An integrated surrogate modeling method for fusing noisy and noise-free data

Datasets in engineering applications often contain multiple types of data, i.e., noise-free data, noisy data with known noise variances, and noisy data with unknown noise variances. In this paper, a data fusion method, termed as multi-type data fusion (MTDF) model, is proposed to fully utilize the information provided by each of these types of data. The proposed model strives to capture the underlying trend implied in the multiple types of data better by approximately interpolating the noise-free data while regressing with the noisy data. To evaluate the prediction accuracy of the MTDF model, it is compared with multiple surrogate models including interpolation models, regression models, and multi-fidelity models on both numerical and practical engineering problems. The results show that the proposed MTDF model presents a more outstanding performance than the other benchmark models. The key issues, i.e., the effect of noise level, the effect of the sample size of noise-free data, and the robustness of the MTDF model are also investigated. The results illustrate that the MTDF model possesses satisfactory feasibility, practicality, and stability.

Spatio-Temporal Clustering of Sarawak Malaysia Total Protected Area Visitors

Based on data of visitors to national parks, nature reserves and wildlife sanctuaries in Sarawak, this study’s objective is to use the spatial and temporal analysis to describe the underlying trend and temporal pattern of local and foreign visitors and ultimately infer the temporal distribution of visitors to 18 different TPAs. The second aim of the study is to cluster the visitors according to the location of TPAs using Wards hierarchical clustering method. By comparing average monthly visitors’ count, we observed that the average number of monthly visitors significantly reflects the distribution concentration of visitors based on the spatial map. Findings indicate that the monthly distributions of local and foreign visitors differ according to different TPAs. The spatial and temporal analysis found that local visitors’ arrival is high at the end of the year while foreign visitors showed significant arrival during the months of July, August and September. The Wards minimum variance method was able to cluster TPAs local and foreign visitors into very high, high, medium and low visitor area. This study provides additional information that could contribute to identifying the periods of highest visitor pressure, design measures to manage the concentration of visitors and improve the overall visitors’ experience. The findings of the study are also important to respective local authorities in providing information for planning and monitoring tourism in TPAs. Consecutively, this will ensure sustainability of TPAs resources while protecting their biodiversity.

Critical Discourse Analysis and Rhetorical Tropes in Donald Trump’s First Speech to the UN

Language and politics go hand in hand and learning and comprehending political genre is to learn a language created for codifying, extending and transmitting political discourse in any text/talk. Drawing upon the theoretical framework of Fairclough’s CDA and Rhetoric, the current study aims at investigating Donald Trump’s First Speech, from the point of frequency and functions of some rhetorical strategies (Parallelism, Anaphora and the Power of Three, Antithesis and Expletive, etc.), Nominalization, Passivization, We-groups and Modality as well as Lexical and Textual Analysis, presented to the UN delivered on Sep. 19, 2017. Specifically, the study seeks to determine: (1) how President Trump succeeded in conveying his notions and assumptions to his intended audience, and in convincing and negotiating, (2) how he attempted to explicitly and implicitly pass his attitudes on his targets, and (3) how those orientations, intended notions and assumptions were seamlessly presented to his addressees in discoursal and lexico-grammatical levels; (4) and finally in this underlying trend how he achieved his own ends. The results of the study hope to enhance reading comprehension and writing in academic registers for EFL/ESL students.

The power of visual analytics and language processing to explore the underlying trend of highly popular song lyrics

The number of songs are increasing at a very high rate around the globe. Out of the songs released every year, only the top few songs make it to the billboard hit charts .The lyrics of the songs place an important role in making them big hits combined with various other factors like loudness, liveness, speech ness, pop, etc. The artists are faced with the problem of finding the most desired topics to create song lyrics on. This problem is further amplified in selecting the most unique, catchy words which if added, could create more powerful lyrics for the songs. We propose a solution of finding the bag of unique evergreen words using the term frequency-inverse document frequency (TF-IDF) technique of natural language processing. The words from this bag of unique evergreen words could be added in the lyrics of the songs to create more powerful lyrics in the future.

