The tuning of CdSe quantum dot (QDs) sizes, and consequently their corresponding two-photon absorption (TPA) cross section, has been systematically investigated. As the size (diameter) of the quantum dots increases, the TPA cross section is found to be empirically related via a power-law proportionality of 3.5+/-0.5 and 5.6+/-0.7 to the diameters of CdSe and CdTe QDs, respectively. The results are tentatively rationalized via a theoretical model of two-photon excitation properties in a system incorporating excitons and defects.

Multipath is one major error source in highaccuracy GNSS positioning. Various hardware and software approaches are developed to mitigate the multipath effect. Among them the MHM (multipath hemispherical map) and sidereal filtering (SF)/advanced SF (ASF) approaches utilize the spatiotemporal repeatability of multipath effect under static environment, hence they can be implemented to generate multipath correction model for real-time GNSS data processing. We focus on the spatial-temporal repeatability-based MHM and SF/ASF approaches and compare their performances for multipath reduction. Comparisons indicate that both MHM and ASF approaches perform well with residual variance reduction (50 %) for short span (next 5 days) and maintains roughly 45 % reduction level for longer span (next 6-25 days). The ASF model is more suitable for high frequency multipath reduction, such as high-rate GNSS applications. The MHM model is easier to implement for real-time multipath mitigation when the overall multipath regime is medium to low frequency.

Background The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with an accelerated decline in lung function, diminished quality of life, and higher mortality. Accurate early detection of acute exacerbations will enable early management and reduce mortality. Objective The aim of this study was to develop a prediction system using lifestyle data, environmental factors, and patient symptoms for the early detection of AECOPD in the upcoming 7 days. Methods This prospective study was performed at National Taiwan University Hospital. Patients with COPD that did not have a pacemaker and were not pregnant were invited for enrollment. Data on lifestyle, temperature, humidity, and fine particulate matter were collected using wearable devices (Fitbit Versa), a home air quality–sensing device (EDIMAX Airbox), and a smartphone app. AECOPD episodes were evaluated via standardized questionnaires. With these input features, we evaluated the prediction performance of machine learning models, including random forest, decision trees, k-nearest neighbor, linear discriminant analysis, and adaptive boosting, and a deep neural network model. Results The continuous real-time monitoring of lifestyle and indoor environment factors was implemented by integrating home air quality–sensing devices, a smartphone app, and wearable devices. All data from 67 COPD patients were collected prospectively during a mean 4-month follow-up period, resulting in the detection of 25 AECOPD episodes. For 7-day AECOPD prediction, the proposed AECOPD predictive model achieved an accuracy of 92.1%, sensitivity of 94%, and specificity of 90.4%. Receiver operating characteristic curve analysis showed that the area under the curve of the model in predicting AECOPD was greater than 0.9. The most important variables in the model were daily steps walked, stairs climbed, and daily distance moved. Conclusions Using wearable devices, home air quality–sensing devices, a smartphone app, and supervised prediction algorithms, we achieved excellent power to predict whether a patient would experience AECOPD within the upcoming 7 days. The AECOPD prediction system provided an effective way to collect lifestyle and environmental data, and yielded reliable predictions of future AECOPD events. Compared with previous studies, we have comprehensively improved the performance of the AECOPD prediction model by adding objective lifestyle and environmental data. This model could yield more accurate prediction results for COPD patients than using only questionnaire data.

Syntheses of CdTe/CdSe type‐II quantum dots (QDs) using CdO and CdCl2 as precursors for core and shell, respectively, are reported. Characterization was made via near‐IR interband emission, transmission electron microscopy (TEM), energy dispersive spectroscopy (EDX), and X‐ray diffraction (XRD). Femtosecond fluorescence upconversion measurements on the relaxation dynamics of the CdTe core (in CdTe/CdSe) emission and CdTe/CdSe interband emission reveal that as the size of the core increases from 5.3, 6.1 to 6.9 nm, the rate of photoinduced electron separation decreases from 1.96, 1.44 to 1.07 ×1012 s−1. The finite rates of the initial charge separation are tentatively rationalized by the small electron–phonon coupling, causing weak coupling between the initial and charge‐separated states.

We present a high-capacity steganographic approach for three-dimensional (3D) polygonal meshes. We first use the representation information of a 3D model to embed messages. Our approach successfully combines both the spatial domain and the representation domain for steganography. In the spatial domain, every vertex of a 3D polygonal mesh can be represented by at least three bits using a modified multi-level embed procedure (MMLEP). In the representation domain, the representation order of vertices and polygons and even the topology information of polygons can be represented with an average of six bits per vertex using the proposed representation rearrangement procedure (RRP). Experimental results show that the proposed technique is efficient and secure, has high capacity and low distortion, and is robust against affine transformations. Our technique is a feasible alternative to other steganographic approaches