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  • 1
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    IJGI, Vol. 4, Pages 1549-1568: Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery (2015)
    MDPI Publishing
    Details
    Publication Date: 2015-08-25
    Description: Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort has been made to classify social media data into stages of disaster management (mitigation, preparedness, emergency response, and recovery), which has been used as a common reference for disaster researchers and emergency managers for decades to organize information and streamline priorities and activities during the course of a disaster. This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages. Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. The classification results are necessary and useful for emergency managers to identify the transition between phases of disaster management, the timing of which is usually unknown and varies across disaster events, so that they can take action quickly and efficiently in the impacted communities. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery.
    Electronic ISSN: 2220-9964
    Topics: Architecture, Civil Engineering, Surveying , Geosciences
    Published by MDPI Publishing on behalf of International Society for Photogrammetry and Remote Sensing (ISPRS).
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  • 2
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    Sensors, Vol. 15, Pages 12700-12719: A Programmable High-Voltage Compliance Neural Stimulator for Deep Brain Stimulation in Vivo (2015)
    MDPI Publishing
    In: Sensors
    Details
    Publication Date: 2015-05-29
    Description: Deep brain stimulation (DBS) is one of the most effective therapies for movement and other disorders. The DBS neurosurgical procedure involves the implantation of a DBS device and a battery-operated neurotransmitter, which delivers electrical impulses to treatment targets through implanted electrodes. The DBS modulates the neuronal activities in the brain nucleus for improving physiological responses as long as an electric discharge above the stimulation threshold can be achieved. In an effort to improve the performance of an implanted DBS device, the device size, implementation cost, and power efficiency are among the most important DBS device design aspects. This study aims to present preliminary research results of an efficient stimulator, with emphasis on conversion efficiency. The prototype stimulator features high-voltage compliance, implemented with only a standard semiconductor process, without the use of extra masks in the foundry through our proposed circuit structure. The results of animal experiments, including evaluation of evoked responses induced by thalamic electrical stimuli with our fabricated chip, were shown to demonstrate the proof of concept of our design.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by MDPI Publishing
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  • 3
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    Sustainability, Vol. 8, Pages 739: Expert Concepts of Sustainable Service Innovation in Restaurants in Taiwan (2016)
    MDPI Publishing
    Details
    Publication Date: 2016-08-04
    Description: Sustainable service innovation is a critical attribute in restaurant management that is widely recognized by experts and restaurant owners. In this paper, we investigated ideas on sustainable service innovation in restaurants gathered from interviews with restaurant managers, government experts and scholars in Taiwan. The analytical results show that five dimensions are major indicators of sustainable service innovation in the restaurant management field. These include the following dimensions: sustainable service innovation, food service technology, organizational learning, adoption of innovation and organizational environment. We also found that these five dimensions are important and that they deeply impact restaurant performance. We discuss the characteristics of these five attributes, and talk about the theoretical and empirical implications of research findings.
    Electronic ISSN: 2071-1050
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by MDPI Publishing
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  • 4
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    Sensors, Vol. 16, Pages 1152: Reduced Graphene Oxide/Au Nanocomposite for NO2 Sensing at Low Operating Temperature (2016)
    MDPI Publishing
    In: Sensors
    Details
    Publication Date: 2016-07-23
    Description: A reduced grapheme oxide (rGO)/Au hybrid nanocomposite has been synthesized by hydrothermal treatment using graphite and HAuCl4 as the precursors. Characterization, including X-ray diffraction (XRD), Raman spectra, X-ray photoelecton spectroscopy (XPS) and transmission electron microscopy (TEM), indicates the formation of rGO/Au. A gas sensor fabricated with rGO/Au nanocomposite was applied for NO2 detection at 50 °C. Compared with pure rGO, rGO/Au nanocomposite exhibits higher sensitivity, a more rapid response–recovery process and excellent reproducibility.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by MDPI Publishing
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  • 5
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    IJERPH, Vol. 13, Pages 395: Structural Factors Affecting Health Examination Behavioral Intention (2016)
    MDPI Publishing
    Details
    Publication Date: 2016-04-02
    Description: Disease screening instruments used for secondary prevention can facilitate early determination and treatment of pathogenic factors, effectively reducing disease incidence, mortality rates, and health complications. Therefore, people should be encouraged to receive health examinations for discovering potential pathogenic factors before symptoms occur. Here, we used the health belief model as a foundation and integrated social psychological factors and investigated the factors influencing health examination behavioral intention among the public in Taiwan. In total, 388 effective questionnaires were analyzed through structural model analysis. Consequently, this study yielded four crucial findings: (1) The established extended health belief model could effectively predict health examination behavioral intention; (2) Self-efficacy was the factor that most strongly influenced health examination behavioral intention, followed by health knowledge; (3) Self-efficacy substantially influenced perceived benefits and perceived barriers; (4) Health knowledge and social support indirectly influenced health examination behavioral intention. The preceding results can effectively increase the acceptance and use of health examination services among the public, thereby facilitating early diagnosis and treatment and ultimately reducing disease and mortality rates.
