Sinkhole development in the Sivas gypsum karst, Turkey

The extensive gypsum karst of Sivas, Turkey is one of the most outstanding examples of bare gypsum karst in the world. It displays a number of remarkable geomorphic features, including: (1) two stepped planation surfaces cut-across folded gypsum developed during an initial phase of slow base level deepening punctuated by periods of stability; (2) unusual deeply entrenched gypsum canyons related to a subsequent phase of rapid fluvial incision and water table lowering; (3) a polygonal karst of superlative quality mainly developed in the upper surface; (4) relict valleys disrupted by sinkholes in the lower erosional surface; (5) a large number of bedrock collapse sinkholes mostly associated with the lower surface; and (6) numerous cover subsidence sinkholes developed in the valley floors. This work analyses the spatial distribution, characteristics and evolution of the sinkholes within the broad Plio-Quaternary geomorphological and paleohydrological evolution of the epigene karst system dominated by autogenic recharge. A cartographic sinkhole inventory has been produced in an area covering 2820 km(2) with morphometric data and including 295 bedrock collapse sinkholes and 302 cover subsidence sinkholes. The different sinkhole types show a general spatial zonation controlled by the hydrogeological functioning of the different sectors: (1) solution sinkholes (polygonal karst) in the upper recharge area; (2) bedrock collapse sinkholes in the lower denudation surface and close to the base level, where well developed caves are inferred; and (3) cover subsidence sinkholes, with high densities probably associated with areas of preferred groundwater discharge. The morphology of the bedrock collapse sinkholes, varying from small cylindrical holes to large and deep tronco-conical depressions with gentle slopes reflect to geomorphic evolution of these sinkholes that reach exceptionally large hectometre-scale diameters. Their evolution, involving substantial enlargement and deepening, is attributed to the solutional removal as solute load of large volumes of gypsum by downward vadose flow. This type of morphological evolution with significant post-collapse solutional denudation differs from that observed in carbonate rocks characterised by lower solubility and erodibility. The analysis of historical imagery reveals that bedrock collapse sinkholes currently have a very low probability of occurrence and that buried cover subsidence sinkholes are used for urban development creating risk situations. (C) 2021 The Author(s). Published by Elsevier B.V

Large Language Models for Software Engineering: Survey and Open Problems

This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE). It also sets out open research challenges for the application of LLMs to technical problems faced by software engineers. LLMs' emergent properties bring novelty and creativity with applications right across the spectrum of Software Engineering activities including coding, design, requirements, repair, refactoring, performance improvement, documentation and analytics. However, these very same emergent properties also pose significant technical challenges; we need techniques that can reliably weed out incorrect solutions, such as hallucinations. Our survey reveals the pivotal role that hybrid techniques (traditional SE plus LLMs) have to play in the development and deployment of reliable, efficient and effective LLM-based SE

Pituitary Insufficiency and Hyperprolactinemia Associated with Giant Intra- and Suprasellar Carotid Artery Aneurysm

Pituitary insufficiency secondary to internal carotid artery (ICA) aneurysm is a very rare condition. Its prevalence is reported as 0.17% (Heshmati et al., 2001). We present a case of pituitary insufficiency and hyperprolactinemia secondary to suprasellar giant intracranial aneurysm. A 71-year-old man was admitted to our clinic with symptoms of hypopituitarism, hyperprolactinemia, and visual field defect. His pituitary MRI and cerebral angiography revealed a giant saccular aneurysm filling suprasellar cistern arising from the ophthalmic segment of the right ICA. Endovascular treatment was performed on the patient to decrease the mass effect of aneurysm and improve the hypophysis dysfunction. After treatment, his one-year follow-up showed the persistence of hypophysis insufficiency, decrease of prolactin (PRL) level, and normal visual field. An intracranial aneurysm can mimic the appearance and behavior of a pituitary adenoma. Intracranial aneurysms should be taken into consideration in the situation of hypopituitarism and hyperprolactinemia. It is important to distinguish them because their treatment approach is different from the others

Comparison of regression and artificial neural network models for surface roughness prediction with the cutting parameters in CNC turning

Surface roughness, an indicator of surface quality, is one of the most specified customer requirements in machining of parts. In this study, the experimental results corresponding to the effects of different insert nose radii of cutting tools (0.4, 0.8, 1.2 mm), various depth of cuts (0.75, 1.25, 1.75, 2.25, 2.75 mm), and different feedrates (100, 130, 160, 190, 220 mm/min) on the surface quality of the AISI 1030 steel workpieces have been investigated using multiple regression analysis and artificial neural networks (ANN). Regression analysis and neural network-based models used for the prediction of surface roughness were compared for various cutting conditions in turning. The data set obtained from the measurements of surface roughness was employed to and tests the neural network model. The trained neural network models were used in predicting surface roughness for cutting conditions. A comparison of neural network models with regression model was carried out. Coefficient of determination was 0.98 in multiple regression model. The scaled conjugate gradient (SCG) model with 9 neurons in hidden layer has produced absolute fraction of variance ( R2) values of 0.999 for the training data, and 0.998 for the test data. Predictive neural network model showed better predictions than various regression models for surface roughness. However, both methods can be used for the prediction of surface roughness in turning

Shear bond strength of a novel porcelain repair system for different computer-aided design/computer-assisted manufacturing ceramic materials

Objectives: The purpose of this study was to compare the shear bond strength of a novel repair system, Nova Compo SF with Ceramic Repair, Ivoclar, to computer-aided design/computer-assisted manufacturing (CAD/CAM) restorative materials (IPS e.max CAD and Empress CAD). Materials and Methods: The specimens of each CAD/CAM restorative material were randomly divided into two subgroups of nine specimens, using one of two repair systems. All specimens were etched with hydrofluoric acid and rinsed under a water spray for 10 s, then air-dried for 10 s. Next, repair systems were applied according to the manufacturer's instructions. All specimens were stored in distilled water at 37 degrees C for 24 h and then additionally aged for 5000 thermal cycles. A shear bond strength test was performed using a universal testing machine. Each fracture type was examined under a stereomicroscope at x12.5 magnification. A two-way ANOVA test was used to detect significant differences between the CAD/CAM restorative materials and the composite repair systems. Subgroup analyses were performed using Tukey's honest significant difference. Results: No statistically significant differences were observed between the repair systems (P = 0.9). The bond strength values from Empress CAD were statistically higher than those from e.max CAD (P < 0.05). Conclusions: Within limitations, SuperFlow may be an alternative to the ceramic repair materials we routinely used in the clinic. Empress CAD can be preferable to e.max CAD in terms of esthetically suitable clinical indications