Time-series Charts Research Articles

Portfolio Evolution Analysis for SPL Scoping: Unveiling the dynamics with dedicated time-series dashboards

Software Product Line Engineering (SPLE) is a recognized methodology for systematically developing reusable software components and tailored software products for specific market segments. A critical activity within domain engineering is SPL Scoping, aimed at profiling the SPL boundaries and fitting this market segment. Yet, this market is a moving target, which requires SPLs to be proactive in anticipating and addressing future challenges and opportunities. In this setting, rather than all the possible products potentially derived from the SPL’s Feature Model (i.e., the potential portfolio), the subset of products actually deployed is a better indicator of market adoption and customer preferences, and ultimately, of the SPL’s fitness to the market. This study characterizes the phenomenon of ‘deployed portfolio evolution’ in terms of the size and frequency of portfolio changes. We then elaborate on the advantages of utilizing this data source and the necessity for dedicated tools for Portfolio Evolution Analysis integrated within variability managers. To achieve this, we introduce the functional requirements, implementation, and architecture of PortfolioScan, a dashboard for Portfolio Evolution Analysis built on top of pure::variants. The dashboard enables the evaluation of feature usage through time-series charts such as temporal heat maps, line charts, and also a scoping-criteria comparison matrix. Evaluation results (n=13) indicate a positive perception. Practitioners also recognize the benefits of Portfolio Evolution Analysis for Testing Resource Optimization and Configuration Assistance. The ultimate goal of the study is to establish a basis for more responsive and market-oriented decision-making in SPL tasks.

DIGITAL PAYMENTS AND FRAUD CONNECTION: INSIGHTS FROM THE INDIAN ECONOMY

In the dynamic world of digital transactions in India, the symbiotic relationship between convenience and vulnerability has become increasingly apparent. This study aims to know how digital payment and fraud connect in Indian economy. The primary objective of this study is to know the relationship between financial fraud with digital payment infrastructures, value of digital payments, and volume of digital payment. The data is collected through RBI’s official quarterly reports from September 2022 to May 2023. The collected time series data for all the variables has been analyzed for descriptive statistics. These series were tested for stationarity using Augmented Dickey–Fuller test (ADF - unit root test), Correlogram, and time series chart. The correlation and regression analysis has been performed on this time series data using SPSS and EViews software. From the correlation results this study reveals that there is no significant correlation between value and volume of fraud with payment infrastructure, value of digital payment, and volume of digital payment. The value of UPI transactions has a significant negative impact on digital payment fraud, indicating a potential protective effect.

Reducing Stimulant Prescribing Error: A Quality Improvement Initiative in Pediatric Outpatient Setting.

To evaluate the impact of the Songklanagarind ADHD Multidisciplinary Assessment and Care Team for Quality Improvement (SAMATI) initiative on reducing stimulant prescribing errors in a pediatric outpatient setting. A retrospective study examined attention deficit hyperactivity disorder (ADHD) registry data from January 2017 to June 2023 to assess the impact of the SAMATI initiative, implemented in early 2020. This initiative, integrating multiple components such as audit and feedback, clinical pharmacist involvement, and Electronic Medical Record utilization, aimed to enhance ADHD medication management. Using interrupted time series and control chart analyses, the study evaluated the initiative's effect on reducing stimulant prescribing errors. Additionally, parental satisfaction was measured to gauge the initiative's overall success. Out of 282 patients enrolled, 267 were included in the final analysis after exclusions. Post-intervention analysis showed significant reductions in various prescribing errors per thousand prescriptions: prescribing without concern drug-condition interaction (443 to 145, p < 0.001), prescribing without adequate monitoring (115 to 14, p < 0.001), lack of regular office visits (98 to 21, p = 0.007), and inappropriate dosage (66 to 14, p = 0.05). Medication errors severity classification also showed significant changes, with notable decreases in classes C and D errors. Parental satisfaction improved from 84% to 95%. The SAMATI initiative significantly reduced stimulant prescribing errors and enhanced parental satisfaction in ADHD care management. This study demonstrates the potential of comprehensive quality improvement strategies in improving medication management in pediatric healthcare. Further research in diverse settings is warranted to confirm these findings.

