Compounded wind gusts and maximum temperature via semiparametric copula in the risk assessments of power blackouts and air conditioning demands for major cities in Canada.

A semiparametric copula joint framework was proposed to model wind gust speed (WGS) and maximum temperature (MT) in Canada, using Gaussian kernel density estimation (GKDE) with parametric copulas. Their joint probability estimates allow for a better understanding of the risk of power blackouts and the demand for air conditioning in the community. The bivariate framework used two extreme sample groups to define extreme pairs at different time lags, i.e., 0 to ± 3 days, annual maximum WGS (AMWGS) and corresponding MT and annual highest MT (AHMT) and corresponding WGS. A thorough model performance comparison indicated that GKDE outperformed the parametric models in defining the marginal distribution of selected univariate series. Significant positive correlations were observed among extreme pairs, except for Calgary and Halifax stations, with inconsistent correlation variations based on selected cities and lag time. Various parametric 2-D copulas were selected to model the dependence structure of bivariate pairs at different time lags for selected stations. AMWGS or AHMT events, when considered independently, would be stressful for all stations due to high estimated quantiles with low univariate RPs. The bivariate events exhibited lower AND-joint RPs with moderate to high design quantiles, indicating a higher risk of power blackouts and heightened air-conditioning demands, which varied inconsistently with time lags across the station. The bivariate AMWGS and corresponding MT events would be stressful in Regina, Quebec City, Ottawa, and Edmonton, while AHMT and corresponding WGS events in Toronto, Regina, and Montreal. Conversely, Vancouver poses a lower risk of joint action of pairs AHMT and corresponding WGS events. These hazard statistics can help in better planning for community well-being during extreme weather.

Talking Realities: Audio Guides in Virtual Reality Visualizations.

Building upon the ideas of storytelling and explorable explanations, we introduce Talking Realities, a concept for producing data-driven interactive narratives in virtual reality. It combines an audio narrative with an immersive visualization to communicate analysis results. The narrative is automatically produced using template-based natural language generation and adapts to data and user interactions. The synchronized animation of visual elements in accordance with the audio connects the two representations. In addition, we discuss various modes of explanation ranging from fully guided tours to free exploration of the data. We demonstrate the applicability of our concept by developing a virtual reality visualization for air traffic data. Furthermore, generalizability is exhibited by sketching mock-ups for two more application scenarios in the context of information and scientific visualization.

Kori: Interactive Synthesis of Text and Charts in Data Documents.

Charts go hand in hand with text to communicate complex data and are widely adopted in news articles, online blogs, and academic papers. They provide graphical summaries of the data, while text explains the message and context. However, synthesizing information across text and charts is difficult; it requires readers to frequently shift their attention. We investigated ways to support the tight coupling of text and charts in data documents. To understand their interplay, we analyzed the design space of chart-text references through news articles and scientific papers. Informed by the analysis, we developed a mixed-initiative interface enabling users to construct interactive references between text and charts. It leverages natural language processing to automatically suggest references as well as allows users to manually construct other references effortlessly. A user study complemented with algorithmic evaluation of the system suggests that the interface provides an effective way to compose interactive data documents.

Impact of Green financing, FinTech, and financial inclusion on energy efficiency.

In modern times, enhancing energy efficiency is one of the core agenda items for the economies to progress the world. United Nations general assembly has recommended increasing energy efficiency by considering the SDG-7 guidelines. In the contemporary period, E7 economies are deficient in producing the financial resources to ensure the availability of funds for the acquisition of energy efficiency. COVID-19 crises, lack of resilience in economies, devastating fiscal burdens, and tight monetary conditions of E7 economies are the major barriers. To resolve such issues, some innovative financing techniques, such as green financing, financial inclusion, and FinTech, were suggested to investigate. However, this research tested the empirical role of financial inclusion, green financing, and FinTech on the energy efficiency of E7 economies. Study findings have shown a significant role of such financing techniques on energy efficiency. Comparatively, green financing is found most fitting and highly supportive financing tool for energy efficiency among the three. The differences in attributes, financing mechanism, funds flow system, transection systems, and variation in support by the financial institution are the main reasons that lessen the role of financial inclusion and FinTech for energy efficiency. However, theorists must revisit the transaction system of FinTech and financial inclusion parameters like green bonds for energy efficiency attainment. Policymakers are suggested to develop viable and energy system-friendly policies to grant green finance to the energy systems of E7 economies, as conveniently as possible.

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions.

The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.

