Nandor Ludvig, Translational Neuroscience Consultation, Astoria, New York, USA
This paper introduces the basic design of a novel neuroscientific device named Creativity Monitoring Device, CMD, able to provide new insights into the brain mechanisms of artistic, scientific and engineering creative acts. The novelty of the device is threefold. First, it allows the artifact-free recording of association cortical EEG waves during such creative acts as painting artistic visions, composing music, running novel experiments in a laboratory or working on an engineering invention. Second, it allows the synchronous monitoring of the sounds and visual events in the environment of such actions along with the artist’s, scientist’s or engineer’s verbal and non-verbal interactions with this environment and his or her own notes on the engaged creative phase. Third, it allows the offline analysis of the synchronously collected EEG and audio-visual data with the new method of dynamic neurocombinatorics, which can reveal at 2 msec accuracy the relationships between the recorded objects of 5 sets: (1) the spatial occurrence codes of the recorded association cortical EEG waves; (2) the complexity codes of each of the recorded 0.2 - 200 Hz EEG waves; (3) the codes of the synchronously recorded environmental events including those of the subject’s verbal and non-verbal interactions with this environment; (4) the subject’s own dictated notes identifying the phase of the engaged creative act, and (5) the time-course of these continuously recorded objects within each of the indicated sets over the entire, at least 10-hour, creativity monitoring period. Thus, CMD studies should reveal the key electrical brain changes underlying the initiation, maintenance and termination of creative acts and should show the similarities and differences between artistic, scientific and engineering creativities.
Artifact-free EEG headset, Wearable environment monitor, Dynamic Neurocombinatorics.
Ven B.Siri Sumedha, Assistant Director of Education, Ministry of Education, Sri Lanka
The evaluation reports of the Sri Lanka Examination Department show that the minimum number of marks has been obtained for the science subject. The main interest of this research was to find the correlation between performance and conceptual understanding in the fields of chemistry and science. After a pilot study, 302 students from 13 secondary schools in Kegalle District, Sri Lanka were selected as the sample. Research was conducted through correlational study research strategy according to a quantitative research design. Standardized research instruments were administered to the sample, and the collected data were analyzed and interpreted. Here the correlation between students' concept perception and student achievement was r = 0.720. It was revealed that student achievement increases as students' conceptual understanding increases. For that one should always resort to using appropriate learning methods..
Conceptual Perception, Student achievement, Chemistry, G.C.E (O/L), Science.
Safa Evirgen1 and Prof. Dr. Maylin Wartenberg2, 1Volkswagen AG, Berliner Ring 2, 38426 Wolfsburg, Germany, 2Hochschule Hannover, Recklinger Stadtweg 120,30459Wolfsburg, Germany
This paper highlights the significance of AI-powered maintenance strategies in modern industry for operational optimization and reduced downtime. It emphasizes the crucial role of sensor data analysis in identifying anomalies and predicting failures. The research specifically examines sensor data from an automotive press shop, addressing questions related to data selection, collection challenges, and knowledge generation. By utilizing unsupervised learning on compressed air data from a press line, the study identifies patterns, anomalies, and correlations. The results offer insights into the potential for implementing an effective predictive maintenance strategy. Additionally, a systematic literature review underscores the importance of data analysis in production systems, particularly in the context of maintenance.
Predictive Maintenance, smart database, sensor data analytics, data science, production.
Mahfuza Khatun1 and Sikandar Siddiqui2, 1Jahangirnagar University, Savar, 1342 Dhaka, Bangladesh, 2Deloitte Audit Analytics GmbH, Europa-Allee 91, 60486 Frankfurt, Germany
This paper introduces a density-based clustering procedure for datasets with variables of mixed type. The proposed procedure, which is closely related to the concept of shared neighbourhoods, works particularly well in cases where the individual clusters differ greatly in terms of the average pairwise distance of the associated objects. Using a number of concrete examples, it is shown that the proposed clustering algorithm succeeds in allowing the identification subgroups of objects with statistically significant distributional characteristics.
Cluster analysis, mixed data, distance measures.
