The interaction of subsonic plasma flow with a neutral particle cloud via charge exchange is considered. We derive a general expression for the velocity potential of the plasma flow induced by charge exchange interaction in the axisymmetric case. The particular case of the interaction of uniform plasma flow with a spherically symmetric distribution of neutral particles is considered in detail. It is shown that charge exchange interaction is able to stagnate the plasma flow completely at a finite distance along the stagnation line, and to redirect the plasma flow around the neutral cloud. We argue that this simple situation could represent the main features of the interaction of the interstellar wind with high‐speed solar hydrogen flowing from the inner heliosphere. Thus the heliopause could in part, if not as a whole, actually result from this particular form of charge exchange interaction. The degree of ionization of the local interstellar medium is shown to determine the extent of the role of the charge exchange interaction. For a partially ionized interstellar wind, charge exchange plays the main role in slowing down and stagnating the interstellar wind, while for a highly ionized interstellar wind, its role is secondary to the direct gasdynamic interaction of the two counter streaming plasmas.

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Background Given tongue features and basic features, this study aimed to develop and assess a non-invasive machine learning model to perform regression prediction on fasting plasma glucose and glycated haemoglobin which will help optimize diabetes risk warning. Methods We collected the basic features, tongue features and blood features of the subjects. Using machine learning algorithms to analyze these data, we built models to predict fasting plasma glucose and glycated haemoglobin. Then the performance of the models was evaluated through 5-fold crossvalidation results and test set results. Results The results of cross validation on the training set showed that given non-invasive input features, the minimum average mean square error of fasting plasma glucose and glycated haemoglobin prediction was 1.227 and 0.438. Our non-invasive fasting plasma glucose prediction model with tongue features and basic features combined achieved a minimum mean square error of 0.601 and a maximum coe cient of determination of 0.606 on the test set. The glycated haemoglobin prediction model product a minimum mean square error of 0.272 and a maximum coe cient of determination of 0.539 on the test set. The Clarke's Error Grid Analysis showed that the non-invasive blending model had 90.83% of points in zone A and 8.49% of points in zone B on the test set. Conclusions We developed an effective non-invasive method for estimating fasting plasma glucose and glycated haemoglobin from tongue features and basic features combined, which may help identify individuals at high risk for diabetes.

Sales professionals need to identify new sales prospects, and sales executives need to deploy the sales force against the sales accounts with the best potential for future revenue. We describe two analytics-based solutions developed within IBM to address these related issues. The Web-based tool OnTARGET provides a set of analytical models to identify new sales opportunities at existing client accounts and noncustomer companies. The models estimate the probability of purchase at the product-brand level. They use training examples drawn from historical transactions and extract explanatory features from transactional data joined with company firmographic data (e.g., revenue and number of employees). The second initiative, the Market Alignment Program, supports sales-force allocation based on field-validated analytical estimates of future revenue opportunity in each operational market segment. Revenue opportunity estimates are generated by defining the opportunity as a high percentile of a conditional distribution of the customer's spending, that is, what we could realistically hope to sell to this customer. We describe the development of both sets of analytical models, the underlying data models, and the Web sites used to deliver the overall solution. We conclude with a discussion of the business impact of both initiatives.

The energy exchange between a plasma particle and an Alfvén wave propagating along a magnetic field is considered. It is shown that, in the presence of an accelerating force parallel to the background magnetic field, there exists a new channel for nonresonant energy transfer between the wave and the particle.