Categorization of Destinations Based on Tourists’ Emotional Responses

It is important for destination marketers to understand tourists’ emotional reactions to tourism experiences in order to initiate successful marketing efforts. Therefore, this study aimed to categorize tourist destinations based on tourists’ emotional responses, by utilizing a circumplex model of emotion, which describes emotional responses to environments along two main dimensions—pleasure and arousal. A self-administered questionnaire was used to collect data from sixteen Japanese destinations. The emotional evaluation of the pleasure and arousal dimensions for each destination was plotted on a two-dimensional grid. The results suggest that the majority of destinations that received a positive evaluation were perceived as exciting (high pleasure and arousal), rather than relaxing (high pleasure and low arousal), by tourists

Tourists’ novelty and familiarity: Their effects on satisfaction and destination loyalty

Novelty and familiarity play a important role in tourists’ perception, and these have been treated as opposite concepts for a long period. However, in recent cognitive neuroscience literature, it is suggested that novelty and familiarity are distinct concepts, which independently influence consumer behavior (Shimojo, 2008). On this basis, this study aimed to examine the difference between the effects of novelty and familiarity on satisfaction and destination loyalty. The results reflected their different roles. Both novelty and familiarity contribute to destination loyalty. Meanwhile, only novelty has an effect on the formation of satisfaction. It can be inferred that tourists want to experience new things in familiar destinations. Managerial implications are also discussed briefly

Flume experiments in the development of crevasse-splay deposits: transition from asymmetric-to-symmetric geometry

Crevasse-splay deposits play an important role in the reconstruction of the magnitude of past flood events and in understanding the behavior of river systems. Despite the extensive studies conducted on the geometry and facies of crevasse-splay deposits, their spatiotemporal developmental processes have remained insufficiently understood. In this study, scaled flume experiments were conducted to study the relationship between the developmental processes of crevasse splays and their characteristics. An experimental flume was set up in a tank to simulate the 2019 Chikuma River flood, Central Japan event. To model the overbank flow, an opening was created on the side of the flume’s wall through which the flow flooded onto a horizontal acrylic plate. The sediment used in the experiments consisted of particles with grain sizes of approximately 0.3 and 0.1 mm, which were determined to be equivalent to bedload gravel and suspended sand in a real-scale river using dimensional analysis. The results of the experi ments revealed three important findings: (1) Crevasse-splay deposits initially developed an asymmetric shape extending downstream of the main river channel but gradually showed a symmetric geometry. The river mainstream initially influenced the direction of the inundation flow, but channel bifurcations after the deposition of the sediment piles later changed the geometry of splays into a more symmetric shape. (2) Crevasse-splay deposits developed in two distinct regions (proximal and distal splay), corresponding to sediment transport by bedload and suspended load, respectively. These two regions are commonly observed in the actual field scale. (3) The original overbank flow was a sheet flow without channels, which caused coarse-grained sediments to be spread over a wide area. Subsequently, the accumulation of coarse sands in the developed channel interiors resulted in the buildup of finer-grained sediments upstream of the proximal splay. Thus, the proximal splay deposits became slightly coarse downstream, whereas they rapidly became fine at the boundary with the distal splay. These findings indicate that the characteristics of crevasse-splay deposits vary with the landform’s development stage, thus providing a basis for interpreting their depositional facies

Deep Learning-Based Average Consensus

In this study, we analyzed the problem of accelerating the linear average consensus algorithm for complex networks. We propose a data-driven approach to tuning the weights of temporal (i.e., time-varying) networks using deep learning techniques. Given a finite-time window, the proposed approach first unfolds the linear average consensus protocol to obtain a feedforward signal-flow graph, which is regarded as a neural network. The edge weights of the obtained neural network are then trained using standard deep learning techniques to minimize consensus error over a given finite-time window. Through this training process, we obtain a set of optimized time-varying weights, which yield faster consensus for a complex network. We also demonstrate that the proposed approach can be extended for infinite-time window problems. Numerical experiments revealed that our approach can achieve a significantly smaller consensus error compared to baseline strategies

Additional kernel observer: privilege escalation attack prevention mechanism focusing on system call privilege changes

Cyberattacks, especially attacks that exploit operating system vulnerabilities, have been increasing in recent years. In particular, if administrator privileges are acquired by an attacker through a privilege escalation attack, the attacker can operate the entire system and cause serious damage. In this paper, we propose an additional kernel observer (AKO) that prevents privilege escalation attacks that exploit operating system vulnerabilities. We focus on the fact that a process privilege can be changed only by specific system calls. AKO monitors privilege information changes during system call processing. If AKO detects a privilege change after system call processing, whereby the invoked system call does not originally change the process privilege, AKO regards the change as a privilege escalation attack and applies countermeasures against it. AKO can therefore prevent privilege escalation attacks. Introducing the proposed method in advance can prevent this type of attack by changing any process privilege that was not originally changed in a system call, regardless of the vulnerability type. In this paper, we describe the design and implementation of AKO for Linux x86 64-bit. Moreover, we show that AKO can be expanded to prevent the falsification of various data in the kernel space. Then, we present an expansion example that prevents the invalidation of Security-Enhanced Linux. Finally, our evaluation results show that AKO is effective against privilege escalation attacks, while maintaining low overhead