Fitting the Reproduction number from UK coronavirus case data, and why it is close to 1

We present a method for rapid calculation of coronavirus growth rates and R-numbers tailored to the publicly available data in the UK. The R-number is derived from time-series of case data, using bespoke data processing to remove systematic and errors and stochastic fluctuations. In principle, growth rate can be obtained by differentiating the reported case numbers, but in fact daily stochastic fluctuations disqualify this method. We therefore assume that the case data comprises a smooth, underlying trend which is differentiable and a noise term. The approach produces, up-to-date estimates of the R-number throughout the period of data availability. Our method is validated against published consensus R-numbers from the UK government, and shown to produce comparable results. A significant advantage of our method is that it is stable up to the most recent data, this enables us to make R-number estimates available over two weeks ahead of the published consensus. The short-lived peaks observed in the R-number and case data cannot be explained by a well-mixed model and are suggestive of spread on a localised network. Such a localised spread model tends to give an Rt number close to 1, regardless of how large R0. The case-driven approach is combined with Weight-Shift-Scale (WSS) methods to monitor trends in the epidemic and for medium term predictions. Using case-fatality ratios, we create a narrative for trends in the UK epidemic increased infectiousness of the alpha and delta variants, and the effectiveness of vaccination in reducing severity of infection.

LO35: A province-wide quality improvement collaborative for treatment of children's pain in Alberta's emergency departments

Background: Pediatric pain is often under-treated in emergency departments (EDs), causing short and long-term harm. In Alberta EDs, children's pain outcomes were unknown. A recent quality improvement collaborative (QIC) led by our team improved children's pain care in 4 urban EDs. We then spread to all EDs in Alberta using the Institute for Healthcare Improvement Framework for Going to Full Scale. Aim Statement: To increase the proportion of children <12 years who receive topical anesthetic before needle procedures from 11% to 50%; and for children <17 years with fractures: to 1) increase the proportion receiving analgesia from 31% to 50%; 2) increase the proportion with pain score documentation from 24% to 50%, and 3) reduce time to analgesia from 60 to 30 minutes, within 1 year. Measures & Design: All 97 EDs in Alberta that treat children were invited. Each was asked to form a project team, attend webinars, develop key driver diagrams and perform PDSA tests of change. Sites were given a monthly list of randomly selected charts for audit and entered data in REDCap for upload to a provincial run chart dashboard. Baseline performance measurement informed aims. Measures included proportion of children <12 years undergoing a lab test who received topical anesthetic, and for children <17 years with fracture, the proportion with a pain score, proportion receiving analgesia and median minutes to analgesia. Length of stay and use of opioids were balancing measures. Control charts were used to detect special cause. Interrupted time series (ITS) was performed to assess significance and trends. Evaluation/Results: 36 sites (37%) participated, including rural and urban sites from all regions. 8417 visits were audited. 23/36 sites completed audits before and after tests of change and were analyzed. Special cause occurred for all aims. The proportion receiving topical anesthetic increased from 11% to 30% (ITS p < 0. 001). For children with fractures, the proportion with pain scores increased from 24% to 34% (ITS p = 0.21, underlying trend present), proportion receiving analgesic medication increased from 31% to 39% (ITS p = 0.41, underlying trend present) and minutes to analgesia decreased from 60 to 28 (ITS p < 0. 01). There was no increase in length of stay or use of opioid medications. Discussion/Impact: A pragmatic approach encouraging locally led change was well-received and key to success. The QIC method shows promise for improving outcomes in diverse EDs across large geographic areas. Next steps include further spread and sustainability measurement.

