Evaluating Research Trends from Journal Paper Metadata, Considering the Research Publication Latency

Investigating the research trends within a scientific domain by analyzing semantic information extracted from scientific journals has been a topic of interest in the natural language processing (NLP) field. A research trend evaluation is generally based on the time evolution of the term occurrence or the term topic, but it neglects an important aspect—research publication latency. The average time lag between the research and its publication may vary from one month to more than one year, and it is a characteristic that may have significant impact when assessing research trends, mainly for rapidly evolving scientific areas. To cope with this problem, the present paper is the first work that explicitly considers research publication latency as a parameter in the trend evaluation process. Consequently, we provide a new trend detection methodology that mixes auto-ARIMA prediction with Mann–Kendall trend evaluations. The experimental results in an electronic design automation case study prove the viability of our approach.

Probabilistic anomaly trend detection for cable-supported bridges using confidence interval estimation

To rate uncertainties within anomaly detection course for large span cable-supported bridges, a probabilistic approach is developed based on confidence interval estimation of extreme value analytics. First, raw signals from structural health monitoring system are pre-processed, including missing data imputation using moving time window mean imputation approach and thermal response separation through multi-resolution wavelet-based method. Then, an energy index is extracted from time domain signals to enhance robust of detection performance. A resampling-based method, namely the bootstrap, is adopted herein for confidence interval estimation. Four confidence levels are defined for the anomaly trend detection in this study, namely 95%, 80%, 50%, and 20%. Finally, the effectiveness of the proposed anomaly trend detection methodology is validated by using in-situ cable force measurements from the Nanjing Dashengguan Yangtze River Bridge. As a result, the four-level anomaly detection triggers are determined by using the confidence interval estimation based on cable force measurements in 2007, which are 58,671, 48,862, 42,499 and 39,035, respectively. Subsequently, three cases are presented, which are spike detection, overloading vehicle detection and snow disaster detection. Through the spike detection, it is verified that energy index is capable to tolerate signal spikes. Three overloading events are simulated to conduct overloading vehicle detections. As a result, the three overloading events are detected successfully associated with different confidences. Snow disaster is detected with a more than 80% confidence based on the field measurements during the snow storm time window.

دراسة التغير النسبي لقيم الاوزون في بغداد

المستخلص: يعد علم المناخ من العلوم التطبيقية التي قلما نجد منافسا له في مجال ارتباطه الوثيق بحياة الانسان ومظاهر نشاطه المختلفة , كونه من العوامل الاساسية المؤثرة في النشاطات الحياتية لمختلف الكائنات الحية , وعلية فهو يمكن الباحثين من فهم مختلف الظواهر الحيوية وتفسيرها بعد فهم محيطها , وبالتالي وضع استراتيجيات , تكون بمثابة واق للبيئة من خطر التدهور والتلوث , وعند ذلك يكون قد حافظ على الحياة الطبيعية . يرمي البحث الى تحديد قيم الأوزون الكلية في العراق , من خلال استخدام الطرق الاحصائية واختيار أهم الطرائق التي يمكن من خلالها حساب الأوزون في منطقة الدراسة , وبالتالي تحليل التغير النسبي في منطقة الدراسة تم تطبيق هذا البحث في محطة (بغداد) , لذ سوف يتم في هذا الفصل الاعتماد على استخدام أسلوب الاتجاه العام ومعدل التغير النسبي من اجل إيضاح التغيرات الحاصلة في قيم الأوزون بمنطقة الدراسة, في وللكشف عن الاتجاه العام ومعدل التغير منطقة الدراسة (Trend Detection) تم حساب الاتجاه العام للمعدلات السنوية للسلاسل الزمنية (لعناصر المناخ), وتم التعبير عن معامل الاتجاه بالنسبة المئوية لمجمل المتغيرات في عناصر المناخ ,وكذلك بالنسبة لمعدلات التغير السنويAnnual Change)) وفق المعادلة الآتية :. حيث ان : c = معدل التغير النسبي السنوي* bi = معامل الاتجاه y = المتوسط الحسابي ويمكن استخراج ( **bi ) من المعادلة التالية: `X2-`X1=الفرق بين الوسطين T2-T1= الفرق بين الزمنين

Comparison of Bayesian quantile and frequency-oriented regressions in studying the trend of discharge changes in several hydrometric stations of Gorganroud basin in Iran.

Climate change causes fluctuations in temperature and precipitation. As a result, it affects the discharge of rivers, the most important consequence of which is the tendency toward extreme events such as torrential rains and widespread droughts. River discharge is one of the most important climatic and hydrological parameters. Investigating the changes in this parameter is one of the main prerequisites in the management and proper use of water resources and rivers. Most trend detection studies are based on analyzing changes in the mean or middle of the data. They do not provide information on how changes occur in different data ranges. Therefore, to investigate parameter changes in a different range of the data series, various regression models were proposed. Frequentist quantile regression and Bayesian quantile regression models were used to estimate their trend and trend slope in different quantiles of discharge in different seasons of the year for Arazkouseh, Tamar, and Galikesh stations of Gorganroud basin in northern Iran with the statistical period of 1346–1396 (1966–2016). The results show that in most seasons of the year, high discharge rates for all 3 stations have decreased with a steep slope, and only in summer, Tamar and Galikesh stations have had an increasing trend, but low discharge rates have not changed significantly. Spatially, the discharge values at Arazkouseh station have a decreasing trend with a higher slope rate, and in terms of time, the most decreasing trend has been in spring. Comparing the models also shows that the Bayesian quantile regression model provides more accurate and reliable results than the frequency-oriented quantile regression model. In general, quantile regression models are useful for predicting and estimating extreme high and low discharge changes for better management to reduce flood and drought damage.