The increase of ship’s energy utilization efficiency and the reduction of greenhouse gas emissions have been high lightened in recent years and have become an increasingly important subject for ship designers and owners. The International Maritime Organization (IMO) is seeking measures to reduce the CO2emissions from ships, and their proposed energy efficiency design index (EEDI) and energy efficiency operational indicator (EEOI) aim at ensuring that future vessels will be more efficient. Waste heat recovery can be employed not only to improve energy utilization efficiency but also to reduce greenhouse gas emissions. In this paper, a typical conceptual large container ship employing a low speed marine diesel engine as the main propulsion machinery is introduced and three possible types of waste heat recovery systems are designed. To calculate the EEDI and EEOI of the given large container ship, two software packages are developed. From the viewpoint of operation and maintenance, lowering the ship speed and improving container load rate can greatly reduce EEOI and further reduce total fuel consumption. Although the large container ship itself can reach the IMO requirements of EEDI at the first stage with a reduction factor 10% under the reference line value, the proposed waste heat recovery systems can improve the ship EEDI reduction factor to 20% under the reference line value.
Speaker‘s emotional states are recognized from speech signal with Additive white Gaussian noise (AWGN). The influence of white noise on a typical emotion recogniztion system is studied. The emotion classifier is implemented with Gaussian mixture model (GMM). A Chinese speech emotion database is used for training and testing, which includes nine emotion classes (e.g. happiness, sadness, anger, surprise, fear, anxiety, hesitation, confidence and neutral state). Two speech enhancement algorithms are introduced for improved emotion classification. In the experiments, the Gaussian mixture model is trained on the clean speech data, while tested under AWGN with various signal to noise ratios (SNRs). The emotion class model and the dimension space model are both adopted for the evaluation of the emotion recognition system. Regarding the emotion class model, the nine emotion classes are classified. Considering the dimension space model, the arousal dimension and the valence dimension are classified into positive regions or negative regions. The experimental results show that the speech enhancement algorithms constantly improve the performance of our emotion recognition system under various SNRs, and the positive emotions are more likely to be miss-classified as negative emotions under white noise environment.
Sapelovirus A (SV-A) is a positive-sense single-stranded RNA virus which is associated with acute diarrhea, pneumonia and reproductive disorders. The virus capsid is composed of four proteins, and the functions of the structural proteins are unclear. In this study, we expressed SV-A structural protein VP1 and studied its antigenicity and immunogenicity. SDS-PAGE analysis revealed that the target gene was expressed at high levels at 0.6 mM concentration of IPTG for 24 h. The mouse polyclonal antibody against SV-A VP1 protein was produced and reached a high antiserum titer (1: 2,048,000). Immunized mice sera with the recombinant SV-A VP1 protein showed specific recognition of purified VP1 protein by western blot assay and could recognize native SV-A VP1 protein in PK-15 cells infected with SV-A by indirect immunofluorescence assay. The successfully purified recombinant protein was able to preserve its antigenic determinants and the generated mouse anti-SV-A VP1 antibodies could recognize native SV-A, which may have the potential to be used to detect SV-A infection in pigs.