The International Maritime Organization along with couple European countries (Paris MoU) has introduced in 1982 the port state control (PSC) inspections of vessels in national ports to evaluate their compliance with safety and security regulations. This study discusses how the PSC data share common characteristics with Big Data fundamental theories, and by interpreting them as Big Data, we could enjoy their governance and transparency as a Big Data challenge to gain value from their use. Thus, from the scope of Big Data, PSC should exhibit volume, velocity, variety, value, and complexity to support in the best possible way both officers ashore and on board to maintain the vessel in the best possible conditions for sailing. For the above purpose, this paper employs Big Data theories broadly used within the academic and business environment on datasets characteristics and how to access the value from Big Data and Analytics. The research concludes that PSC data provide valid information to the shipping industry. However, the lack of PSC data ability to present the complete picture of PSC regimes and ports challenges the maritime community’s attempts for a safer and more sustainable industry.

In deception research, little consideration is given to how the framing of the question might impact the decision-making process used to reach a veracity judgment. People use terms such as “sure” to describe their uncertainty about an event (i.e., aleatory) and terms such as “chance” to describe their uncertainty about the world (i.e., epistemic). Presently, the effect of such uncertainty framing on veracity judgments was considered. By manipulating the veracity question wording the effect of uncertainty framing on deception detection was measured. The data show no difference in veracity judgments between the two uncertainty framing conditions, suggesting that these may operate on a robust and invariant cognitive process.

Replication studies have become an emerging line of research in recent decades, including in computer-assisted language learning (CALL). Exact replication, which closely follows a study’s protocol, is rare as it is hard to recreate results without establishing a highly controlled environment. However, using data available online, we were able to conduct an exact replication of Łodzikowski’s (2021) study, which reported on the use of an allophonic transcription tool by 55 Polish learners of English. Allophonic features are used by native speakers to produce acoustic variants of the same phoneme. The original study offered learners an allophonic transcription tool, examined how they used it and considered its association with phonological awareness. This study extended the original research by addressing the limitations of its regression and transcription analyses. Our findings allowed us to offer several suggestions on (1) how an allophonic transcription tool can be better designed to help learners, (2) how CALL researchers can acquire more data for more useful research and (3) why more replication studies are needed in CALL.

Machines and mechanisms realize processes, from the shaping process of a milling machine and the motion process of an automotive system to the trajectory realization of a robot. The dynamics of a machine generated by a properly chosen set of constraints in combination with an appropriate drive system is designed to meet the prescribed requirements of the process, which is done by projecting the machine equations of motion on the process dynamics. We get a set of nonlinear relations, which represent the machine motion in terms of the required process motion. A well-known example is the projection of arbitrary many robot degrees of freedom (DOFs) on a given path resulting in a set of nonlinear equations with one DOF only, the path coordinate s. Application of this idea can be used to construct a mobility space $(\ddot{s}, \dot{s}, s)$ for any combination of coordinates and constraints. The paper presents a corresponding approach for n-link robots by applying multibody system theory. Method might be interesting for layout of machines and mechanisms. Practical aspects are discussed, and an example is given.

This meta-analytic study explores the overall effectiveness of automatic speech recognition (ASR) on ESL/EFL student pronunciation performance. Data with 15 studies representing 38 effect sizes found from 2008 to 2021 were meta-analyzed. The findings of the meta-analysis indicated that ASR has a medium overall effect size (g = 0.69). Results from moderator analyses suggest that (1) ASR with explicit corrective feedback is largely effective, while ASR with indirect feedback (e.g. ASR dictation) is moderately effective; (2) ASR has a large effect on segmental pronunciation but a small effect on suprasegmental pronunciation; (3) medium to long treatment duration of ASR results in higher learning outcomes, but short duration offers no differential effect compared to a non-ASR condition; (4) practicing pronunciation with peers in an ASR condition produces a large effect, but the effect is small when practicing alone; (5) ASR is largely effective for adult (i.e. 18 years old and above) and intermediate English learners. Overall, ASR is a beneficial application and is recommended for assisting L2 student pronunciation development.