Accurately identifying the exact boundary region of the pulmonary nodules in lung cancer images are the most challenging tasks in the Computer Aided Diagnosing schemes (CADx). Detecting the boundaries from different nodule structures is crucial due to the presence of similar visualization characteristics between the nodules and its surroundings. The study proposed an approach for pulmonary nodule region of interest (NROI) detection and segmentation using Computed Tomography (CT) lung images. Lung nodule CT images are acquired from the Lung Image Database Consortium (LIDC) public repository having 1018 cases. In this paper, a methodology for automated tumor grading of pulmonary lung nodules is proposed using Convolutional Neural Network (CNN). The salient features of benign and malignant nodules from different nodule structures are automatically self-learned and classified based on the classification strategy. The stages involved in the methodology are: 1) Pre-processing the image datasets using discrete wavelet transforms (DWT). 2) NROI segmentation. 3) NROI Feature extraction using CNN. 4) Nodule classification. CNN are trained with self-learned extracted features from NROI and are further classified as benign or malignant. Analyzing and segregating these extracted features plays a vital role in the correct classification of malignancy levels. The methodology is compared with conventional state-of-art methods and traditional hand-crafted methods. A total of 710 pulmonary nodules are used in the study, with 258 benign samples and 452 malignant samples. A consistent behavior was observed using CNN with reduced low false positives and a classification accuracy of 96.5\\%, sensitivity of 96\\%, specificity of 96.55\\% and standard Receiver operating characteristic (ROC) curve with the highest value of 0.969 was recorded.

Long-range seasonal ENSO (El Ni�o Southern Oscillation) forecasts are provided by operational dynamical and statistical models and the skills of these models are a matter of contention. In this work, new skill metrics are proposed for determining whether or not the models skills are significantly differ. Using an ENSO idealized data set, it is shown that the newly developed metrics RIP (Rotated Index Positive) and RIN (Rotated Index Negative) were capable of distinguishing between under-prediction and over-prediction whereas other popular metrics ACC (Anomaly Coefficient Correlation) and RMSE (Root Mean Squared Error) metrics failed. These metrics were also applied to perform skill assessment on the ENSO operational data set. RIP, RIN, ACC and RMSE metrics are the only metrics that successfully differentiate models skill based on ENSO phase. Dynamical models need more improvement in reducing their false alarms. The two models do not significantly differ in predicting the La Ni�a phase. It is recommended that both RIP and RIN should be used to complement ACC and RMSE in ENSO model skill assessment.

In the modern world, there is a tendency to increase the number of people of the «third» and «fourth» generation, therefore, in psychological science there is growing interest in studying their socio-psychological problems.\n\nIn order to adapt older people and overcome the feeling of loneliness, it is necessary and important to develop a system of their socio-psychological support, which will help to find new semantic supports in life, create other stereotypes of behavior, accept the fact of aging, reveal own resources for an active life, ensure conservation of mental health.\n\nIn order to intensify the life of the elderly people, according to the request of the society in Ukraine, experts propose to involve the elderly in education. Taking into account the interests of students, it is carried out in the following areas: literature and art, foreign languages, healthy lifestyle, the basics of medicine, local history, psychology, jurisprudence, religious studies, cooking, hairdressing skills. Particular attention should be paid to the significant role of computer courses, since it has become apparent to most people that it is easy to be isolated without the mastery of information technology and modern communications.\n\nIn working with elderly people, both general therapeutic approaches (creating a therapeutic environment, organizing optimal communication, developing positive congratulatory attitudes and a positive attitude to the social environment, increasing the level of mental and social activity of a person), and special psychotherapeutic (behavioral and cognitive-behavioral psychotherapy; interpersonal, rational psychotherapy, supporting individual, family psychotherapy, the use of narrative psychotechnologies and the like) are used. Special directions are actively being introduced: occupational therapy, horticultural therapy, art therapy and others.\n\nIn gerontopsychology, an innovative health-saving approach – art therapy, is effectively used. Its methods are aimed at changes in the person\'s worldview and system of his attitude to himself and the world, which promotes emotional discharge, harmonization of the inner state of the individual, restoration of the ability to find life balance, preservation of mental health, opening of creative potential and internal resources; facilitates adaptation to social life, opens opportunities for self-actualization through free expression and self-knowledge.

In this paper, we study weighted simplified regularization method for ill-posed operator equations in the finite dimensional subspaces of a Hilbert space. Using general Holder type source condition we obtain an optimal order error estimate. Adaptive parameter choice strategy proposed by Pereverzev and Schock (2005) is used for choosing the regularization parameter. We applied the proposed method to an academic example to test the validity of theoretical result.