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(1) Background: Identifying early pancreas parenchymal changes remains a challenging radiologic diagnostic task. In this study, we hypothesized that applying artificial intelligence (AI) to contrast-enhanced ultrasound (CEUS) along with measurement of Heat Shock Protein (HSP)-70 levels could improve detection of early pancreatic necrosis in acute pancreatitis. (2) Methods: Acute pancreatitis $(n=146)$ and age- and sex matched healthy controls $(n=50)$ were enrolled in the study. The severity of acute pancreatitis was determined according to the revised Atlanta classification. The selected severe acute pancreatitis (AP) patient and an age/sex-matched healthy control were analysed for the algorithm initiation. Peripheral blood samples from the pancreatitis patient were collected on admission and HSP-70 levels were measured using ELISA. A CEUS device acquired multiple mechanical index contrast-specific mode images. Manual contour selection of the two-dimensional (2D) spatial region of interest (ROI) followed by calculations of the set of quantitative parameters. Image processing calculations and extraction of quantitative parameters from the CEUS diagnostic images were performed using algorithms implemented in the MATLAB software. (3) Results: Serum HSP-70 levels were 100.246 ng/ml (mean 76.4 ng/ml) at the time of the acute pancreatitis diagnosis. The CEUS Peek value was higher (155.5) and the mean transit time was longer (40.1 s) for healthy pancreas than in parenchyma affected by necrosis (46.5 and 34.6 s, respectively). (4) Conclusions: The extracted quantitative parameters and HSP-70 biochemical changes are suitable to be used further for AI-based classification of pancreas pathology cases and automatic estimation of pancreatic necrosis in AP.

A development of algorithms and writing of programs are considered as closely related but not identical parts of computer programming. Some differences between them are important for learning of computer programming, in particular, in distance learning. These differences are identified and discussed from the pedagogical point of view. The arguments for the selection of pedagogical based and cost-effective delivery modes in the case of distance learning are investigated. Practical examples supporting theoretical arguments are given on the activities of Lithuanian schools.

The existing decomposition technology of cooperative developments of multidiscipline technical complexes (MTC) don't provide global optimality die to the imposiolity of solving the problem of developing principles of local project solutions made by a dreat number of specialists of different branches of science. This problem is supposed to be solved by means of controlling of real-time of MTC space structural-parametric synthesis in terms of hierarchically organized variety of assumed scheme-structural and technological solutions. The basis algorithm: 1) realization method for a variety of possible structural organizations of a complex technical system in the form of a network analyzer; 2) method combining synthesis combinatorial operations and parametric operations in search for short routes of the developed network analyzer.

An algorithm testing methodology used in the programming teaching process is presented. Turbo Pascal system is used. Due to systematic use of some features of this system a pseudo-direct execution of algorithms expressed by Pascal procedures and functions may be ensured. The need to use data read and write statements is excluded. This enables a novice learner to concentrate himself on the main actions of an algorithm.

The role of an interface between an algorithm and its environment for school students is discussed. The motivation of the use of Pascal functions and procedures instead of commonly used programs in the first lessons of programming is presented. The main features of an interpreter of algorithms (Pascal functions and procedures) implemented on school computer are presented.