How Visualization PhD Students Cope with Paper Rejections.

We conducted a questionnaire study aimed towards PhD students in the field of visualization research to understand how they cope with paper rejections. We collected responses from 24 participants and performed a qualitative analysis of the data in relation to the provided support by collaborators, resubmission strategies, handling multiple rejects, and personal impression of the reviews. The results indicate that the PhD students in the visualization community generally cope well with the negative reviews and, with experience, learn how to act accordingly to improve and resubmit their work. Our results reveal the main coping strategies that can be applied for constructively handling rejected visualization papers. The most prominent strategies include: discussing reviews with collaborators and making a resubmission plan, doing a major revision to improve the work, shortening the work, and seeing rejection as a positive learning experience.

Determining the knowledge of primary healthcare (PHC) physicians and factors influencing practices in the implementation of the catch-up vaccination program in PHC clinics in Dammam, Saudi Arabia.

OBJECTIVES: To determine the knowledge of primary healthcare (PHC) physicians and factors influencing implementation of the catch-up vaccination program in PHC clinics in Dammam, Saudi Arabia. Methods: A cross-sectional study was conducted with a sample of 136 general and family physicians between October 2018 and August 2019 in PHC clinics. A self-administered questionnaire related to knowledge, practices, and factors affecting their practices regarding vaccination was given to participants. Their responses were assessed for level of knowledge, practices regarding the vaccination program, and factors affecting their practices, and then compared between family and general physicians. Results: The majority of physicians (60%) surveyed had good knowledge of the catch-up vaccination program and no significant difference was observed between family and general physicians. Most physicians (60%) demonstrated correct knowledge of vaccination schedules and gaps between vaccines; no significant differences were observed between family and general physicians, except for the next scheduled child visit after 3 months, for the 18-month vaccination. A statistically significant difference was observed between family and general physicians related to attending educational lectures and seeing children younger than 5 years. General physicians were more involved in the vaccination program. Conclusion: The overall knowledge and practices of the catch-up vaccination program was satisfactory among PHC physicians. However, a lack of family physicians' involvement in the vaccination program was observed that needs to be addressed in their training program.

Exploranative Code Quality Documents.

Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope.

VIS Author Profiles: Interactive Descriptions of Publication Records Combining Text and Visualization.

Publication records and collaboration networks are important for assessing the expertise and experience of researchers. Existing digital libraries show the raw publication lists in author profiles, whereas visualization techniques focus on specific subproblems. Instead, we look at publication records from various perspectives mixing low-level publication data with high-level abstractions and background information. This work presents VIS Author Profiles, a novel approach to generate integrated textual and visual descriptions to highlight patterns in publication records. We leverage template-based natural language generation to summarize notable publication statistics, evolution of research topics, and collaboration relationships. Seamlessly integrated visualizations augment the textual description and are interactively connected with each other and the text. The underlying publication data and detailed explanations of the analysis are available on demand. We compare our approach to existing systems by taking into account information needs of users and demonstrate its usefulness in two realistic application examples.

Bemisia tabaci-mediated facilitation in diversity of begomoviruses: Evidence from recent molecular studies.

Begomoviruses are considered as one of the most notorious plant viruses worldwide, which cause substantial economic losses to various field crops. Management of begomoviruses has become a challenge due to the continuous evolution and the emergence of new strains. Bemisia tabaci is globally known to be the key vector of begomoviruses, having relatively high reproductivity, fast dispersal ability, high survival rate due to its polyphagous nature and high resistance to various groups of insecticides. Continuous transmission of begomoviruses by the vector has led to the development and spread of epidemics of various diseases worldwide. In this review, we have critically analyzed the various dynamics which facilitate the diversity of begomoviruses through their vector. The interaction of begomovirus-whitefly leads to continuous research activities regarding management of both virus and its vector, thus opening exciting new horizons to formulate potential control strategies to ensure a disease-free cropping environment.

Evaluation of outpatient service quality in Eastern Saudi Arabia. Patient's expectations and perceptions.

