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This article analyses published prior article regarding the questions: is the study setup correct (true) and if comparable cases are evaluated for the compared methods. Futhermore the content, the pictures, the tables, the graphs and the pictures legends of the article and statistics are analysed for truth, relevance and applicability. We investigate furthermore if this article addresses all relevant questions regarding bias, the study groups, the evaluation of the outcomes and the evaluation of patient parameters such as burden of the treatment. As a result of the analysis, we state that the analysed article is misleading and it does not provide valuable information regarding decision making for other healthcare professionals in the dental implant field.

This Consensus-Document is issued by the International Implant Foundation (Munich/Germany). It describes distinct methods of placement of cortico-basal oral implants in different areas of the jaw bone and the maxillo-facial skeleton. The implants utilize the method of “osseofixation” instead of “osseointegration” for achieving primary, functional stability, hence they work according to the (AO-) principles of fracture treatment. The concept of the technology allows for immediate functional loading, just as it allows for various types of loading following orthopaedic surgery. The 16 methods and sub-methods as described and displayed here have been undergoing extensive observation and testing and they have been approved by the Board of the International Implant Foundation. All Consensus Documents of the International Implant Foundation are revised every three years. This is the 3rd version of the Consensus Document on placement of corticobasal oral implants Level of Evidence: S3 (evidence based, systematically developed consensus guideline).

Federated remote laboratory management system, REMLABNET III, (version 2016), for the integrating and management of remote experiments for starting university level and secondary schools, is presented. Its building was initiated both from the extensive use and expertise in Internet School Experimental System (ISES) and the lack of a similar system for remote experiments building for starting university level and secondary schools in Europe. The system uses new features, compared to REMLABNET II (version 2015) we intend to present: — federation of two RLMS systems Go-Lab and Remlabnet into one cooperating system sharing remote experiments, new Measureserver with advanced diagnostics of connected experiments, and log files for individual connections of clients and their evaluation.

A Abstract - work focuses on the design of the simulation embedded in remote experiment "Simple pendulum" built on the Internet School Experimental System (ISES). This platform is intended for wide educational purposes at schools and universities in order to provide the suitable measuring environment for students using conventional computing resources Informatics., Grant of the Internal Agency of UTB [IGA/2016/]; Slovak Research and Development Agency [APVV-0096-11]; Scientific Grant Agency VEGA [2/0157/12]; KEGA Agency [011TTU-4/2012, 020TTU-4/2013]

The Remote Laboratory Management System (RLMS), REMLABNET (version 2014), for the integrating and management of remote experiments for starting university level and secondary schools, is presented. Its building was initiated both from the extensive use and expertise in Internet School Experimental System (ISES), remote experiments built and the lack of a similar system for secondary schools in Europe. RLMS is built, using new components, designed for the purpose, as Measureserver, web space management, data warehouse, communication board of RLMS, etc. The communication server will provide beside connection and diagnostics services also services for the teacher's comfort as white board, IP telephony, simulation inclusion, test management and reservation management. For the sake of safety, optimal access to all experiments and economical exploitation the virtualized cloud will be used.

The Remote Laboratory Management System (RLMS), REMLABNET, for the integrating and management of remote experiments for starting university level and secondary schools, is presented. Its building was initiated both from the extensive use and expertise in Internet School Experimental System (ISES), remote experiments built and the lack of a similar system for secondary schools in Europe. RLMS is built using new components, designed for the purpose, as Measureserver, web space management, data warehouse, communication board of RLMS, etc. The communication server will provide beside connection and diagnostics services also services for the teacher's comfort as white board, IP telephony, simulation inclusion, test management and reservation management. For the sake of safety, optimal access to all experiments and economical exploitation the virtualized cloud will be used.

In spite of the fact that remote laboratories have been existing for at least three decades, virtually no attention has been devoted to the accumulated data analysis of this new means of education. The paper deals with the data analysis, gathered in the Datacentre (DTC) implemented with the Laboratory Management System (RLMS), connected in turn to remote laboratories and remote experiments. In particular, we concentrate and describe a new model of experiment data analysis, based on the principles of artificial intelligence, based on the criterion function in need. The leading idea of the model functioning is during the procedure of rig(s) recognition i.e Data weighting: Data recognition: Data preparation: Phenomenon modelling: Model and measurement data comparison: Result deployment, where the artificial intellingence is integrated with steps of Data weighting by association and regression using neuron network. Benefit of the suggested method is its speed and efficiency and thus using it for the optimization of individual remote experiments and ther efficiency.Paper may serve as an inspiring source for the development in the field of remote laboratories, but it may influence in the similar areas of data mining.