Android IoT Lifelog System and Its Application to Motion Inference

In social science, health care, digital therapeutics, etc., smartphone data have played important roles to infer users’ daily lives. However, smartphone data collection systems could not be used effectively and widely because they did not exploit any Internet of Things (IoT) standards (e.g., oneM2M) and class labeling methods for machine learning (ML) services. Therefore, in this paper, we propose a novel Android IoT lifelog system complying with oneM2M standards to collect various lifelog data in smartphones and provide two manual and automated class labeling methods for inference of users’ daily lives. The proposed system consists of an Android IoT client application, an oneM2M-compliant IoT server, and an ML server whose high-level functional architecture was carefully designed to be open, accessible, and internationally recognized in accordance with the oneM2M standards. In particular, we explain implementation details of activity diagrams for the Android IoT client application, the primary component of the proposed system. Experimental results verified that this application could work with the oneM2M-compliant IoT server normally and provide corresponding class labels properly. As an application of the proposed system, we also propose motion inference based on three multi-class ML classifiers (i.e., k nearest neighbors, Naive Bayes, and support vector machine) which were created by using only motion and location data (i.e., acceleration force, gyroscope rate of rotation, and speed) and motion class labels (i.e., driving, cycling, running, walking, and stilling). When compared with confusion matrices of the ML classifiers, the k nearest neighbors classifier outperformed the other two overall. Furthermore, we evaluated its output quality by analyzing the receiver operating characteristic (ROC) curves with area under the curve (AUC) values. The AUC values of the ROC curves for all motion classes were more than 0.9, and the macro-average and micro-average ROC curves achieved very high AUC values of 0.96 and 0.99, respectively.     

5 nm Silver Nanoparticles Amplify Clinical Features of Atopic Dermatitis in Mice by Activating Mast Cells

The triggering effect of silver nanoparticles (NPs) on the induction of allergic reactions is evaluated, by studying the activation of mast cells and the clinical features of atopic dermatitis in a mouse model. Granule release is induced in RBL‐2H3 mast cells by 5 nm, but not 100 nm silver NPs. Increases in the levels of reactive oxygen species (hydrogen peroxide and mitochondrial superoxide) and intracellular Ca⁺⁺ in mast cells are induced by 5 nm silver NPs. In a mouse model of atopic dermatitis induced by a mite allergen, the skin lesions are more severe and appear earlier in mice treated simultaneously with 5 nm silver NPs and allergen compared with mice treated with allergen alone or 100 nm silver NPs and allergen. The histological findings reveal that number of tryptase‐positive mast cells and total IgE levels in the serum increase in mice treated with 5 nm silver NPs and allergen. The results in this study indicate that cotreatment with 5 nm silver NPs stimulates mast cell degranulation and induces earlier and more severe clinical alterations in allergy‐prone individuals.     

Silver Nanoflower Decorated Graphene Oxide Sponges for Highly Sensitive Variable Stiffness Stress Sensors

Soft conductive materials should enable large deformation while keeping high electrical conductivity and elasticity. The graphene oxide (GO)‐based sponge is a potential candidate to endow large deformation. However, it typically exhibits low conductivity and elasticity. Here, the highly conductive and elastic sponge composed of GO, flower‐shaped silver nanoparticles (AgNFs), and polyimide (GO‐AgNF‐PI sponge) are demonstrated. The average pore size and porosity are 114 µm and 94.7%, respectively. Ag NFs have thin petals (8–20 nm) protruding out of the surface of a spherical bud (300–350 nm) significantly enhancing the specific surface area (2.83 m² g^?1). The electrical conductivity (0.306 S m^?1 at 0% strain) of the GO‐AgNF‐PI sponge is increased by more than an order of magnitude with the addition of Ag NFs. A nearly perfect elasticity is obtained over a wide compressive strain range (0–90%). The strain‐dependent, nonlinear variation of Young's modulus of the sponge provides a unique opportunity as a variable stiffness stress sensor that operates over a wide stress range (0–10 kPa) with a high maximum sensitivity (0.572 kPa^?1). It allows grasping of a soft rose and a hard bottle, with the minimal object deformation, when attached on the finger of a robot gripper.     

A new quadruple therapy for the eradication of Helicobacter pylori. Effect of pretreatment with omeprazole on the cure rate

The aim of the study was to assess the speech discrimination ability of postlingually deaf adults implanted with the Combi 40 cochlear implant and to compare the results with the postoperative data published for other devices. The postoperative open and closed set speech perception performance of 21 consecutive patients was tested using a standardized test battery comprising a number, monosyllable, sentence, consonant and vowel discrimination test as well as a rhyme test in the sound only condition. Mean values achieved for each test 1, 6 and 12 months after "switch on" were evaluated. The results demonstrate that all patients have a substantial benefit from their implant and show a continuous improvement in their speech perception abilities with increased device experience. The mean percentages of correct answers after 12 months were 93.4 for numbers, 44.6 for monosyllables, 78.5 for sentences, 67.6 for the rhyme test, 59.8 for vowel, and 67.3 for consonant discrimination. Preoperatively, the mean discrimination score for monosyllables was 0%. The speech discrimination scores of our patients were similar or higher than described for similar patient groups implanted with other devices. The high stimulation rate of the implant system using the continuous interleaved speech processing strategy as well as a deep atraumatic electrode insertion into the apicalmost regions of the scala tympani may be the reason for good performance.     

Adaptive triangular-mesh reconstruction by mean-curvature-based refinement from point clouds using a moving parabolic approximation

We use a moving parabolic approximation (MPA) to reconstruct a triangular mesh that approximates the underlying surface of a point cloud from closed objects. First, an efficient strategy is presented for constructing a hierarchical grid with adaptive resolution and generating an initial mesh from point clouds. By implementing the MPA algorithm, we can estimate the differential quantities of the underlying surface, and subsequently, we can obtain the local quadratic approximants of the squared distance function for any point in the vicinity of the target shape. Thus, second, we adapt the mesh to the target shape by an optimization procedure that minimizes a quadratic function at each step. With the objective of determining the geometrical features of the target surface, we refine the approximating mesh selectively for the non-flat regions by comparing the estimated curvature from the point clouds and the estimated curvatures computed from the current mesh. Finally, we present various examples that demonstrate the robustness of our method and show that the resulting reconstructions preserve geometric details.