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Design patterns raise the abstraction level at which people design and communicate design of object-oriented software. But design patterns still leave the mechanics of their implementation to the programmer. This paper describes the architecture and implementation of a tool that automates the implementation of design patterns. The user of the tool supplies application-specific information for a given pattern, from which the tool generates all the pattern-prescribed code automatically. The tool has a distributed architecture that lends itself to implementation with off-the-shelf components.

Anion storage in cathode of dual-ion batteries provides leeway for new battery chemistries. For high energy density and better safety, it is desirable but challenging to reversibly intercalate chloride in a graphite cathode because either the oxygen or chlorine evolution reaction can prevail over chloride insertion. The primary barrier is the lack of suitable aqueous electrolytes that suppress these parasitic reactions. Herein, we report an aqueous deep eutectic solvent gel electrolyte that allows reversible chloride storage for graphite based on a chloride-based electrolyte via the formation of iodine–chloride interhalogens. The results suggest three reversible steps: iodine plating on the host surface, oxidation to form I-Cl interhalides, and then intercalation into graphite. As a result, the graphite cathode delivers a high reversible capacity of 291 mAh g–1 with stable cycling performance. Facilitated by the same mechanism, a porous graphenic carbon delivered a record-high capacity of over 1100 mAh g–1.

This paper presents an automatic computer-aided detection scheme on digital chest radiographs to detect pneumoconiosis. Firstly, the lung fields are segmented from a digital chest X-ray image by using the active shape model method. Then, the lung fields are subdivided into six non-overlapping regions, according to Chinese diagnosis criteria of pneumoconiosis. The multi-scale difference filter bank is applied to the chest image to enhance the details of the small opacities, and the texture features are calculated from each region of the original and the processed images, respectively. After extracting the most relevant ones from the feature sets, support vector machine classifiers are utilized to separate the samples into the normal and the abnormal sets. Finally, the final classification is performed by the chest-based report-out and the classification probability values of six regions. Experiments are conducted on randomly selected images from our chest database. Both the training and the testing sets have 300 normal and 125 pneumoconiosis cases. In the training phase, training models and weighting factors for each region are derived. We evaluate the scheme using the full feature vectors or the selected feature vectors of the testing set. The results show that the classification performances are high. Compared with the previous methods, our fully automated scheme has a higher accuracy and a more convenient interaction. The scheme is very helpful to mass screening of pneumoconiosis in clinic.

Most of today's structured data is stored in rela-

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Mood disorders are common and associated with significant morbidity and mortality. Early diagnosis has the potential to greatly alleviate the burden of mental illness and the ever increasing costs to families and society. Mobile devices provide us a promising opportunity to detect the users' mood in an unobtrusive manner. In this study, we use a custom keyboard which collects keystrokes' meta-data and accelerometer values. Based on the collected time series data in multiple modalities, we propose a deep personalized mood prediction approach, called dpMood, by integrating convolutional and recurrent deep architectures as well as exploring each individual's circadian rhythm. Experimental results not only demonstrate the feasibility and effectiveness of using smart-phone meta-data to predict the presence and severity of mood disturbances in bipolar subjects, but also show the potential of personalized medical treatment for mood disorders.