An agent-based simulation and logistics optimization model for managing uncertain demand in forest supply chains

This paper aims to model and minimize transportation costs in collecting tree logs from several regions and delivering them to the nearest collection point. This paper presents agent-based modeling (ABM) that comprehensively encompasses the key elements of the pickup and delivery supply chain model and presents the units as autonomous agents communicating. The modeling combines components such as geographic information systems (GIS) routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. ABM models the entire pickup and delivery operation, and modeling outcomes are presented by time series charts such as the number of trucks in use, facilities inventory, and travel distance. In addition, various simulation scenarios are used to investigate potential facility locations and truck numbers and determine the optimal facility location and fleet size.

A Correlation Analysis and Visualization of Climate Change using Post-Disaster Heterogeneous Datasets

&lt;p&gt;There are numerous geo-climatic and human factors that contribute to the occurrence of natural disasters in the real-world scenario. Besides the study of causes and preconditions of such calamities, post-disaster analysis is essential for the efficient management of the disaster situation. This process needs timely and accurate data in light of the increasing frequency and severity of climate change-related extreme weather events. The analysis of disaster data involves the challenging task of integrating multiple heterogeneous sources, data ingestion and visualization. This paper aims at providing a three-dimensional analytical view of disaster data as time-series charts and a statistical model to evaluate the correlation between the occurrence of disasters, climate change and the corresponding economic damages (as a percentage of GDP). The created corpus consists of two unified heterogeneous datasets. The first one forms the basis for exploratory data analysis involving visualization charts for disaster-related factors. The second dataset involves temperature anomaly information which allows for climate change analysis. The integration of the above datasets provides scope for correlation analysis using three different variations of coefficients. Therefore, statistical methodologies are leveraged to play important role in managing disasters, from preparation to recovery and reporting. The graphical representations provide insights on regional trends that follow, related to factors such as the proportion of each type of disaster in the various losses incurred. Therefore, obtaining reliable information about the population, the economy and climate are crucial both for risk management and preparedness and for responding to disasters. The research puts forth a detailed statistical methodology with a spatial dimension to study the impacts on the economy and infrastructure in the aftermath of a disaster thereby ensuring specialized assistance. The inference of the analysis confirmed a positive correlation between climate change and occurrence of natural disasters. Therefore, statistical evidence of an important phenomenon like climate change affecting natural disasters brings awareness among the population in the society to be more environmentally responsible.&lt;/p&gt;&#x0D;

Fractal characteristics of the middle reach of the Paraná River floodplain during extreme hydrological events

AbstractThe hydrological events that occur on floodplains are largely defined by precipitation and runoff inputs interacting with their geomorphological characteristics. In the middle Paraná River (MPR), hydrological events have traditionally been studied using water level time series and topographic charts or satellite images of certain floodplain sectors, using the same scale throughout the study area. Different zones of the floodplain were integrated using fractal dimension (FD) to better understand hydrological processes. FD is a useful tool used to characterize the geomorphological complexity of river networks in watersheds. The box‐counting method was applied in the MPR floodplain. The result showed that as water levels increase, FD decreases, thereby indicating the direct relation with the hydrological attributes of the floodplain (e.g., duration, magnitude, recurrence of the flood event, and connectivity). This relation constitutes the basis of a new approach of the analysis of hydrological events and their effect on the MPR floodplain. This research provides fundamental information obtained from two hydrological stages and different spatial scales which may complement the method designed to accurately understand the hydrogeomorphological processes that operate on the Paraná River floodplain. It provides a simple parameter that can be used to evaluate the role that each floodplain section plays in attenuating the hydrograph recorded at each gauge analyzed along the river. This information will be useful in the implementation of management policies directed to the mitigation of future hydrological threats generated by alterations in the hydrological regime due to hydroclimatic variations or watershed alterations. The results further strengthen the basis for integrated sustainable planning of the main channel of the Paraná River, its floodplain, and the resources provided by this ecosystem.

