Detecting Popularity of Ideas and Individuals in Online Community

Research in the last decade has prioritized the effects of online texts and online behaviors on user information prediction. However, the previous research overlooks the overall meaning of online texts and more detailed features about users’ online behaviors. The purpose of the research is to detect the adopted ideas, the popularity of ideas, and the popularity of individuals by identifying the overall meaning of online texts and the centrality features based on user’s online interactions within an online community. To gain insights into the research questions, the online discussions on MyStarbucksIdea website is examined in this research. MyStarbucksIdea had launched since 2008 that encouraged people to submit new ideas for improving Starbuck’s products and services. Starbucks had adopted hundreds of ideas from this crowdsourcing platform. Based on the example of the MyStarbucksIdea community, a new document representation approach, Doc2Vec, synthesized with the users’ centrality features was unitized in this research. Additionally, it also is essential to study the surface-level features of online texts, the sentiment features of online texts, and the features of users’ online behaviors to determine the idea adoption as well as the popularity of ideas and individuals in the online community. Furthermore, supervised machine learning approaches, including Logistic Regression, Support Vector Machine, and Random Forest, with the adjustments for the imbalanced classes, served as the classifiers for the experiments. The results of the experiments showed that the classifications of the idea adoption, the popularity of ideas, and the popularity of individuals were all considered successful. The overall meaning of idea texts and user’s centrality features were most accurate in detecting the adopted ideas and the popularity of ideas. The overall meaning of idea texts and the features of users’ online behaviors were most accurate in detecting the popularity of individuals. These results are in accord with the results of the previous studies, which used behavioral and textual features to predict user information and enhance the previous studies\u27 results by providing the new document embedding approach and the centrality features. The models used in this research can become a much-needed tool for the popularity predictions of future research

Missing link in community psychiatry: When a patient with schizophrenia was expelled from her home

Treatment and disposition of homeless patients with schizophrenia represent a great challenge in clinical practice. We report a case of this special population, and discuss the development of homelessness, the difficulty in disposition, their utilization of health services, and possible applications of mandatory community treatment in this group of patients. A 51-year-old homeless female was brought to an emergency department for left femur fracture caused by an assault. She was diagnosed with schizophrenia about 20 years ago but received little help from mental health services over the decades. During hospitalization, her psychotic symptoms were only partially responsive to treatment. Her family refused to handle caretaking duties. The social welfare system was mobilized for long-term disposition. Homeless patients with schizophrenia are characterized by family disruption, poor adherence to health care, and multiple emergency visits and hospitalization. We hope this article can provide information about the current mental health policy to medical personnel. It is possible that earlier intervention and better outcome can be achieved by utilizing mandatory community treatment in the future, as well as preventing patients with schizophrenia from losing shelters

Qubit Mapping Toward Quantum Advantage

Qubit Mapping is a pivotal stage in quantum compilation flow. Its goal is to convert logical circuits into physical circuits so that a quantum algorithm can be executed on real-world non-fully connected quantum devices. Qubit Mapping techniques nowadays still lack the key to quantum advantage, scalability. Several studies have proved that at least thousands of logical qubits are required to achieve quantum computational advantage. However, to our best knowledge, there is no previous research with the ability to solve the qubit mapping problem with the necessary number of qubits for quantum advantage in a reasonable time. In this work, we provide the first qubit mapping framework with the scalability to achieve quantum advantage while accomplishing a fairly good performance. The framework also boasts its flexibility for quantum circuits of different characteristics. Experimental results show that the proposed mapping method outperforms the state-of-the-art methods on quantum circuit benchmarks by improving over 5% of the cost complexity in one-tenth of the program running time. Moreover, we demonstrate the scalability of our method by accomplishing mapping of an 11,969-qubit Quantum Fourier Transform within five hours

Maximum Power Point Tracking Method Based on Modified Particle Swarm Optimization for Photovoltaic Systems

