131 research outputs found
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
ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²Π»ΠΈΡΠ½ΠΈΡ Π±ΠΈΠΎΡΠ°Π·Π»Π°Π³Π°Π΅ΠΌΡΡ Π±ΡΠ΅ΠΉΠΊΠ΅ΡΠΎΠ² Π½Π° ΡΠ΅ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ Ρ Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΠΆΠΈΠ΄ΠΊΠΎΡΡΠ΅ΠΉ Π³ΠΈΠ΄ΡΠΎΡΠ°Π·ΡΡΠ²Π° ΠΏΠ»Π°ΡΡΠ°
ΠΠ±ΡΠ΅ΠΊΡΠΎΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΡΠΎΡΠ΅ΡΡ Π΄Π΅ΡΡΡΡΠΊΡΠΈΠΈ ΡΡΠΈΡΠΎΠ³ΠΎ Π³Π΅Π»Ρ ΠΠ Π Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π³ΡΠ°ΡΠΎΠ²ΠΎΠΉ ΡΠΌΠΎΠ»Ρ. Π¦Π΅Π»Ρ ΡΠ°Π±ΠΎΡΡ β ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²Π»ΠΈΡΠ½ΠΈΡ Π±ΠΈΠΎΡΠ°Π·Π»Π°Π³Π°Π΅ΠΌΡΡ
Π±ΡΠ΅ΠΉΠΊΠ΅ΡΠΎΠ² Π½Π° ΡΠ΅ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΠΆΠΈΠ΄ΠΊΠΎΡΡΠΈ ΠΠ Π. Π ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ»ΠΈ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΡΠ°ΠΊΡΠΎΡΡ, Π²Π»ΠΈΡΡΡΠΈΠ΅ Π½Π° ΠΏΡΠΎΡΠ΅ΡΡ ΠΠ Π, ΡΠΎΡΡΠ°Π²Π»ΡΡΡΠΈΠ΅ Π°Π³Π΅Π½ΡΡ ΠΆΠΈΠ΄ΠΊΠΎΡΡΠ΅ΠΉ ΠΠ Π Π½Π° Π²ΠΎΠ΄Π½ΠΎΠΉ ΠΎΡΠ½ΠΎΠ²Π΅, Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ Π±ΠΈΠΎΡΠ°Π·Π»Π°Π³Π°Π΅ΠΌΡΡ
ΠΏΠΎΠ»ΠΈΠΌΠ΅ΡΠΎΠ², Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΠΌΠΎΠ»ΠΎΡΠ½ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡΡ, ΠΎΠ»ΠΈΠ³ΠΎΠΌΠ΅Ρ ΠΈ ΠΏΠΎΠ»ΠΈΠΌΠ΅Ρ Π½Π° Π΅Ρ ΠΎΡΠ½ΠΎΠ²Π΅. ΠΠ·ΡΡΠ΅Π½Ρ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΠ΅ ΡΠ°Π·Π½ΠΎΠ²ΠΈΠ΄Π½ΠΎΡΡΠΈ Ρ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄Π΅ΡΡΡΡΠΊΡΠΈΠΈ.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
Π‘ΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Π³Π°Π·ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ³Π»Ρ Π² ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΠΉ ΡΠ½Π΅ΡΠ³Π΅ΡΠΈΠΊΠ΅
Π Π°Π·ΡΠ°Π±ΠΎΡΠΊΠ° Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΠ΅ΡΠΈ ΠΏΠΎΠ΄ΠΎΠ³ΡΠ΅Π²Π° ΡΡΡΠΎΠΉ Π½Π΅ΡΡΠΈ ΡΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠΈ Π½Π΅ΡΡΠΈ
Π Π°Π·ΡΠ°Π±ΠΎΡΠΊΠ° Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΠ΅ΡΠΈ ΠΏΠΎΠ΄ΠΎΠ³ΡΠ΅Π²Π° ΡΡΡΠΎΠΉ Π½Π΅ΡΡΠΈ ΡΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠΈ Π½Π΅ΡΡΠΈ, ΠΊΠΎΡΠΎΡΠ°Ρ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΡ ΡΠ»ΡΡΡΠΈΡΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΡΡΡΠΎΠΉ Π½Π΅ΡΡΠΈ Π΄Π»Ρ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅ΠΉ ΠΏΠ΅ΡΠ΅ΡΠ°Π±ΠΎΡΠΊΠΈ Π² ΡΠΎΠ²Π°ΡΠ½ΡΡ Π½Π΅ΡΡΡDevelopment 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
ΠΠ»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²Π½ΡΠ΅ ΠΊΠ°ΡΠ°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ ΠΊΡΠ΅ΠΌΠ½ΠΈΠΉΠΎΡΠ³Π°Π½ΠΈΡΠ΅ΡΠΊΠΈΡ ΡΡΠΈΠ²Π°ΡΡΠΈΡ Π°Π³Π΅Π½ΡΠΎΠ² Π΄Π»Ρ ΠΏΠΎΠ»ΠΈΠΎΠ»Π΅ΡΠΈΠ½ΠΎΠ²ΡΡ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΉ, ΡΡΡΠΎΠΉΡΠΈΠ²ΡΡ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ Π΄ΠΎΠ±ΡΡΠΈ Π½Π΅ΡΡΠΈ
ΠΠ»Π΅ΠΊΡΡΠΎΠΏΡΠΈΠ²ΠΎΠ΄ Π·Π°Π΄Π²ΠΈΠΆΠΊΠΈ ΠΌΠ°Π³ΠΈΡΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π½Π΅ΡΡΠ΅ΠΏΡΠΎΠ²ΠΎΠ΄Π°
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