Optimizing Transfer of Postcesarean Patients to Postoperative Ward Through a Quality Improvement (QI) Project: Curtailing Delays, Improving Care.

Close monitoring of patients in the first 2 hours after cesarean delivery (CD) is crucial. Delays in shifting of the post-CD patients led to a chaotic environment in the postoperative ward, suboptimal monitoring, and inadequate nursing care. Our aim was to increase the percentage of post-CD patients shifted from transfer trolley to bed within 10 minutes of arrival in the postoperative ward from a baseline of 64% to 100%, and to maintain that rate for more than 3 weeks. A quality improvement team including physicians, nurses, and workers was constituted. Problem analysis revealed lack of communication among the caregivers as the main cause of delay. The percentage of post-CD patients shifted from trolley to bed within 10 minutes of being wheeled into the postoperative ward out of the total number of post-CD patients transferred from the operation theater to the postoperative ward was taken as the outcome indicator for the project. Multiple Plan-Do-Study-Act cycles based on the Point of Care Quality Improvement methodology were undertaken to achieve the target. Main interventions were: 1) written information of patient being transferred to operation theater for CD sent to the postoperative ward; 2) stationing of a duty doctor in the postoperative ward; and 3) keeping a buffer of 1 vacant bed in the postoperative ward. The data were plotted weekly as a dynamic time series chart and signals of change were observed. Eighty-three percent (172 out of 206) of women were shifted in time by 3 weeks. After Plan-Do-Study-Act 4, the percentages kept improving leading to a median shift from 85.6% to 100% after 10 weeks post-initiation of the project. Sustainment was confirmed by continuing observations for 6 more weeks to ensure that the changed protocol was assimilated in the system. We found that all women were shifted within 10 minutes of their arrival in postoperative ward from trolley to bed. Providing high-quality care to patients must be a priority for all health care providers. High-quality care is timely, efficient, evidence based, and patient-centric. Delays in transfer of postoperative patients to the monitoring area can be detrimental. The point of Care Quality Improvement methodology is useful and effective in solving complex problems by understanding and fixing the various contributory factors one by one. Reorganization of processes and available manpower without any extra investment in terms of infrastructure and resources is pivotal for long term success of a quality improvement project.

The Great Wall Vibration Monitoring of Traffic and Natural Hazards Using MEMS Accelerometers.

The stability of the Great Wall is mainly affected by traffic vibrations and natural hazards, such as strong winds, heavy rainfall, and thunderstorms, which are extremely harmful to the safety of the Great Wall. To determine the impact of the above factors on the Great Wall, a comparative analysis based on MEMS (micro-electro-mechanical system) accelerometer data was conducted between the non-impacts and the impacts of the above factors. An analysis of the relationship between vibration acceleration and each potential hazard based on a visual time series chart was presented using the data of accelerometers, traffic video, meteorology, rainfall, and wind. According to the results, traffic vibration is one of the primary dangerous factors affecting the stability of the Great Wall, Moreover, the intensity of the vibrations increases with the traffic flow. Thunderstorms also influence the stability of the Great Wall, with enhanced thunderstorm excitation resulting in increased vibration displacement. Furthermore, wind load is an influencing factor, with average wind speeds greater than 9 m/s significantly affecting the stability of the Great Wall. Rainfall has no impact on the stability of the Great Wall in the short term. This research can provide important guidance for risk assessment and protection of the Great Wall.

Sustaining Long-term Asthma Outcomes at a Community and Tertiary Care Pediatric Hospital.