OBJECTIVES: To investigate perceptions and expectations of patients regarding hospital outpatient services by using a service quality gap model and factors influencing such gaps. METHODS: In this cross-sectional descriptive study conducted between October and November 2014 in the outpatient waiting areas of a hospital in the Eastern Province of Saudi Arabia, a sample of 306 patients was selected by convenience sampling technique. The data was collected through an Arabic version of the service quality (SERVQUAL) questionnaire consisting of 2 parts: patients' demographic characteristics, and 22 items scales of patients' expectations and perceptions of SERVQUAL. The data was analyzed by confirmatory factor analysis, independent, and paired t samples tests and one way analysis of variance test RESULTS: The results showed that the proposed model for service quality dimensions had a good fit by satisfying the recommended values. The patients' expectations exceeded perceptions in all service quality dimensions indicating statistically significant service quality gaps (t=26.3, p less than 0.000). Findings revealed that the empathy dimension contributed most patients' expectations (4.7 ± 0.5) and perceptions (3.7 ± 0.8) scores, and responsiveness contributed least to expectations (4.5 ± 0.6) and perceptions (3.2 ± 0.8) scores. Prompt services showed highest service quality gap, while observation of privacy showed the smallest service quality gap in the statements. The study showed a significant association between gender, age, education, multiple visits, and service quality dimensions. CONCLUSION: The proposed model is valid and reliable and significant service quality gaps of all 5 dimensions need to be prioritized and addressed by focused improvement efforts of hospital management.

Venous thromboembolism risk and prophylaxis in a Saudi hospital.

OBJECTIVE: To evaluate the risk factors and physician's compliance to American College of Chest Physicians (ACCP) guidelines recommendations for venous thromboembolism (VTE) prevention at our hospital. METHODS: This retrospective cohort study was conducted at King Abdulaziz Hospital, Al-Ahsa, Saudi Arabia from November 2009 to December 2009. We used the American College of Chest Physicians (ACCP) 2008 guidelines and Caprini's scores to assess VTE risk and to determine whether patients had received recommended prophylaxis. All hospital in-patients aged 15 years or above were assessed for risk of VTE by reviewing the hospital chart. A data sheet was developed to obtain the data on demographics, VTE prophylaxis medication, dose, route, duration, and associated risk factors. The primary endpoint was the rate of appropriate thromboprophylaxis. RESULTS: Nine hundred and sixty-eight patients were included. The mean age was 40 +/- 18.7 years, and 647 (66.8%) were women. According to the ACCP criteria, 547 (56.5%) patients were at risk for VTE. Of 210 patients that qualified for prophylaxis, 117 (55.7%) received some form of prophylaxis. However, 46 (39.3%) of them received ACCP-recommended VTE prophylaxis. In contrast, 25.6% of patients with no risk, according to Caprini score, had thromboprophylaxis prescribed. CONCLUSION: This study demonstrates that only a small proportion of eligible patients received the recommended VTE prophylaxis. Efforts should be made to develop strategies to improve patient safety practices.

Heart rate variability fraction--a new reportable measure of 24-hour R-R interval variation.

BACKGROUND: The scatterplot of R-R intervals has several unique features. Its numerical evaluation may produce a new useful index of global heart rate variability (HRV) from Holter recordings. METHODS: Two-hundred and ten middle-aged healthy subjects were enrolled in this study. The study was repeated the next day in 165 subjects. Each subject had a 24-hour ECG recording taken. Preprocessed data were transferred into a personal computer and the standard HRV time-domain indices: standard deviation of total normal R-R intervals (SDNN), standard deviation of averaged means of normal R-R intervals over 5-minute periods (SDANN), triangular index (TI), and pNN50 were determined. The scatterplot area (0.2-1.8 second) was divided into 256 boxes, each of 0.1-second interval, and the number of paired R-R intervals was counted. The heart rate variability fraction (HRVF) was calculated as the two highest counts divided by the number of total beats differing from the consecutive beat by <50 ms. The HRVF was obtained by subtracting this fraction from 1, and converting the result to a percentage. RESULTS: The normal value of the HRVF was 52.7 +/- 8.6%. The 2-98% range calculated from the normal probability plot was 35.1-70.3%. The HRVF varied significantly with gender (female 48.7 +/- 8.4% vs male 53.6 +/- 8.6%, P = 0.002). The HRVF correlated with RRI (r = 0.525) and showed a similar or better relationship with SDNN (0.851), SDANN (0.653), and TI (0.845) than did the standard HRV measures with each other. Bland-Altman plot showed a good day-by-day reproducibility of the HRVF, with the intraclass correlation coefficient of 0.839 and a low relative standard error difference (1.8%). CONCLUSION: We introduced a new index of HRV, which is easy for computation, robust, reproducible, easy to understand, and may overcome the limitations that belong to the standard HRV measures. This index, named HRV fraction, by combining magnitude, distribution, and heart-rate influences, might become a clinically useful index of global HRV.