DEFINING THE ONTOLOGICAL STATUS OF LABOUR PRODUCTIVITY IN HEALTH CARE

he present research touches upon the problem of comprehension of the concept «labor productivity» as applied to the field of healthcare in comparison with other categories and the corresponding indicators, used in the assessment of the medical organization personnel activity. As methodological basis the ideas in the field of labor economics, conceptual apparatus and theoretical bases of which have proved their efficiency in the solution of the tasks of development of the organizations of different branch affiliation act. The statement that labor productivity in public health services is connected with the transfer of knowledge and technologies from industrial sphere to medicine, requiring the interpretation of concepts and conceptual apparatus in relation to the considered branch is taken as the basis. In modern conditions of high intensity of work of medical organizations at simultaneous necessity of observance of obligatory requirements to quality and safety of medical aid, rational use of resources, the demand for formation of scientifically grounded approaches to labor productivity estimation in healthcare is realized by authors by means of development of an integrated index of labor productivity estimation. The present research can be considered in the development of the theory of labor productivity as applied to the branch of health care taking into account its orientation on rendering qualitative and safe medical aid. The offered by the author approach to the estimation of the given indicator allows to reflect not only quantitative (output, labor input) or cost estimation of labor productivity, but also takes into account complexity, intensity of work of the employees, and also an estimation of a degree of achievement of productivity (quality) of their activity. Its novelty is seen in the mutual conditionality of medical, social and economic evaluation of the effectiveness of the use of labor resources of the employees of medical organizations.

Automatic Asbestos Control Using Deep Learning Based Computer Vision System

The paper discusses the results of the research and development of an innovative deep learning-based computer vision system for the fully automatic asbestos content (productivity) estimation in rock chunk (stone) veins in an open pit and within the time comparable with the work of specialists (about 10 min per one open pit processing place). The discussed system is based on the applying of instance and semantic segmentation of artificial neural networks. The Mask R-CNN-based network architecture is applied to the asbestos-containing rock chunks searching images of an open pit. The U-Net-based network architecture is applied to the segmentation of asbestos veins in the images of selected rock chunks. The designed system allows an automatic search and takes images of the asbestos rocks in an open pit in the near-infrared range (NIR) and processes the obtained images. The result of the system work is the average asbestos content (productivity) estimation for each controlled open pit. It is validated to estimate asbestos content as the graduated average ratio of the vein area value to the selected rock chunk area value, both determined by the trained neural network. For both neural network training tasks the training, validation, and test datasets are collected. The designed system demonstrates an error of about 0.4% under different weather conditions in an open pit when the asbestos content is about 1.5–4%. The obtained accuracy is sufficient to use the system as a geological service tool instead of currently applied visual-based estimations.

Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data

PurposeThis paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.Design/methodology/approachThe paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering out outliers. Finally, four algorithms, namely SVR (Support Vector Regression), XGBoost (Extreme Gradient Boosting), LSTM (Long-Short Term Memory Network) and BP (Back Propagation) Neural Network, were used for modeling and testing.FindingsThe paper provided a comprehensive forecasting framework for productivity estimation including feature selection, data processing and model evaluation. The optimal coefficient of determination (R2) of four algorithms were all above 80.0%, indicating that the features selected were representative. Finally, the BP neural network model coupled with the SVR model was selected as the final model.Originality/valueMachine-learning algorithm incorporating domain expert judgments was used to select predictive features. The final optimal coefficient of determination (R2) of the coupled model of BP neural network and SVR is 87.6%, indicating that the method proposed in this paper is effective for CSD productivity estimation.