Zooplankton abundance trends and patterns in Shelikof Strait, western Gulf of Alaska, USA, 1990–2017

A multivariate approach was used to analyze spring zooplankton abundance in Shelikof Strait, western Gulf of Alaska. abundance of individual zooplankton taxa was related to environmental variables using generalized additive models. The most important variables that correlated with zooplankton abundance were water temperature, salinity and ordinal day (day of year when sample was collected). A long-term increase in abundance was found for the calanoid copepod Calanus pacificus, copepodite stage 5 (C5). A dynamic factor analysis (DFA) indicated one underlying trend in the multivariate environmental data that related to phases of the Pacific Decadal Oscillation. DFA of zooplankton time series also indicated one underlying trend where the positive phase was characterized by increases in the abundance of C. marshallae C5, C. pacificus C5, Eucalanus bungii C4, Pseudocalanus spp. C5 and Limacina helicina and declines in the abundance of Neocalanus cristatus C4 and Neocalanus spp. C4. The environmental and zooplankton DFA trends were not correlated over the length of the entire time period; however, the two time series were correlated post-2004. The strong relationship between environmental conditions, zooplankton abundance and time of sampling suggests that continued warming in the region may lead to changes in zooplankton community composition and timing of life history events during spring.

The dangers of ignoring underlying trends in before-and-after studies – A cautionary tale using hip fracture mortality data

Background: Before-and-after studies are a valuable study design in situations where randomization is not feasible. These studies measure an outcome both before and after an intervention and compare the outcome rates in both time periods to determine the effectiveness of the intervention. Before-and-after studies do not involve a contemporaneous control group and must, therefore, take into account any underlying secular trends to separate the effect of the intervention from any pre-existing trend. Methods: To illustrate the importance of accounting for underlying trends, we performed a before-and-after study assessing 30-day mortality in hip fracture patients without any actual intervention, and instead designated an arbitrarily chosen time point as our ‘intervention’. We then analysed the data first disregarding and then incorporating the pre-existing underlying trend. We did this to show that even intervention of nothing may be spuriously interpreted to have an effect if the before-and-after study design is incorrectly analysed. Our study involved a secondary analysis of routinely collected data on 30-day mortality following hip fracture in our institution. Results: We found a secular trend in our data showing improving 30-day mortality in hip fracture patients in our institution. We then demonstrated that disregarding this underlying trend showed that our intervention of nothing ‘resulted’ in a significant 54% decrease in mortality, from 6.7% in the ‘before’ period to 3.1% in the ‘after’ period ( p < 0.0008). Though the 30-day mortality rate decreased during the ‘after’ period, the decrease was not significantly different from the underlying trend in the ‘before’ period, projected onto the ‘after’ period. When we accounted for the underlying trend in our analysis, the impact of the intervention (nothing) on 30-day mortality was no longer apparent (incidence rate ratio 0.75, 95% confidence interval 0.32–1.78; p = 0.5). Conclusion: Our study highlights the importance of appropriate measurement and consideration of underlying trends when analysing data from before-and-after studies and illustrates what can happen should researchers neglect this important step.

Studies in Informatics and Control: A Bibliometric Analysis from 2008 to 2019

As an international scientific journal, Studies in Informatics and Control (SIC ) covers the field of Information Technology (IT) and topics related to research areas, as well as important applications in IT, with particular emphasis on Advanced Automatic Control, Modeling and Optimization. SIC has greatly contributed to the areas where it involves since its first online publication way back in 1992. This paper sets out to analyze the structure and the underlying trend of the journal by making use of bibliometric methods. Firstly, the classical indicators are provided to illustrate the performance of the journal. This current study performs an in-depth analysis of the most productive and the most influential authors, institutions, and countries/regions, as well as the most cited research works published in SIC. Secondly, the visualization tools VoS viewer and CiteSpace are used to create scientific maps that may explain the structure of the journal in an intuitionistic way. In the science mapping, the co-citation maps, and the co-authorship networks of various items (such as authors, institutions, and countries/regions) are conducted. Also, the bursts detection of these items are derived. The co-occurrence of keywords and their bursts detection and timeline review are shown, respectively. Finally, some conclusions are given. This paper provides a comprehensive and visual understanding of this well-regarded scientific journal.