This study investigated the output characteristics of photovoltaic module arrays with partial module shading. Accordingly, we presented a maximum power point tracking (MPPT) method that can effectively track the global optimum of multipeak curves. This method was based on particle swarm optimization (PSO). The concept of linear decreases in weighting was added to improve the tracking performance of the maximum power point tracker. Simulation results were used to verify that this method could successfully track maximum power points in the output characteristic curves of photovoltaic modules with multipeak values. The results also established that the performance of the modified PSO-based MPPT method was superior to that of conventional PSO methods

Association Between Thyroid Disorders and Colorectal Cancer Risk in Adult Patients in Taiwan

IMPORTANCE Thyroid hormones have been shown to affect several important pathways in cancer development, including colorectal cancer (CRC). Clinical studies examining the association between thyroid disorders and colorectal cancer have conflicting results and have predominantly involved white populations. OBJECTIVE To determine if a diagnosis of hyperthyroidism or hypothyroidism is associated with the risk of developing colorectal cancer in an East Asian population. DESIGN, SETTING, AND PARTICIPANTS This nationwide population-based case-control study was conducted from April 27, 2018, to November 8, 2018, using the Taiwanese National Health Insurance Research Database. Participants were adults (n = 139 426) either with a new diagnosis (between 2008 and 2013) of primary colorectal cancer without a history of cancer, or without cancer. Cases and controls were matched 11 by age, sex, and index date. Diagnosis of hyperthyroidism or hypothyroidism prior to the diagnosis of colorectal cancer (or the same index date in controls) was then determined. MAIN OUTCOMES AND MEASURES Risk differences in developing colorectal cancer among patients with a medical history of hyperthyroidism or hypothyroidism. RESULTS A total of 139 426 patients were included in the study, and 69 713 individuals made up each case and control group, which were both predominantly male (39 872 [57.2%]). The mean (SD) age for those with CRC was 65.8 (13.7) years and for those without CRC was 66.0 (13.6) years. Both hyperthyroidism (adjusted odds ratio [aOR], 0.77; 95% CI, 0.69-0.86; P < .001) and hypothyroidism (aOR, 0.78; 95% CI, 0.65-0.94; P = .008) were associated with a decreased risk of being diagnosed with colorectal cancer. An inverse association of rectal cancer was found among patients aged 50 years or older with a history of hypothyroidism despite treatment (aOR, 0.54; 95% CI, 0.39-0.74; P < .001). A history of hyperthyroidism in all age groups was associated with a lower risk of colon cancer (aOR, 0.74; 95% CI, 0.64-0.85; P < .001), with a stronger association seen among those younger than 50 years (aOR, 0.55; 95% CI, 0.36-0.85; P = .007). CONCLUSIONS AND RELEVANCE In this study, hypothyroidism appeared to be associated with a lower risk of rectal cancer, whereas hyperthyroidism appeared to be associated with a lower risk of colon cancer. Because of this, biochemical in vivo research and epidemiologic studies appear to be needed to further clarify the nature of these associations.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Emotion and Concentration Integrated System: Applied to the Detection and Analysis of Consumer Preference

With the expansion of consumer market, the appearance becomes an important issue when consumers make decisions under the situation of similar qualities and contents. Accordingly, to attract consumers, companies cost and take much attention on product appearance. Compared to using questionnaires individually, obtaining humans’ thoughts directly from their brains can accurately grasp the actual preference of consumers, which can provide effective and precious decisions for companies. \ In this study, consumers’ brainwaves which are related to concentration and emotion are extracted by wearing a portable and wireless Electroencephalography (EEG) device. The extracted EEG data are then trained by using perceptron learning algorithm (PLA) to make the judgments of concentration and emotion work well with each subject. They are then applied to the detection and analysis of consumer preference. Finally, the questionnaires are also performed and used as the reference on training process. They are integrated with brainwaves data to create one prediction model which can improve the accuracy significantly. The Partial Least Squares is used to compare the correlation between different factors in the model, to ensure the test can accurately meet consumers’ thoughts

A quantitative analysis of monochromaticity in genetic interaction networks

<p>Abstract</p> <p>Background</p> <p>A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed.</p> <p>Results</p> <p>In this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes.</p> <p>Conclusion</p> <p>In summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).</p