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On 6 February 2023, two significant seismic events occurred in the southeastern region of Türkiye. The seismic activity, which was perceptible in numerous countries beyond Türkiye, resulted in a considerable number of fatalities. A considerable number of individuals lost their lives and were rendered homeless as a result of the disaster. Two of the most significant factors contributing to the occurrence of these tragedies are the magnitude of the earthquake and structural deficiencies. The present study is concerned with a detailed analysis of these two factors. This study initially considers the seismicity of the region where the earthquakes that occurred on 6 February 2023 took place, as well as the seismic characteristics of these earthquakes. Subsequently, the findings of the field studies conducted in Hatay, Adıyaman, Kahramanmaraş and Malatya, the cities where the earthquakes caused the most destruction, are presented. The objective of the field study is to ascertain the collapse patterns, structural damages and the factors influencing these damages in reinforced concrete structures in the region. The primary causes of damage to structures can be attributed to several factors, including the presence of a strong beam-weak column mechanism, the soft story-weak story mechanism, the pounding effect, the short column damage, the long cantilever and overhangs, the short beam damage, the buckling damage, the torsion effect, the quality of the materials, the insufficient transverse reinforcement, the compressive failure due to over-reinforcement, the corrosion effect, the damage to reinforced concrete shear walls, the infill wall damage, and the damage caused by the soil and foundation system. These causes have been evaluated and recommendations have been formulated to prevent structural damage.

On January 24, 2020, an 6.8 magnitude ( M w ) earthquake struck the Sivrice district of Elazığ province in south-eastern Türkiye. The earthquake, which occurred in the East Anatolian Fault Zone (EAFZ), affected many structures. In particular, most of the masonry buildings close to the epicenter were severely damaged or destroyed and the earthquake resulted in 41 fatalities. This paper aims to provide information on the tectonic characteristics of the EAFZ, the seismic characteristics of the earthquake territory and the general characteristics of the main shock. Another purpose of this paper is to reveal the damage caused by the earthquake to the masonry buildings in rural areas through post-earthquake field observations. As a result of the field observations, the types of damage seen in masonry structures are poor workmanship and binder damages, in/out of the plane mechanism, damages due to the absence of lintels or insufficient lintel length, weak load‑bearing walls damage, corner damages and multi-leaf walls damages. The most important reason for the damage to the structures is the lack of engineering services and not being constructed in accordance with earthquake codes. In addition, considering the damage to the masonry buildings as a result of the field studies, some retrofitting methods are presented for strengthening existing masonry buildings with low seismic performance against future earthquakes.

Background Polysomnography (PSG) is used to define physiological sleep and different physiological sleep stages, to assess sleep quality and diagnose many types of sleep disorders such as obstructive sleep apnea. However, PSG requires not only the connection of various sensors and electrodes to the subject but also spending the night in a bed that is different from the subject's own bed. This study is designed to investigate the feasibility of automatic classification of sleep stages and obstructive apneaic epochs using only the features derived from a single-lead electrocardiography (ECG) signal. Methods For this purpose, PSG recordings (ECG included) were obtained during the night's sleep (mean duration 7 hours) of 17 subjects (5 men) with ages between 26 and 67. Based on these recordings, sleep experts performed sleep scoring for each subject. This study consisted of the following steps: (1) Visual inspection of ECG data corresponding to each 30-second epoch, and selection of epochs with relatively clean signals, (2) beat-to-beat interval (RR interval) computation using an R-peak detection algorithm, (3) feature extraction from RR interval values, and (4) classification of sleep stages (or obstructive apneaic periods) using one-versus-rest approach. The features used in the study were the median value, the difference between the 75 and 25 percentile values, and mean absolute deviations of the RR intervals computed for each epoch. The k-nearest-neighbor (kNN), quadratic discriminant analysis (QDA), and support vector machines (SVM) methods were used as the classification tools. In the testing procedure 10-fold cross-validation was employed. Results QDA and SVM performed similarly well and significantly better than kNN for both sleep stage and apneaic epoch classification studies. The classification accuracy rates were between 80 and 90% for the stages other than non-rapid-eye-movement stage 2. The accuracies were 60 or 70% for that specific stage. In five obstructive sleep apnea (OSA) patients, the accurate apneaic epoch detection rates were over 89% for QDA and SVM. Conclusion This study, in general, showed that RR-interval based classification, which requires only single-lead ECG, is feasible for sleep stage and apneaic epoch determination and can pave the road for a simple automatic classification system suitable for home-use.