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dashboards</title> <meta name="description" content="Search results for: dashboards"> <meta name="keywords" content="dashboards"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="dashboards"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 21</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: dashboards</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21</span> Challenges for Interface Designers in Designing Sensor Dashboards in the Context of Industry 4.0</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naveen%20Kumar">Naveen Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Shyambihari%20Prajapati"> Shyambihari Prajapati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Industry 4.0 is the fourth industrial revolution that focuses on interconnectivity of machine to machine, human to machine and human to human via Internet of Things (IoT). Technologies of industry 4.0 facilitate communication between human and machine through IoT and forms Cyber-Physical Production System (CPPS). In CPPS, multiple shop floors sensor data are connected through IoT and displayed through sensor dashboard to the operator. These sensor dashboards have enormous amount of information to be presented which becomes complex for operators to perform monitoring, controlling and interpretation tasks. Designing handheld sensor dashboards for supervision task will become a challenge for the interface designers. This paper reports emerging technologies of industry 4.0, changing context of increasing information complexity in consecutive industrial revolutions and upcoming design challenges for interface designers in context of Industry 4.0. Authors conclude that information complexity of sensor dashboards design has increased with consecutive industrial revolutions and designs of sensor dashboard causes cognitive load on users. Designing such complex dashboards interfaces in Industry 4.0 context will become main challenges for the interface designers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Industry4.0" title="Industry4.0">Industry4.0</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20dashboard%20design" title=" sensor dashboard design"> sensor dashboard design</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber-physical%20production%20system" title=" cyber-physical production system"> cyber-physical production system</a>, <a href="https://publications.waset.org/abstracts/search?q=Interface%20designer" title=" Interface designer"> Interface designer</a> </p> <a href="https://publications.waset.org/abstracts/110214/challenges-for-interface-designers-in-designing-sensor-dashboards-in-the-context-of-industry-40" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110214.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">128</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20</span> Elevating User Experience for Thailand Drivers: Dashboard Design Analysis in Electric Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Poom%20Thiparapkul">Poom Thiparapkul</a>, <a href="https://publications.waset.org/abstracts/search?q=Tanat%20Jiravansirikul"> Tanat Jiravansirikul</a>, <a href="https://publications.waset.org/abstracts/search?q=Pakpoom%20Thongsari"> Pakpoom Thongsari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explores the design of electric vehicle (EV) dashboards with a focus on user interaction. Findings from a Thai sample reveal a preference for physical buttons over touch interfaces due to their immediate feedback. Touchscreens lack this assurance, leading to potential uncertainty. Users' smartphone experiences create a learning curve that doesn't translate well to in-car touch systems. Gender-wise, females exhibit slightly longer decision times. Designing EV dashboards should consider these factors, prioritizing user experience while avoiding overreliance on smartphone principles. A successful example is Subaru XV's design, which calculates screen angles and button positions for targeted users. In summary, EV dashboards should be intuitive, minimize touch dependency, and accommodate user habits. Balancing modernity with functionality can enhance driving experiences while ensuring safety. A user-centered approach, acknowledging gender differences, will yield efficient and safe driving environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=user%20experience%20design" title="user experience design">user experience design</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20experience" title=" user experience"> user experience</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20vehicle" title=" electric vehicle"> electric vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=dashboard%20design" title=" dashboard design"> dashboard design</a>, <a href="https://publications.waset.org/abstracts/search?q=Thailand%20driver." title=" Thailand driver."> Thailand driver.</a> </p> <a href="https://publications.waset.org/abstracts/175020/elevating-user-experience-for-thailand-drivers-dashboard-design-analysis-in-electric-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175020.