Implementing asthma Clinical Practice Guidelines (CPG) have been shown to improve length of stay (LOS) and readmission rates on a short-term basis at both tertiary care and community hospital settings. Whether these outcomes are sustained long term is not known. The goal of this study was to measure the long-term impact of CPG implementation at both tertiary and community sites in 1 hospital system. A retrospective study was conducted using the Pediatric Health Information System database. LOS and 7- and 14-day emergency department (ED) revisit and readmission rates from 2009 to 2020 were compared pre and post implementation of asthma CPG in 2012 at both sites. Implementation involved electronic order sets, early metered dose inhaler introduction, and empowering respiratory therapists to wean per the bronchodilator weaning protocol. Interrupted time series and statistical process control charts were used to assess CPG impact. Implementation of asthma CPG was associated with significant reductions in the variability of LOS without impacting ED revisit or readmission rates at both the tertiary and community sites. Secular trends in the interrupted time series did not demonstrate significant impact of CPG on LOS. However, the overall trend toward decreased LOS that started before CPG implementation was sustained for 7 years after CPG implementation. Early metered dose inhaler introduction, respiratory therapist-driven bronchodilator weaning, and electronic order sets at both the community and tertiary care site led to a significant reduction in the variation of LOS, without impacting ED revisit or readmission rate.

Worldwide Correlations Support COVID-19 Seasonal Behavior and Impact of Global Change.

Many viral diseases exhibit seasonal behavior and can be affected by environmental stressors. Using time-series correlation charts extrapolated from worldwide data, we provide strong support for the seasonal development of COVID-19 regardless of the immunity of the population, behavioral changes, and the periodic appearance of new variants with higher rates of infectivity and transmissibility. Statistically significant latitudinal gradients were also observed with indicators of global change. Using the Environmental Protection Index (EPI) and State of Global Air (SoGA) metrics, a bilateral analysis of environmental health and ecosystem vitality effects showed associations with COVID-19 transmission. Air quality, pollution emissions, and other indicators showed strong correlations with COVID-19 incidence and mortality. Remarkably, EPI category and performance indicators also correlated with latitude, suggesting cultural and psychological diversity in human populations not only impact wealth and happiness but also planetary health at latitudinal level. Looking forward, we conclude there will be a need to disentangle the seasonal and global change effects of COVID-19 noting that countries that go against the health of the planet affect health in general.

An Algorithm to Detect Endangered Cultural Heritage by Agricultural Expansion in Drylands at a Global Scale

This article presents AgriExp, a remote-based workflow for the rapid mapping and monitoring of archaeological and cultural heritage locations endangered by new agricultural expansion and encroachment. Our approach is powered by the cloud-computing data cataloguing and processing capabilities of Google Earth Engine and it uses all the available scenes from the Sentinel-2 image collection to map index-based multi-aggregate yearly vegetation changes. A user-defined index threshold maps the first per-pixel occurrence of an abrupt vegetation change and returns an updated and classified multi-temporal image aggregate in almost-real-time. The algorithm requires an input vector table such as data gazetteers or heritage inventories, and it performs buffer zonal statistics for each site to return a series of spatial indicators of potential site disturbance. It also returns time series charts for the evaluation and validation of the local to regional vegetation trends and the seasonal phenology. Additionally, we used multi-temporal MODIS, Sentinel-2 and high-resolution Planet imagery for further photo-interpretation of critically endangered sites. AgriExp was first tested in the arid region of the Cholistan Desert in eastern Pakistan. Here, hundreds of archaeological mound surfaces are threatened by the accelerated transformation of barren lands into new irrigated agricultural lands. We have provided the algorithm code with the article to ensure that AgriExp can be exported and implemented with little computational cost by academics and heritage practitioners alike to monitor critically endangered archaeological and cultural landscapes elsewhere.

Air pollution in Delhi, India: It's status and association with respiratory diseases.

