Tarsus University is a public university in Turkey, Mersin. On 18 May 2018, it was established as a separate university in Tarsus district with the establishment of academic units affiliated to Mersin University and new academic units. Tarsus University provides education in 1 institute, 7 faculties and 3 vocational schools.
Most of the dam structures around the world are approaching the end of their economic life of 50 to 70 years, especially due to sediment accumulation in reservoir areas. This situation necessitates the development of proactive infrastructure management strategies. This study presents an original framework for the process of renewal of aging dams that blends remote sensing techniques and meta-intuitive optimization methods. Within the scope of the study, the Hasanlar Dam located in D & uuml;zce was selected as a sample, and a new dam axis was determined in the upper part of the basin. A detailed volume-height curve was created using 12.5 m resolution ALOS PALSAR numerical height models (DEM) and GIS-based spatial data curation to calculate the reservoir storage capacity in precise increments of 2 m. To maximize the structural efficiency of the proposed "New Hasanlar Dam", the cross-sectional area has been minimized through seven current algorithms such as Genetic Algorithm (GA), Arithmetic Optimization Algorithm (AOA), Gray Wolf Optimizer (GWO), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Crayfish Optimization Algorithm (CAO), and Cheetah Optimizer (CO). The findings obtained prove that the PSO and CAOs achieved a significant reduction in cross-sectional area by 29.36% and successfully approached the global optimum. The replacement of the 55.5 million m3 capacity of the existing Hasanlar Dam with a new structure with a height of 78 m will guarantee sustainability and structural safety in water management. As a result, this study reveals that the integration of high-resolution remote sensing data and advanced heuristic methods is a cost-effective and powerful tool in the strategic renovation of aging hydraulic infrastructures.
Throughout history, natural and man-made disasters have caused significant destruction in biological, psychological, social, and economic areas, and their complete prevention has not been possible. Disaster management is essential to reduce the devastating effects of events such as earthquakes, fires, landslides, floods, erosion, droughts, and avalanches, whose frequency is increasing. In this process, data sharing and the rapid, coordinated flow of information are crucial for post-disaster recovery. The earthquake in our country on February 6, 2023, once again demonstrated the importance of data sharing in disaster management. After the disaster, social media platforms, especially X, emerged as valuable data sources. This study aims to classify and analyze data obtained from X using machine learning algorithms and sentiment analysis in the context of disaster management. It seeks to understand the emotional responses of society during and after disasters and to support rapid response and relief operations through the effective use of this information. Considering the speed and constantly updated nature of data, various algorithms were compared for accurate and fast classification. In the data pre-processing, natural language processing techniques and sentiment analysis methods were employed to identify the emotional states of society following the disaster. The experimental results demonstrate that Random Forest, Multi-Layer Neural Networks, and GRU models achieve a 99% accuracy rate, thereby exhibiting a substantial improvement over the Naive Bayes (91%) and Extra Decision Tree (94%) approaches. In class-based evaluations, it was observed that GRU and neural network-based methods, in particular, produced high precision and recall values (0.9 and above) across all categories, demonstrating consistent performance despite the imbalanced class distribution. This study highlights the importance of operations research and industrial engineering and demonstrates the potential of data science to enhance disaster response strategies.
Preoperative surgical fear and anxiety, is known to influence postoperative pain perception. The aim of this study is to evaluate the relationship between preoperative surgical fear, surgical anxiety, and postoperative pain severity. This descriptive and correlational study included 138 adult patients undergoing elective general surgery at a tertiary care hospital. Preoperative surgical fear and anxiety were assessed one day before surgery using the Surgical Fear Questionnaire (SFQ) and the Surgical Anxiety Questionnaire (SAQ). Postoperative pain intensity was measured using the Visual Analog Scale (VAS) at predefined intervals during the first 24 postoperative hours. Correlation analyses were performed to examine associations between psychological variables and pain intensity. Patients had mean SFQ and SAQ scores of 34.6±18.1 and 40.4±12.6, respectively. SFQ was weakly but significantly correlated with postoperative pain intensity across all time intervals during the first 24 hours (r = 0.186–0.257, p< 0.05). Similarly, SAQ showed significant positive correlations with postoperative pain at all time points, with slightly stronger associations observed in the early postoperative period (r = 0.232–0.326, p < 0.01). A strong positive correlation was identified between SFQ and SAQ (r = 0.733, p< 0.001). No significant association was found between psychological variables and total analgesic consumption (p > 0.05). Preoperative surgical fear and anxiety were significantly associated with postoperative pain in general surgery patients, particularly in the early postoperative period. Routine psychological assessment may contribute to improved pain management and perioperative care outcomes.
The current energy context raises the awareness to search for renewable energy sources to lower the dependence on the fossil fuels in our energy mixes and go toward a more sustainable future. Biogas, a renewable fuel obtained from the biological breakdown of biomass, has gained a significant interest lately due to its similarity with CH4. However, because of its high CO2 content, the combustion of this renewable fuel faces a variety of instabilities such as blow-off and liftoff. In this study, the effect of CO2 addition and burner geometry on the laminar combustion of biogas is investigated. For this purpose, the range of CO2 in the fuel was varied from 0% to 70% while maintaining the flame power at 1 kW. an ICCD camera coupled with OH* filter was used to study the chemiluminescence and the effect of the mentioned parameters on the flame front and the combustion characteristics. Four different type of burners (laboratory burners, propane/butane stove, natural gas stove, and biogas stove) are used to assess the geometry effects on the biogas combustion and to determine the range of operating conditions, CO2 in particular, of every gas stove. A gas analyzer is used to measure the NOx and CO emissions. The experiments using the laboratory burners show that the increase in CO2 in the blend enhanced flame instability and increased the liftoff height while the increase in the burner's diameter helped stabilize the flame and delayed the liftoff. It was also found that the addition of CO2 increased CO and decreased NOx emissions in exhaust gases. The results of biogas combustion in different gas stoves demonstrated that they are not compatible for sustaining a stable flame of the biogas with a high content of CO2 as the blow-off started at 20% CO2 for propane/butane, at 40% for natural gas stove and at 70% CO2 for the biogas stove. The OH* chemiluminescence images show how the CO2 content changed the flame front and altered the combustion chemistry. These findings highlighted the effect CO2 proportion in the biogas mixture on flame behavior for different burners, it is challenging to use the commercial stoves the biogas.
The purpose of this study is to analyze how the CEOs of the world's five largest airline companies articulate the concept of social sustainability from a strategic leadership perspective. Due to its strong human engagement, cultural diversity, and demanding service environment, aviation stands out as a critical sector for integrating social sustainability into corporate strategy. Accordingly, a total of86 CEO discourse documents - including public speeches, press releases, and interviews dated 2021-2023 - were examined to capture leadership-driven framings of social sustainability in global aviation. A qualitative discourse analysis was conducted using MAXQDA 2020, guided by Braun and Clarke's thematic analysis framework. The findings reveal that CEOs consistently refer to equity, inclusion, employee well-being, ethical integrity, corporate citizenship, and long-term social value as core elements of strategic leadership. Strategic leadership practices were found to function not only as mechanisms of governance and coordination but also as symbolic carriers of legitimacy, social trust, and organizational resilience. This study contributes to the literature by addressing the limited scholarly attention to the social dimension of sustainability in aviation leadership discourse and by offering an empirically grounded thematic framework that links social sustainability to strategic leadership in the post-pandemic recovery phase.
