Association between Temperature and Emergency Room Visits for Cardiorespiratory Diseases, Metabolic Syndrome-Related Diseases, and Accidents in Metropolitan Taipei

Objective: This study evaluated risks of the emergency room visits (ERV) for cerebrovascular diseases, heart diseases, ischemic heart disease, hypertensive diseases, chronic renal failure (CRF), diabetes mellitus (DM), asthma, chronic airway obstruction not elsewhere classified (CAO), and accidents associated with the ambient temperature from 2000 to 2009 in metropolitan Taipei. Methods: The distributed lag non-linear model was used to estimate the cumulative relative risk (RR) and confidence interval (CI) of cause-specific ERV associated with daily temperature from lag 0 to lag 3 after controlling for potential confounders. Results: This study identified that temperatures related to the lowest risk of ERV was 26 °C for cerebrovascular diseases, 18 °C for CRF, DM, and accidents, and 30 °C for hypertensive diseases, asthma, and CAO. These temperatures were used as the reference temperatures to measure RR for the corresponding diseases. A low temperature (14°C) increased the ERV risk for cerebrovascular diseases, hypertensive diseases, and asthma, with respective cumulative 4-day RRs of 1.56 (95% CI: 1.23, 1.97), 1.78 (95% CI: 1.37, 2.34), and 2.93 (95% CI: 1.26, 6.79). The effects were greater on, or after, lag one. At 32°C, the cumulative 4-day RR for ERV was significant for CRF (RR = 2.36; 95% CI: 1.33, 4.19) and accidents (RR = 1.23; 95% CI: 1.14, 1.33) and the highest RR was seen on lag 0 for CRF (RR = 1.69; 95% CI: 1.01, 3.58), DM (RR = 1.69; 95% CI: 1.09, 2.61), and accidents (RR = 1.19; 95% CI: 1.11, 1.27). Conclusions: Higher temperatures are associated with the increased ERV risks for CRF, DM, and accidents and lower temperatures with the increased ERV risks for cerebrovascular diseases, hypertensive diseases, and asthma in the subtropical metropolitan

A Maxwell-vector p-wave holographic superconductor in a particular background AdS black hole metric

We study the p-wave holographic superconductor for AdS black holes with planar event horizon topology for a particular Lovelock gravity, in which the action is characterized by a self-interacting scalar field nonminimally coupled to the gravity theory which is labeled by an integer $k$. As the Lovelock theory of gravity is the most general metric theory of gravity based on the fundamental assumptions of general relativity, it is a desirable theory to describe the higher dimensional spacetime geometry. The present work is devoted to studying the properties of the p-wave holographic superconductor by including a Maxwell field which nonminimally couples to a complex vector field in a higher dimensional background metric. In the probe limit, we find that the critical temperature decreases with the increase of the index $k$ of the background black hole metric, which shows that a larger $k$ makes it harder for the condensation to form. We also observe that the index $k$ affects the conductivity and the gap frequency of the holographic superconductors.Comment: 14 pages, 6 figure

DERIVING TECHNOLOGY ROADMAPS WITH TECH MINING TECHNIQUES

Technology monitoring has been a knowledge intensive and time-consuming task for IT managers or domain experts. Tech mining techniques can be used to mitigate these efforts. This paper proposes a technology monitoring framework based on tech mining techniques to facilitate the derivative of information and communication technology (ICT) roadmaps. With this framework, a tech mining engine is able to allocate the most relevant documents which describe a category of technologies. Domain experts were participated in a scan meeting to verify the generated roadmaps based on the selected cluster of documents. The draft roadmaps can be further articulated with domain experts\u27 judgment for technology forecasting and assessment

AI Labor Markets: Toward a Dynamic Skills-Based Approach to Measurement

Artificial intelligence (AI) is transforming the nature of work and reshaping labor markets. Viewing labor as a bundle of skills, recent research has analyzed AI skills and offered important insights about the impacts of AI on labor markets. We add to this on-going discourse and argue that taking a dynamic skill-based approach to measurement is critical: just like the development of AI is emergent and ever-evolving, so are AI skills. Taking stock of the literature, we show that existing studies tend to take a static approach to measuring AI skills, which fails to fully reflect the dynamic phenomenon of AI skills and could cause measurement errors. We propose a dynamic co-occurrence method and demonstrate that it performs better than the extant static methods, which can cause severe Type I and II errors, omit emerging AI skills, and temporally over- and under-estimate the demands for AI skills and jobs

Quantum Dimensionality Reduction by Linear Discriminant Analysis

Dimensionality reduction (DR) of data is a crucial issue for many machine learning tasks, such as pattern recognition and data classification. In this paper, we present a quantum algorithm and a quantum circuit to efficiently perform linear discriminant analysis (LDA) for dimensionality reduction. Firstly, the presented algorithm improves the existing quantum LDA algorithm to avoid the error caused by the irreversibility of the between-class scatter matrix $S_B$ in the original algorithm. Secondly, a quantum algorithm and quantum circuits are proposed to obtain the target state corresponding to the low-dimensional data. Compared with the best-known classical algorithm, the quantum linear discriminant analysis dimensionality reduction (QLDADR) algorithm has exponential acceleration on the number $M$ of vectors and a quadratic speedup on the dimensionality $D$ of the original data space, when the original dataset is projected onto a polylogarithmic low-dimensional space. Moreover, the target state obtained by our algorithm can be used as a submodule of other quantum machine learning tasks. It has practical application value of make that free from the disaster of dimensionality