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This research study explores the perceptions of the importance and meaning of innovation in education by qualified teachers. The authors deliberately selected geography teachers for the research because the extraordinary dynamics of changes and innovations the teacher has to deal with are significantly reflected, particularly in geography teaching.

The main aim of the research was to determine geography teachers' views on the importance, role and meaning of innovation in teaching. The research group consisted of 12 qualified teachers, and a semistructured interview was chosen as the research method. The research was conducted over six months, from October 2020 to March 2021.

This research confirmed the interest in introducing innovations into teaching by the teachers interviewed. Teachers mainly think of innovation as new ways of teaching that aim to revive and make teaching more attractive, to increase the motivation of all actors in the learning process. While teachers with more ample teaching experience connect innovations mainly with presentations, education games, and excursions, teachers-beginners and teachers with shorter teaching experience understand innovations mainly as the application of new trends in education, such as research projects and working with GIS and digital technologies. The research confirmed that lectures supported by presentations are the most frequently used teaching method for explaining the geography curriculum in primary and secondary schools. Presentations in which teachers focus on linking relationships and explaining connections more deeply replace existing textbooks and teaching texts for most teachers interviewed.

The number and qualifications of the teachers involved in the research.

Teachers see the quality of the school environment and the education system as the significant barriers to providing better geography education. They often come to innovations through their own study and activities and feel a significant lack of available materials for the practical application of innovations in teaching. They also perceive the support for creation by state authorities and educational institutions as insufficient. Most teachers interviewed would welcome regular training courses and vocational education on the appropriate introduction and use of innovations in the classrooms in the form of practical examples and developed methodologies.

The selection of teachers for the research was deliberate and included active teachers of both genders working in primary and secondary schools. The selected teachers had varying teaching experiences and studied different combinations of teaching subjects with geography.

Drawing on social exchange theory, this study clarifies the roles of authenticity, interactivity and involvement in predicting customer engagement (CE) and, ultimately, customer loyalty towards an online peer-to-peer accommodation platform. In addition, the study explores the effect of interactivity in increasing authenticity.

Data were collected through an online questionnaire of a sample of Italian tourists who had previously booked a service on Airbnb. The analyses were conducted by adopting partial least squares structural equation modelling.

The model has high power in predicting customer loyalty to an online peer-to-peer accommodation platform. Specifically, involvement is the primary predictor of CE and customer loyalty. Authenticity and interactivity also have a significant and positive effect both on CE and customer loyalty. In addition, CE partially mediates the relationship between authenticity, interactivity and involvement and customer loyalty. Finally, interactivity has a significant positive effect on authenticity.

The results encourage hospitality service providers to invest in the creation (and co-creation) of authentic experiences to increase CE and customer loyalty. Hospitality managers can also enhance CE by increasing involvement and interaction with customers through various touchpoints (online and offline) in different moments of the customer journey.

This study proposes an original model to predict customer loyalty to peer-to-peer hospitality platforms. The findings shed new light on the drivers of CE and provide empirical support for the mediating effect of CE. The study also contributes to the literature on authenticity by demonstrating the positive effect of interactivity on authenticity.

The purpose of the present study is to identify the core cultural aspects perceived by the executives of public, private and foreign banks in India.

Of the 124 responses, 96 usable responses were assessed from middle and lower level managers. Qualitative content analysis and deconstruction method were used to identify the perceived cultural aspects.

Interestingly, managers of Indian banking industry stated that cultural aspects of their banks possess good work and working environment, prefer people, management, experience and promotions in comparison to other factors like policy, bonus, market, commitment, project, etc. It is also noted that cultural aspects of banks prefer learning, training and team working.

Assessment of the perception of managers toward their culture will foster the banks to develop integral subculture and to achieve the long-term organizational goals.

The study analyze the cultural aspects in Indian banking industry qualitatively, based on executives characteristics. This qualitative analysis helps to find out more contemporary and prevailing factors of banks.

Automatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.

Efficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.

The best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.

Most DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.