Sustainability has become an important issue in container terminal operations. However, relatively little research has been conducted to assess its association with sustainable supply chain management. In this study, sustainable supply chain management consists of internal sustainability practices and external sustainability collaboration. We empirically examined the effects of internal sustainability practices and external sustainability collaboration on sustainability performance in container terminal operations at the Port of Kaohsiung in Taiwan. We developed a model adopting the sustainable supply chain management construct, which consisted of two dimensions: internal sustainability practices and external sustainability collaboration. Several research hypotheses were formulated from the theory and the hypotheses were tested using survey data collected from 141 employees who worked with container terminals. We found that internal sustainability practices and external sustainability collaboration positively affected sustainability performance, whereas external sustainability collaboration had a positive influence on internal sustainability practices. There is a discussion of the implications of these findings for developing sustainability and improving sustainability performance in container terminals and ports.

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Programming robots by human demonstration is an intuitive approach, especially by gestures. Because robot pick-and-place tasks are widely used in industrial factories, this paper proposes a framework to learn robot pick-and-place tasks by understanding human hand gestures. The proposed framework is composed of the module of gesture recognition and the module of robot behaviour control. For the module of gesture recognition, transport empty (TE), transport loaded (TL), grasp (G), and release (RL) from Gilbreth's therbligs are the hand gestures to be recognized. A convolution neural network (CNN) is adopted to recognize these gestures from a camera image. To achieve the robust performance, the skin model by a Gaussian mixture model (GMM) is used to filter out non-skin colours of an image, and the calibration of position and orientation is applied to obtain the neutral hand pose before the training and testing of the CNN. For the module of robot behaviour control, the corresponding robot motion primitives to TE, TL, G, and RL, respectively, are implemented in the robot. To manage the primitives in the robot system, a behaviour-based programming platform based on the Extensible Agent Behavior Specification Language (XABSL) is adopted. Because the XABSL provides the flexibility and re-usability of the robot primitives, the hand motion sequence from the module of gesture recognition can be easily used in the XABSL programming platform to implement the robot pick-and-place tasks. The experimental evaluation of seven subjects performing seven hand gestures showed that the average recognition rate was 95.96%. Moreover, by the XABSL programming platform, the experiment showed the cube-stacking task was easily programmed by human demonstration.

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In this study, a fail-stop group signature scheme (FSGSS) that combines the features of group and fail-stop signatures to enhance the security level of the original group signature is proposed. Assuming that FSGSS encounters an attack by a hacker armed with a supercomputer, this scheme can prove that the digital signature is forged. Based on the aforementioned objectives, this study proposes three lemmas and proves that they are indeed feasible. First, how does a recipient of a digitally signed document verify the authenticity of the signature? Second, when a digitally signed document is under dispute, how can the group’s manager determine the identity of the original group member who signed the document, if necessary, for an investigation? Third, how can one prove that the signature is indeed forged following an external attack from a supercomputer? Following an attack, the signature could be proved to be forged without exposing the key. In addition, the ultimate goal of the group fail-stop signature scheme is to stop using the same key immediately after the discovery of a forgery attack; this would prevent the attack from being repeated.