The policymakers need research studies indicating the role of different pollutants with morbidity for polluted cities to install a strategic air quality management system. This study critically assessed the air pollution of Delhi for 2016–18 to found out the role of air pollutants in respiratory morbidity under the ICD-10, J00-J99. The critical assessment of Delhi air pollution was done using various approaches. The mean PM2.5 and PM10 concentrations during the measurement period exceeded both national and international standards by a wide margin. Time series charts indicated the interdependence of PM2.5 and PM10 and connection with hospital visits due to respiratory diseases. Violin plots showed that daily respiratory disease hospital visits increased during the winter and autumn seasons. The winter season was the worst from the city’s air pollution point of view, as revealed by frequency analyses. The single and multi-pollutant GAM models indicated that short-term exposure to PM10 and SO2 led to increased hospital visits due to respiratory diseases. Per 10 units increase in concentrations of PM10 brought the highest increase in hospital visits of 0.21% (RR: 1.00, 95% CI: 1.001, 1.002) at lag0-6 days. This study found the robust effect of SO2 persisted in Delhi from lag0 to lag4 days and lag01 to lag06 days for single and cumulative lag day effects, respectively. While every 10 μg m-3 increase of SO2 concentrations on the same day (lag0) led to 32.59% (RR: 1.33, 95% CI: 1.09, 1.61) rise of hospital visits, the cumulative concentration of lag0-1 led to 37.21% (RR: 1.37, 95% CI:1.11, 1.70) rise in hospital visits which further increased to even 83.33% (RR: 1.83, 95% CI:1.35, 2.49) rise at a lag0-6 cumulative concentration in Delhi. The role of SO2 in inducing respiratory diseases is worrying as India is now the largest anthropogenic SO2 emitter in the world.

Assets Trading Strategy Based on ALG Evaluation and Prediction Integrated Model

With the rapid development of economy and the maturity of financial markets, more and more people choose to invest in stocks to achieve property appreciation, so the study of stock investment decision-making is increasingly important. In this topic, in order to maximize the interests of investors, our team not only needs to accurately predict the future trend of gold and bitcoin, but also needs to make the most reasonable strategy according to the future trend of gold and bitcoin. Therefore, our team reads a large number of papers, refer to the current model of stock trend prediction and decision-making and combine with the data given by the topic to establish our model. According to the stock trend, this paper designs an ALG (Apriori algorithm - Attentional BiLSTM neural network - GARCH model) evaluation and prediction integrated model, and makes a time series chart of daily return rate related to gold and bitcoin, finally predicts the daily return rate. This paper compares the daily yield predicted by ALG model with that predicted by grey model, which shows that the yield predicted by ALG model is much higher than that predicted by grey prediction model.

E-mail-based health care in patients with dementia during the pandemic.

Introduction/aimFrail and cognitively impaired older patients are particularly vulnerable groups during the pandemic. Lockdowns, social isolation, and physical inactivity considerably affect physical and mental wellbeing. During the pandemic process, routine medical checks and acute medical care services may be disrupted. The study aimed to demonstrate the feasibility and effectiveness of telemedicine in the delivery of healthcare services to elderly patients during the pandemic.Materials and methodsE-mails sent to the e-mail address of the department of geriatrics, which has been actively used for 4 years, between April 2020 and June 2021, were retrospectively evaluated. The time and reason for each application, referral to the patients, demographic data of the patients, and chronic diseases were recorded. E-mail frequencies were considered monthly time series, and time series charts for e-mail frequencies from patients were produced.ResultsA total of 374 e-mails that 213 patients sent were assessed. A vast majority, 97.6% of the e-mails, were sent by proxies. The mean age of patients was 78.7 ± 8.1 years, and 59.2% were women. Hypertension and dementia were the most common comorbidities. The applications mostly occurred in April-May and October-November 2020. The most common complaint in dementia was behavioral disturbances (13.6%). Geriatric outpatient appointments were arranged for 29.9% of the applicants, 14.2% were referred to the emergency department, and 23.0% were offered medical treatment. Outpatient examination and treatment were completed in 15% of the patients and 10.4% of them were hospitalized. The time series charts showed that e-mails were sent more frequently by patients with dementia than the others (p = 0.03).ConclusionsTelemedicine, which enables many problems of patients to be solved in geriatric practice without face-to-face appointments, can also prevent infections and unnecessary hospitalizations, especially during these unusual pandemic days.

