Marketing As Tool of Resource Efficiency

This paper shows the role of marketing in ensuring resource efficiency. It is found that the marketing is one of the methods of saving resources, making them effective in use. The conclusion about the need to use marketing to increase the efficiency of resource management in the organization is justified. It is suggested to use SWOT-analysis as a marketing technique for choosing a particular strategy, significant for the company in the management of resource efficiency. The forecasting of demand allows receiving evidence-based options in tendencies of change, indicators of quality, expenses and other indicators. Therefore, the system of the resource efficiency at an enterprise has to be guided by forecasting the demand and its task. Improved analysis cost methods (such as the factorial analysis, the functional and cost analysis) help to solve a problem of resource efficiency at the stage of design or production improvement. It is proved that application of the concept of social and ethic marketing promotes development of the resource efficiency program in management

The labour movement in Canterbury, 1880-1893.

The. development of industry and the associated .. problems of sweated labour within the newly developed industries are examined. The emerging awareness of the working class of the necessity for trade union organisation in order to safeguard their right~ to a reasonable standard of living and the organisation of these trade unions is discussed. The rise of confidence among trade unionists and their involvement in the Maritime Strike of 1890 emerges as a critical influence. The defeat of the unions in the strike and the subsequent move towards political representation by working men emerges during the late 1880 1s. The alliance between Liberal and Labour influences in parliament is discussed

Исследование влияния биоразлагаемых брейкеров на реологические характеристики жидкостей гидроразрыва пласта

Объектом исследования является процесс деструкции сшитого геля ГРП на основе гуаровой смолы. Цель работы – исследование влияния биоразлагаемых брейкеров на реологические характеристики жидкости ГРП. В процессе исследования были рассмотрены основные факторы, влияющие на процесс ГРП, составляющие агенты жидкостей ГРП на водной основе, характеристики биоразлагаемых полимеров, характеристики молочной кислоты, олигомер и полимер на её основе. Изучены существующие разновидности химической деструкции.The object of research is the process of destruction of a crosslinked hydraulic fracturing gel based on guar gum. The purpose of the work is to study the effect of biodegradable breakers on the rheological characteristics of hydraulic fracturing fluid. During the study, the main factors affecting the hydraulic fracturing process, the constituent agents of water-based hydraulic fracturing fluids, the characteristics of biodegradable polymers, the characteristics of lactic acid, the oligomer and the polymer based on it were examined. Existing varieties of chemical destruction have been studied

Разработка автоматизированной системы управления печи подогрева сырой нефти установки подготовки нефти

Разработка автоматизированной системы управления печи подогрева сырой нефти установки подготовки нефти, которая позволит улучшить качественные показатели сырой нефти для дальнейшей переработки в товарную нефтьDevelopment of an automated control system for the furnace for heating crude oil of an oil preparation unit, which will improve the quality indicators of crude oil for further processing into commercial oi

Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI

Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. To the best of our knowledge, this is among the first attempts to study the complex heterogeneous progression of LLD based on task-oriented and handcrafted MRI features. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks

Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model

Objectives Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD). Participants Diagnosed with LLD (N = 116) and enrolled in a prospective treatment study.DesignCross-sectional.MeasurementsStructural magnetic resonance imaging (sMRI) was used to predict five depression symptom phenotypes from the Hamilton and MADRS depression scales previously derived from factor analysis: (1) Anhedonia, (2) Suicidality, (3) Appetite, (4) Sleep Disturbance, and (5) Anxiety. Our deep learning model was deployed to predict each factor score via learning deep feature representations from 3D sMRI patches in 34 a priori regions-of-interests (ROIs). ROI-level prediction accuracy was used to identify the most discriminative brain regions associated with prediction of factor scores representing each of the five symptom phenotypes. Results Factor-level results found significant predictive models for Anxiety and Suicidality factors. ROI-level results suggest the most LLD-associated discriminative regions in predicting all five symptom factors were located in the anterior cingulate and orbital frontal cortex. Conclusions We validated the effectiveness of using deep learning approaches on sMRI for predicting depression symptom phenotypes in LLD. We were able to identify deep embedded local morphological differences in symptom phenotypes in the brains of those with LLD, which is promising for symptom-targeted treatment of LLD. Future research with machine learning models integrating multimodal imaging and clinical data can provide additional discriminative information