    Print ISSN: 1661-7827
    Electronic ISSN: 1660-4601
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Medicine
    Published by MDPI Publishing
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  • 6
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    Sensors, Vol. 16, Pages 1314: Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones (2016)
    MDPI Publishing
    In: Sensors
    Details
    Publication Date: 2016-08-21
    Description: As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current operating hand, detect the hand-changing process and then adjust the user interfaces subsequently. In this paper, we proposed, implemented and evaluated two novel systems. The first one leverages the user-generated touchscreen traces to recognize the current operating hand, and the second one utilizes the accelerometer and gyroscope data of all kinds of activities in the user’s daily life to detect the hand-changing process. These two systems are based on two supervised classifiers constructed from a series of refined touchscreen trace, accelerometer and gyroscope features. As opposed to existing solutions that all require users to select the current operating hand or confirm the hand-changing process manually, our systems follow much more convenient and practical methods and allow users to change the operating hand frequently without any harm to the user experience. We conduct extensive experiments on Samsung Galaxy S4 smartphones, and the evaluation results demonstrate that our proposed systems can recognize the current operating hand and detect the hand-changing process with 94.1% and 93.9% precision and 94.1% and 93.7% True Positive Rates (TPR) respectively, when deciding with a single touchscreen trace or accelerometer-gyroscope data segment, and the False Positive Rates (FPR) are as low as 2.6% and 0.7% accordingly. These two systems can either work completely independently and achieve pretty high accuracies or work jointly to further improve the recognition accuracy.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by MDPI Publishing
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  • 7
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    Sensors, Vol. 16, Pages 1324: Transportation Modes Classification Using Sensors on Smartphones (2016)
    MDPI Publishing
    In: Sensors
    Details
    Publication Date: 2016-08-20
    Description: This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user’s transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by MDPI Publishing
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  • 8
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    Forests, Vol. 6, Pages 1439-1453: Applying Effective Population Size Estimates of Kandelia Obovata Sheue, Liu and Yong to Conservation and Restoration Management (2015)
    MDPI Publishing
    In: Forests
    Details
    Publication Date: 2015-04-29
    Description: Effective population size (Ne) is a crucial metric for evaluating the current status of genetic diversity and conservation management. Population of Kandelia obovata, a mangrove species that is patchily distributed along the estuaries off Southeastern China, is genetically structured. Here, we applied skyline analyses to infer the demographic history of K. obovata based on Amplified Fragment Length Polymorphisms (AFLP) data. Congruent trends of population growth rate among populations, but concurrent change in Ne estimates, were inferred in all populations. The recent rapid habitat expansion explains the high census population size but small Ne of populations in Northern Taiwan. Our study also revealed lower Ne of reforested populations than their sources. In silico demographic analyses simulate the small or biased sampling of seedlings for reforestation and revealed over 90% and 99% Ne reduction when only 1/2 and 1/10 samples were collected, respectively. These results emphasize the importance of a comprehensive sampling of seeds for restoration. Overall, this study rendered, not only the current Ne of K. obovata populations, but also indicates the importance of Ne estimation on restoration.
    Electronic ISSN: 1999-4907
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by MDPI Publishing
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  • 9
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    Sensors, Vol. 15, Pages 15326-15338: A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle (2015)
    MDPI Publishing
    In: Sensors
    Details
    Publication Date: 2015-06-30
    Description: In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI) algorithm. The gray level co-occurrence matrix (GLCM) was further used to evaluate the texture features of the segmented region. These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures. A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles. The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by MDPI Publishing
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  • 10
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    Sensors, Vol. 12, Pages 11592-11600: A Fully Integrated Humidity Sensor System-on-Chip Fabricated by Micro-Stamping Technology (2012)
    MDPI Publishing
    In: Sensors
    Details
    Publication Date: 2012-08-28
    Description: A fully integrated humidity sensor chip was designed, implemented, and tested. Utilizing the micro-stamping technology, the pseudo-3D sensor system-on-chip (SSoC) architecture can be implemented by stacking sensing materials directly on the top of a CMOS-fabricated chip. The fabricated sensor system-on-chip (2.28 mm × 2.48 mm) integrated a humidity sensor, an interface circuit, a digital controller, and an On-Off Keying (OOK) wireless transceiver. With low power consumption, i.e., 750 μW without RF operation, the sensitivity of developed sensor chip was experimentally verified in the relative humidity (RH) range from 32% to 60%. The response time of the chip was also experimentally verified to be within 5 seconds from RH 36% to RH 64%. As a consequence, the implemented humidity SSoC paves the way toward the an ultra-small sensor system for various applications.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by MDPI Publishing
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