Nanostructured hydroxyapatite (HAP) and three multiple substituted HAPs, containing Mg, Zn, Sr and Si were synthesized by a wet precipitation method The presence of the HAP lattice as unique crystalline phase was established by XRD and by FTIR spectroscopy. The chemical composition was confirmed by SEM-EDX. The TEM, SEM and AFM imaging showed the morphology of these biomaterials. The elements release in water and in simulated body fluid (SBF) was monitored in time from 1 to 90 days, by using inductively coupled plasma optical emission spectrometry (ICP-OES). The results are important for the future use of these hydroxyapatite biomaterials, as bone substitutes or coatings on metallic implants, able to release essential physiological elements, both in vitro and in vivo, with great impact in orthopedics and dentistry.

Despite the claimed worth and huge interest regarding the increasing volumes of complex data sets and the rewarding promise to improve research, there is, however, a growing concern regarding data privacy that affects both qualitative and quantitative higher education research. Within the contemporary debates on the impact of Big Data on the nature of higher education research and the effective ways to harmonize Big Data practice with privacy restrictions and regulations, this study sets out to qualitatively examine current issues regarding data privacy, anonymity, informed consent and confidentiality in data-centric higher education research, with a focus on the data collector, data subject and data user. We argue that within current regulations, data protection of research subjects concerns more data collection and disclosure and insufficiently describes use, having procedural implications for both the complex nature of higher education (HE) research and the type of research data being collected. We work our argument through an examination of several factors that call for a reconsideration of data privacy and access to private information in HE research. The conclusions indicate that Big Data-centric HE research is increasingly becoming a mainstream research paradigm which needs to address critical data privacy issues before being widely embraced.

Nowadays, REpresentational State Transfer Application Programming Interfaces (REST APIs) are widely used in web applications, hence a plethora of test cases are developed to validate the APIs calls. We propose a solution that automates the generation of test cases for REST APIs based on their specifications. In our approach, apart from the automatic generation of test cases, we provide an option for the user to influence the test case generation process. By adding user interaction, we aim to augment the automatic generation of APIs test cases with human testing expertise and specific context. We use the latest version of OpenAPI 3.x and a wide range of coverage metrics to analyze the functionality and performance of the generated test cases, and non-functional metrics to analyze the performance of the APIs. The experiments proved the effectiveness and practicability of our method.

This paper sets out to examine the importance and virtues of non-musical elements in operatic performance. By identifying expressive gestures in the artistic interpretation of two famous pieces, this study also explores the prevalence of the type of information (auditory or visual) that can determine the evaluation not only of an artistic interpretation but also of an entire musical performance.

Originating and striking from anywhere, cyberattacks have become ever more sophisticated in our modern society and users are forced to adopt increasingly good and vigilant practices to protect from them. Among these, ransomware remains a major cyber-attack whose major threat to end users (disrupted operations, restricted files, scrambled sensitive data, financial demands, etc.) does not particularly lie in number but in severity. In this study we explore the possibility of real-time detection of ransomware source through a linguistic analysis that examines machine translation relative to the Levenshtein Distance and may thereby provide important indications as to attacker's language of origin. Specifically, the aim of our research is to advance a metric to assist in determining whether an external ransom text is an indicator of either a human-or a machine-generated cyber-attack. Our proposed method works its argument on a set of Eastern European languages but is applicable to a large(r) range of languages and/or probabilistic patterns, being characterized by usage of limited resources and scalability properties.