CRM-система как инструмент повышения эффективности деятельности компании

Объектом исследования является ООО "СМЕТ". Предметом исследования является управление взаимоотношениями с клиентами. Цель работы – рассмотрение CRM-систем как инструмент повышения эффективности бизнеса. Актуальность работы: в новых условиях компаниям продавать свои товары и услуги и удерживать клиентов стало намного сложнее. Клиенты стали тщательнее "считать деньги", торговаться и экономить. Покупательский спрос смещается в сторону более дешевых предложений, клиенты отказываются от всего дополнительного и необязательного, сопутствующих товаров. Усиливается конкуренция на рынках внутри страны.The object of this study is LLC "ESTIMATES". The subject of this study is customer relationship management. Purpose - consideration of CRM-systems as a tool to improve business performance. Relevance of the work: under the new conditions the companies sell their products and services and hold the customer has become much more difficult. Customers have become more thoroughly "counting money" to trade and save. Consumer demand shifts toward lower-cost offerings, customers abandon all the extra and optional, related products. There is a growing competition in the domestic markets and with foreign companies

Using Research and Planning to Develop Community Outreach: A Case Study in Helping Clientele Cope with Stress

Extension educators collaborated with local agencies to conduct a survey on sources of stress in the lives of local residents. Results reveal that five variables emerge as statistically significant factors associated with reported stress levels: financial problems, stress on the job, having too little time, number of major life changes in the past year, and being a woman. Educators followed with a Vision to Action Program that identified specific goals aimed at helping community residents cope with and reduce stress levels. Combining applied research with existing Extension programming is an effective way to engage the public on issues of local concern

Robust registration procedures for endoscopic imaging

Abstract This paper presents a robust algorithm for calibration and system registration of endoscopic imaging devices. The system registration allows us to map accurately each point in the world coordinate system into the endoscope image and vice versa to obtain the world line of sight for each image pixel. The key point of our system is a robust linear algorithm based on singular value decomposition (SVD) for estimating simultaneously two unknown coordinate transformations. We show that our algorithm is superior in terms of robustness and computing efficiency to iterative procedures based on Levenberg-Marquardt optimization or on quaternion approaches. The algorithm does not require the calibration pattern to be tracked. Experimental results and simulations verify the robustness and usefulness of our approach. They give an accuracy of less than 0.7 mm and a success rate >99%. We apply the calibrated endoscope to the neurosurgical relevant case of red out, where in spite of the complete loss of vision the surgeon gets visual aids in the endoscope image at the actual position, allowing him/her to manoeuvre a coagulation fibre into the right position. Finally we outline how our registration algorithm can be used also for standard registration applications (establish the mapping between two sets of points). We propose our algorithm as a linear, non-iterative algorithm also for projective transformations and for 2D-3D-mappings. Thus it can be seen as a generalization of the well-known Umeyama registration algorithm

SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning

Real-world optimization problems often have expensive objective functions in terms of cost and time. It is desirable to find near-optimal solutions with very few function evaluations. Surrogate-assisted optimizers tend to reduce the required number of function evaluations by replacing the real function with an efficient mathematical model built on few evaluated points. Problems with a high condition number are a challenge for many surrogate-assisted optimizers including SACOBRA. To address such problems we propose a new online whitening operating in the black-box optimization paradigm. We show on a set of high-conditioning functions that online whitening tackles SACOBRA's early stagnation issue and reduces the optimization error by a factor between 10 to 1e12 as compared to the plain SACOBRA, though it imposes many extra function evaluations. Covariance matrix adaptation evolution strategy (CMA-ES) has for very high numbers of function evaluations even lower errors, whereas SACOBRA performs better in the expensive setting (less than 1e03 function evaluations). If we count all parallelizable function evaluations (population evaluation in CMA-ES, online whitening in our approach) as one iteration, then both algorithms have comparable strength even on the long run. This holds for problems with dimension D Algorithms and the Foundations of Software technolog

Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks

Real-world anomaly detection for time series is still a challenging task. This is especially true for periodic or quasi-periodic time series since automated approaches have to learn long-term correlations before they are able to detect anomalies. Electrocardiography (ECG) time series, a prominent real-world example of quasi-periodic signals, are investigated in this work. Anomaly detection algorithms often have the additional goal to identify anomalies in an unsupervised manner. In this paper we present an unsupervised time series anomaly detection algorithm. It learns with recurrent Long Short-Term Memory (LSTM) networks to predict the normal time series behavior. The prediction error on several prediction horizons is used to build a statistical model of normal behavior. We propose new methods that are essential for a successful model-building process and for a high signal-to-noise-ratio. We apply our method to the well-known MIT-BIH ECG data set and present first results. We obtain a good recall of anomalies while having a very low false alarm rate (FPR) in a fully unsupervised procedure. We compare also with other anomaly detectors (NuPic, ADVec) from the state-of-the-art.Algorithms and the Foundations of Software technolog

Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control

Algorithms and the Foundations of Software technolog

Temporal convolutional autoencoder for unsupervised anomaly detection in time series

Algorithms and the Foundations of Software technolog

Circulating endothelial cells as biomarker for cardiovascular diseases.

Background: Endothelial dysfunction is involved in several cardiovascular diseases. Elevated levels of circulating endothelial cells (CECs) and low levels of endothelial progenitor cells (EPCs) have been described in different cardiovascular conditions, suggesting their potential use as diagnostic biomarkers for endothelial dysfunction. Compared to typical peripheral blood leukocyte subsets, CECs and EPCs occur at very low frequency. The reliable identification and characterization of CECs and EPCs is a prerequisite for their clinical use, however, a validated method to this purpose is still missing but a key for rare cell events. Objectives: To establish a validated flow cytometric procedure in order to quantify CECs and EPCs in human whole blood. Methods: In the establishment phase, the assay sensitivity, robustness, and the sample storage conditions were optimized as prerequisite for clinical use. In a second phase, CECs and EPCs were analyzed in heart failure with preserved (HFpEF) and reduced (HFrEF) ejection fraction, in arterial hypertension (aHT), and in diabetic nephropathy (DN) in comparison to age-matched healthy controls. Results: The quantification procedure for CECs and EPCs showed high sensitivity and reproducibility. CEC values resulted significantly increased in patients with DN and HFpEF in comparison to healthy controls. CEC quantification showed a diagnostic sensitivity of 90% and a sensitivity of 68.0%, 70.4%, and 66.7% for DN, HFpEF, and aHT, respectively. Conclusion: A robust and precise assay to quantify CECs and EPCs in pre-clinical and clinical studies has been established. CEC counts resulted to be a good diagnostic biomarker for DN and HFpEF

Q2Q^2 Independence of QF2/F1QF_2/F_1, Poincare Invariance and the Non-Conservation of Helicity

A relativistic constituent quark model is found to reproduce the recent data regarding the ratio of proton form factors, $F_2(Q^2)/F_1(Q^2)$. We show that imposing Poincare invariance leads to substantial violation of the helicity conservation rule, as well as an analytic result that the ratio $F_2(Q^2)/F_1(Q^2)\sim 1/Q$ for intermediate values of $Q^2$.Comment: 13 pages, 7 figures, to be submitted to Phys. Rev. C typos corrected, references added, 1 new figure to show very high Q^2 behavio