Automatic Mapping and Monitoring of Marine Water Quality Parameters in Hong Kong Using Sentinel-2 Image Time-Series and Google Earth Engine Cloud Computing

Continuous monitoring of coastal water qualities is critical for water resource management and marine ecosystem sustainability. While remote sensing data such as Sentinel-2 satellite imagery routinely provide high-resolution observations for time-series analysis, the cloud-based Google Earth Engine (GEE) platform supports simple image retrieval and large-scale processing. Using coastal waters of Hong Kong as the study area, this study utilized GEE to (i) query and pre-process all Sentinel-2 observations that coincided with in situ measurements; (ii) extract the spectra to develop empirical models for water quality parameters using artificial neural networks; and (iii) visualize the results using spatial distribution maps, time-series charts and an online application. The modeling workflow was applied to 22 water quality parameters and the results suggested the potential to predict the levels of several nutrients and inorganic constituents. In-depth analyses were conducted for chlorophyll-a, suspended solids and turbidity which produced high correlations between the predicted and observed values when validated with an independent dataset. The selected input variables followed spectral characteristics of the optical constituents. The results were considered more robust compared to previous works in the same region due to the automatic extraction of all available images and larger number of observations from different years and months. Besides visualizing long-term spatial and temporal variabilities through distribution maps and time-series charts, potential anomalies in the monitoring period including algal bloom could also be captured using the models developed from historical data. An online application was created to allow novice users to explore and analyze water quality trends with a simple web interface. The integrated use of remotely-sensed images, in situ measurements and cloud computing can offer new opportunities for implementing effective monitoring programs and understanding water quality dynamics. Although the obtained levels of accuracies were below the desired standard, the end-to-end cloud computing workflow demonstrated in this study should be further investigated considering the cost and computational efficiency for timely information delivery.

EDM: An ensemble decision method for introducing meteorological forecast uncertainty into the disaster response decision process

In this study, an ensemble decision method (EDM) that ensures that disaster risk information includes sufficient meteorological forecast uncertainty and that decision supporting information retains sufficient disaster risk is proposed, thereby resolving the current issue of incomprehensive information. Visual presentations of real-time, multidimensional information are created to help decision-makers discern comprehensive information directly from ensemble warning charts, thereby enhancing the efficiency of operations and the quality of the decision process. The EDM comprises three steps: developing the disaster data model, developing ensemble warning charts, and developing the decision process to utilize the charts. In the first step, simulations are run using the forecast data of several ensemble members, rainfall inundation thresholds, and flooding risk hotspots in weather forecasts. In the second step, ensemble warning information is visualized by adopting three types of charts to present the disaster simulation results: a counties and cities inundation warning threat graph, a towns inundation warning time series chart, and a multidimensional inundation warning chart. In the third step, a decision process is designed to support decision-makers in assessing inundation threats, evaluating activation of disaster response operations, and determining response levels. To validate the proposed method, it is tested using data from three torrential rain events in 2019. The test results of the three torrential rain events show that the inundation warnings produced by the EDM better reflect actual inundation than those produced by the deterministic forecasts.

Health Management in Italian Prisons during COVID-19 Outbreak: A Focus on the Second and Third Wave.

The SARS-CoV-2 spread is a threatening and challenging issue for correctional systems worldwide because of many factors, particularly overcrowding and of the intrinsic characteristics of the population. The prevention measures adopted by the Italian Government were aimed to protect and preserve both inmates’ and prison workers’ health. The present study aimed to evaluate the efficacy of the adopted strategies. Methods: Data regarding Italian prisons’ occupation and prisoners’ population from January 2019 to June 2021, as well as the cumulative weekly increase of confirmed cases and the number of doses of vaccine administered among the population of inmates, the prison workers, and Italian population from November 2020 to the end of June 2021, were collected. Results: Prisons’ occupation dropped from 120% to 106% after the beginning of the pandemics. The confirmed cases between inmates were consistently lower than among the Italian population and prison workers. A time-series chart showed a time lag of one week between the peaks of the different population. Conclusions: The containing strategies adopted by the Italian correctional system have proved their effectiveness in terms of the prevention and protection of both inmate and staff health.

