional transit attributes in mode choice models, awareness of transit services, and recommendations for bringing these analyses into practice. Phase II will include data collection for two additional case study cities (Chicago and Charlotte) with minor modifications based on limitations identified in Phase I, additional analyses where Phase I results indicated a need, and a demonstration of the research in practice for at least one case study city.     

Assessing a maritime service website prototype in a ship bridge simulator: navigators’ experiences and perceptions of novel e-Navigation solutions

The aim of this study was to assess proof of concept and usability of a maritime service website prototype in a full-mission ship bridge simulator through Swedish mariners’ experiences and perceptions. This test was part of the European Commission’s EfficienSea2 project for e-navigation. The prototype was intended as an aid to existing standard systems and methodologies for planning, executing and monitoring voyages. The study began with 5 days of simulator trials focused on today’s standard practices. This served as a baseline to compare to subsequent 4 days of simulator trials testing the prototype. For data collection, observations, video footage, interviews, and eye tracking were used. Data analysis included breaking apart the qualitative data to capture the perceptions of the participants, and a preliminary analysis of eye-tracking data as a complement. The results suggested that the prototype could be more suitable for a route planning stage, that the participants were familiar with similar existing solutions from other manufacturers, and that the contents of the tool would be most beneficial if integrated within the Electronic Chart Display and Information Systems (ECDIS). There is a pressing need for the novel solutions to be user needs-driven, integrated with the existing technologies, and standardized across the domain, and these processes must go hand-in-hand with accounting for all involved stakeholders, procedures, regulations, and training, as this will alter the course of shipping.     

Fault diagnosis for wind turbine based on improved extreme learning machine

A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application.