This paper presents an online coverage method for the exploration of unknown oceanic terrains using multiple autonomous underwater vehicles (AUVs). Working from the concept of planar algorithm developed by Hert, this study attempts to develop an improved method. Instead of theoretical research, it focuses on the practical aspects of exploration by considering the equations of motion for AUVs that are actually used in oceanic exploration as well as on the characteristics of complex oceanic topography and other realistic variables, such as sea current. These elements are used to calculate cross track error (CTE) and path width for AUV movement. The validity of the improved algorithm for terrain coverage is first verified mathematically and then by a simulation of the real underwater environment that analyzes the path length and time taken for the coverage as well as the missed areas, which is the key element of efficiency. In order to apply the improved method to the multi-AUV operation, each AUV was assigned a covering or a scanning role by means of a dynamic role-changing mechanism. The results showed that the multi-AUV operation has an advantage over a single-AUV operation in many ways. The method proposed in this study will be useful not only for commercial applications but also for mine counter-measures (MCMs) and rapid environmental assessments (REAs) as part of naval military operations as well. We also believe that it will be ideal for use in variable oceanic environment, particularly in shallow water terrains. For the purposes of this study, we assume that the communication between AUVs is problem-free.

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Assessment of lymph node (LN) status in patients with papillary thyroid carcinoma (PTC) is often troublesome because of cervical LNs with indeterminate US (ultrasound) features. We aimed to explore whether Superb Microvascular Imaging (SMI) could be helpful for distinguishing metastasis from indeterminate LNs when combined with power Doppler US (PDUS). From 353 consecutive patients with PTC, LNs characterized as indeterminate by PDUS were evaluated by SMI to distinguish them from metastasis. Indeterminate LNs were reclassified according to the SMI, the malignancy risk of each category was assessed, and the diagnostic performance of suspicious findings on SMI was calculated. The incidence of US-indeterminate LNs was 26.9%. Eighty PDUS-indeterminate LNs (39 proven as benign, 41 proven as malignant) were reclassified into probably benign (n = 26), indeterminate (n = 20), and suspicious (n = 34) categories according to SMI, with malignancy risks of 19.2%, 20.0%, and 94.1%, respectively. After combining SMI with PDUS, 80.8% (21/26) of probably benign LNs and 94.1% (32/34) of suspicious LNs could be correctly diagnosed as benign and metastatic, respectively. The diagnostic sensitivity, specificity, and accuracy of categorizing LNs as suspicious based on SMI were 78.1%, 94.9%, and 86.3%, respectively. In conclusion, the combination of SMI with PDUS was helpful for the accurate stratification of indeterminate LNs based on US in patients with PTC.

PurposeAs the center of the fourth industrial revolution, artificial intelligence (AI) has marked its presence in various disciplines including the education field in the form of AI-powered learning applications. The purpose of this study is to build a research model capturing the relationships among use contexts, user gratification, attitude, learning performance and continuous intention to use an AI-powered English learning application.Design/methodology/approachUsing the use and gratification theory, use contexts and the belief-attitude-intention theory, this paper uses a quantitative approach based on a survey method for data collection and structural equation modeling for analysis. A total of 478 students from an international university in Guangdong, China, participated in the survey after using Liulishuo for two weeks.FindingsThe results showed that perceived use contexts affected all variables associated with gratifications-obtained and gratification-opportunities. With the exception of social integrativeness, all other gratification-based factors significantly affected attitude. The attitude in turn significantly influenced learning performance and continuous use intention.Originality/valueMobile AI-powered learning applications are at the center of research on technology-enhanced learning in the age of media and technology convergence. The study is timely and contributes to the discussion of the roles of use context and gratifications on technology users’ attitudes and behavioral intentions.