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The purpose of this paper is to measure the financial efficiency and productivity of 23 public universities founded in 1992 in Turkey over the period between 2004 and 2013. The results obtained will provide managerial information and act as a guide to public universities’ administrations, in using their resources more effectively.

Data envelopment analysis is applied to assess the relative financial efficiency of these universities, while Malmquist total factor productivity index is used to measure the total factor productivity change concerning financial inputs of the universities.

The number of financially efficient universities and the number of universities showing an increase in their productivity according to their financial inputs change annually and both of them display a rough trend over the years. A decrease of about 5 percent in the financial productivity of the universities is observed which stems from a technological recession. Therefore, public universities in Turkey are not able to develop effective policies to diversify, increase and use their financial resources.

When the lack of studies within the literature measuring the financial efficiency of higher education institutions is taken into account, this study can fill a gap in this area. The analyses conducted here distinguish from existing studies on this subject with regards to the extent and diversity of financial data set and the measurement of both efficiency and productivity change of universities considering financial inputs concurrently.

Coronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction.

In this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results.

The main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task.

The novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector.

In dark tourism, professional tourist guides are the primary intermediaries interacting with travelers. Guides provide them with an immersive and educational experience by drawing on many different fields such as history, geography, and literature in their narratives. They use a variety of sources in these fields to enrich their narratives, and literary works are one such source that can be particularly valuable. By drawing on the insights and inspiration from literary works, tourist guides can create engaging and memorable narratives that deepen visitors’ understanding of the local culture and heritage. The battlefield is one area where such enriched narratives are essential. Wars that have occurred in locations with both cultural and historical significance are well documented in national and international tourism literature. The way these battlefields are shown is novel. From this perspective, this study aims to: firstly, investigate battlefields that have not received sufficient attention, utilizing novels as underexplored literary sources; secondly, to analyze these sources through geo-literary reading, a relatively novel approach. The development of tour routes in five provinces in Turkey (Ankara, Eskişehir, Bilecik, Kütahya, and Afyon), where significant battles occurred during the national struggle over a period of four years, aims to provide travel companies with innovative tour programs for the global market and to facilitate the planning of specialized battlefield training for guides in these regions.