RoboHapalytics: A Robot Assisted Haptic Controller for Immersive Analytics.

Immersive environments offer new possibilities for exploring three-dimensional volumetric or abstract data. However, typical mid-air interaction offers little guidance to the user in interacting with the resulting visuals. Previous work has explored the use of haptic controls to give users tangible affordances for interacting with the data, but these controls have either: been limited in their range and resolution; were spatially fixed; or required users to manually align them with the data space. We explore the use of a robot arm with hand tracking to align tangible controls under the user's fingers as they reach out to interact with data affordances. We begin with a study evaluating the effectiveness of a robot-extended slider control compared to a large fixed physical slider and a purely virtual mid-air slider. We find that the robot slider has similar accuracy to the physical slider but is significantly more accurate than mid-air interaction. Further, the robot slider can be arbitrarily reoriented, opening up many new possibilities for tangible haptic interaction with immersive visualisations. We demonstrate these possibilities through three use-cases: selection in a time-series chart; interactive slicing of CT scans; and finally exploration of a scatter plot depicting time-varying socio-economic data.

Analysis of spatiotemporal characteristics of internal coincidence accuracy in global TEC grid data

Evaluating the coincidence accuracy and time-varying characteristics of global total electron content (TEC) grid data can contribute to the improvement of the performance of global ionosphere maps (GIMs) and provide effective data support and theoretical verification for GIMs model update. This paper applies the global TEC grid data and GPS single-station ionospheric TEC data published by IGS from 1999 to 2020, as well as the F10.7 and SSN data series provided by the World Sunspot Index Data Center in Belgium. Using correlation, time series, and contour chart analyses, the correlation between TEC RMS and F10.7, SSN, and the accuracy-compliant space–time variation characteristics in the global TEC grid data are studied and analyzed. The results show the accuracy of the global TEC grid data is highly correlated with the solar activity intensity. The mean values of the correlation coefficients R between TEC RMS and F10.7, SSN are 0.755 and 0.707, respectively. The internal coincidence accuracy of single station GPS-TEC and global grid TEC is better than 3 TECU and is closely related to the solar activity. Globally, TEC RMS in land and ocean areas have significant differences. In general, the accuracy of the northern hemisphere is significantly higher than that of the southern hemisphere, and the accuracy of land is higher than that of the ocean. Among them, the accuracy of Europe and North America is the highest, the precision of the Pacific region is obviously on the low side, and “double hump” phenomenon can be observed. The accuracy of the global TEC grid data is closely related to the number of stations and satellites. The accuracy of the TEC grid data is higher in areas where the number of stations is dense, or when there are more satellites.

Visualization of Similar Patients in a Clinical Decision Support System for Rare Diseases - A Focus Group Study.

The diagnosis of patients with rare diseases is often delayed. A Clinical Decision Support System using similarity analysis of patient-based data may have the potential to support the diagnosis of patients with rare diseases. This qualitative study has the objective to investigate how the result of a patient similarity analysis should be presented to a physician to enable diagnosis support. We conducted a focus group with physicians practicing in rare diseases as well as medical informatics researchers. To prepare the focus group, a literature search was performed to check the current state of research regarding visualization of similar patients. We then created software-mockups for the presentation of these visualization methods for the discussion within the focus group. Two persons took independently field notes for data collection of the focus group. A questionnaire was distributed to the participants to rate the visualization methods. The results show that four visualization methods are promising for the visualization of similar patients: "Patient on demand table", "Criteria selection", "Time-Series chart" and "Patient timeline. "Patient on demand table" shows a direct comparison of patient characteristics, whereas "Criteria selection" allows the selection of different patient criteria to get deeper insights into the data. The "Time-Series chart" shows the time course of clinical parameters (e.g. blood pressure) whereas a "Patient timeline" indicates which time events exist for a patient (e.g. several symptoms on different dates). In the future, we will develop a software-prototype of the Clinical Decision Support System to include the visualization methods and evaluate the clinical usage.