Improvement in Mechanical Properties of Al2024 Alloy Using Mechanical Working and Heat Treatment.

Extrusion speed has a significant influence on the extrusion temperature, microstructure and mechanical properties of the material in the repetitive continuous extrusion forming (RCEF) process. In this work, the mechanical properties of Al2024 were improved by adjusting the speed (with a general range of 2-10 rpm) of repetitive continuous extrusion and applying subsequent heat treatment. During the RCEF process, an increase in the extrusion speed from 4 to 8 rpm was found to increase the extrusion temperature and then enhance the solid solution function. The grain size was affected by the combined effect of deformation speed and its induced temperature. A high-strength Al2024 (ultimate tensile strength of 497.6 MPa) with good elongation (12.93%) was obtained by increasing the extrusion speed and conducting solid solution and artificial aging treatments. The main strengthening mechanisms could be attributed to finer grain size and a larger amount of S (Al2CuMg) precipitates.

A smartphone-based support system coupled with a bluetooth breathalyzer in the treatment of alcohol dependence: A 12-week randomized controlled trial.

Background: Our prior open trial showed the feasibility of a smartphone-based support system coupled with a Bluetooth breathalyzer (SoberDiary) in assisting recovery for patients with alcohol dependence (AD). In this 24-week follow-up study, we further explored the efficacy of supplementing SoberDiary to treatment as usual (TAU) over 12 weeks of intervention and whether the efficacy persisted in the post-intervention 12 weeks. Methods: 51 patients who met the DSM-IV criteria of AD were randomly assigned to the technological intervention group (TI group, receiving technology intervention of SoberDiary plus TAU, n = 25) or those receiving only TAU (TAU group, n = 26). After 12 weeks of intervention (Phase I), all participants were followed for another post-intervention 12 weeks (Phase II). We collected the drinking variables and psychological assessment data every 4 weeks (i.e., weeks 4, 8, 12, 16, 20, and 24). In addition, the cumulative abstinence days and retention rates were recorded. We used mixed-model analysis to compare the difference in outcomes between groups. Results: In Phase I or Phase II, we did not find differences in drinking variables, alcohol craving, depression, or anxiety severity between the two groups. However, the TI group showed greater self-efficacy for drinking refusal in Phase II than the TAU group. Conclusions: Although our system (SoberDiary) did not demonstrate benefits in drinking or emotional outcomes, we found the system holds promise to enhance self-efficacy on drinking refusal. Whether the benefit in promoting self-efficacy persists longer than 24 weeks requires further investigation.

Learning and Recognition of Clothing Genres From Full-Body Images.

According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel approach for automatically classifying clothing genres based on the visually differentiable style elements. A set of style elements, that are crucial for recognizing specific visual styles of clothing genres, were identified based on the clothing design theory. In addition, the corresponding salient visual features of each style element were identified and formulated with variables that can be computationally derived with various computer vision algorithms. To evaluate the performance of our algorithm, a dataset containing 3250 full-body shots crawled from popular online stores was built. Recognition results show that our proposed algorithms achieved promising overall precision, recall, and -score of 88.76%, 88.53%, and 88.64% for recognizing upperwear genres, and 88.21%, 88.17%, and 88.19% for recognizing lowerwear genres, respectively. The effectiveness of each style element and its visual features on recognizing clothing genres was demonstrated through a set of experiments involving different sets of style elements or features. In summary, our experimental results demonstrate the effectiveness of the proposed method in clothing genre recognition.

Smartphone-based support system (SoberDiary) coupled with a Bluetooth breathalyser for treatment-seeking alcohol-dependent patients.

BACKGROUND: Relapse prevention in patients with alcohol dependence (AD) has long been a clinical challenge. It is vital to provide services with minial restrictions for patients to have access to continuous after-treatment care. The study was aimed to examine the benefits of a smartphone application (SoberDiary) coupled to a Bluetooth breathalyser to assist patients recovering from alcohol dependence (AD). METHODS: This study recruited 38 patients that fulfilled the DSM-IV-TR criteria for AD and who were undergoing an outpatient maintenance program for abstinence. The participants were provided a breathalyser and smartphone-equipped SoberDiary and followed for 12weeks. The participants were divided into highly adherent (HA) and less adherent (LA) groups according to the medium number of reward points they were awarded for using the SoberDiary system throughout the follow-up period based on the average amount of time spent on using the application, the number of function modules they accessed, and the number of BrAC tests they completed each day. RESULTS: 19 of the patients were classified as HA and 19 patients were classified as LA group. Members of the HA group recorded fewer drinking days and drinks consumed per week, a higher cumulative number of abstinence days, a higher abstinence rate, less pronounced anxiety, and superior quality of life. CONCLUSIONS: The proposed smartphone-assisted support system coupled with a Bluetooth breathalyser might be a feasible supplement to conventional treatment for AD. Higher SoberDiary compliance appears to be associated with better clinical outcomes.

A novel design of Rogowski coil for measurement of nanosecond-risetime high-level pulsed current.

In pulsed power systems, pulsed currents with risetimes from nanosecond to microsecond can be effectively measured by self-integrating Rogowski coils. Appropriate design of the structure and the integrating resistor is crucial to the high-frequency response of a coil. In this paper, several novel designs of Rogowski coil's integrating resistors were proposed and tested. Experimental results showed that the optimized coil could response square waves with fronts of ∼1.5 ns and had a sensitivity of ∼0.75 V/kA. The maximal peak current was designed as 100 kA.

Learning and recognition of on-premise signs from weakly labeled street view images.

Camera-enabled mobile devices are commonly used as interaction platforms for linking the user's virtual and physical worlds in numerous research and commercial applications, such as serving an augmented reality interface for mobile information retrieval. The various application scenarios give rise to a key technique of daily life visual object recognition. On-premise signs (OPSs), a popular form of commercial advertising, are widely used in our living life. The OPSs often exhibit great visual diversity (e.g., appearing in arbitrary size), accompanied with complex environmental conditions (e.g., foreground and background clutter). Observing that such real-world characteristics are lacking in most of the existing image data sets, in this paper, we first proposed an OPS data set, namely OPS-62, in which totally 4649 OPS images of 62 different businesses are collected from Google's Street View. Further, for addressing the problem of real-world OPS learning and recognition, we developed a probabilistic framework based on the distributional clustering, in which we proposed to exploit the distributional information of each visual feature (the distribution of its associated OPS labels) as a reliable selection criterion for building discriminative OPS models. Experiments on the OPS-62 data set demonstrated the outperformance of our approach over the state-of-the-art probabilistic latent semantic analysis models for more accurate recognitions and less false alarms, with a significant 151.28% relative improvement in the average recognition rate. Meanwhile, our approach is simple, linear, and can be executed in a parallel fashion, making it practical and scalable for large-scale multimedia applications.