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">81</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">19</span> Insight-Based Evaluation of a Map-Based Dashboard</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anna%20Fredriksson%20H%C3%A4%C3%A4gg">Anna Fredriksson Häägg</a>, <a href="https://publications.waset.org/abstracts/search?q=Charlotte%20Weil"> Charlotte Weil</a>, <a href="https://publications.waset.org/abstracts/search?q=Niklas%20R%C3%B6nnberg"> Niklas Rönnberg</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Map-based dashboards are used for data exploration every day. The present study used an insight-based methodology for evaluating a map-based dashboard that presents research findings of water management and ecosystem services in the Amazon. In addition to analyzing the insights gained from using the dashboard, the evaluation method was compared to standardized questionnaires and task-based evaluations. The result suggests that the dashboard enabled the participants to gain domain-relevant, complex insights regarding the topic presented. Furthermore, the insight-based analysis highlighted unexpected insights and hypotheses regarding causes and potential adaptation strategies for remediation. Although time- and resource-consuming, the insight-based methodology was shown to have the potential of thoroughly analyzing how end users can utilize map-based dashboards for data exploration and decision making. Finally, the insight-based methodology is argued to evaluate tools in scenarios more similar to real-life usage compared to task-based evaluation methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=visual%20analytics" title="visual analytics">visual analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=dashboard" title=" dashboard"> dashboard</a>, <a href="https://publications.waset.org/abstracts/search?q=insight-based%20evaluation" title=" insight-based evaluation"> insight-based evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=geographic%20visualization" title=" geographic visualization"> geographic visualization</a> </p> <a href="https://publications.waset.org/abstracts/146829/insight-based-evaluation-of-a-map-based-dashboard" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146829.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">115</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18</span> Fueling Efficient Reporting And Decision-Making In Public Health With Large Data Automation In Remote Areas, Neno Malawi</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wiseman%20Emmanuel%20Nkhomah">Wiseman Emmanuel Nkhomah</a>, <a href="https://publications.waset.org/abstracts/search?q=Chiyembekezo%20Kachimanga"> Chiyembekezo Kachimanga</a>, <a href="https://publications.waset.org/abstracts/search?q=Julia%20Huggins"> Julia Huggins</a>, <a href="https://publications.waset.org/abstracts/search?q=Fabien%20Munyaneza"> Fabien Munyaneza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Partners In Health – Malawi introduced one of Operational Researches called Primary Health Care (PHC) Surveys in 2020, which seeks to assess progress of delivery of care in the district. The study consists of 5 long surveys, namely; Facility assessment, General Patient, Provider, Sick Child, Antenatal Care (ANC), primarily conducted in 4 health facilities in Neno district. These facilities include Neno district hospital, Dambe health centre, Chifunga and Matope. Usually, these annual surveys are conducted from January, and the target is to present final report by June. Once data is collected and analyzed, there are a series of reviews that take place before reaching final report. In the first place, the manual process took over 9 months to present final report. Initial findings reported about 76.9% of the data that added up when cross-checked with paper-based sources. Purpose: The aim of this approach is to run away from manually pulling the data, do fresh analysis, and reporting often associated not only with delays in reporting inconsistencies but also with poor quality of data if not done carefully. This automation approach was meant to utilize features of new technologies to create visualizations, reports, and dashboards in Power BI that are directly fished from the data source – CommCare hence only require a single click of a ‘refresh’ button to have the updated information populated in visualizations, reports, and dashboards at once. Methodology: We transformed paper-based questionnaires into electronic using CommCare mobile application. We further connected CommCare Mobile App directly to Power BI using Application Program Interface (API) connection as data pipeline. This provided chance to create visualizations, reports, and dashboards in Power BI. Contrary to the process of manually collecting data in paper-based questionnaires, entering them in ordinary spreadsheets, and conducting analysis every time when preparing for reporting, the team utilized CommCare and Microsoft Power BI technologies. We utilized validations and logics in CommCare to capture data with less errors. We utilized Power BI features to host the reports online by publishing them as cloud-computing process. We switched from sharing ordinary report files to sharing the link to potential recipients hence giving them freedom to dig deep into extra findings within Power BI dashboards and also freedom to export to any formats of their choice. Results: This data automation approach reduced research timelines from the initial 9 months’ duration to 5. It also improved the quality of the data findings from the original 76.9% to 98.9%. This brought confidence to draw conclusions from the findings that help in decision-making and gave opportunities for further researches. Conclusion: These results suggest that automating the research data process has the potential of reducing overall amount of time spent and improving the quality of the data. On this basis, the concept of data automation should be taken into serious consideration when conducting operational research for efficiency and decision-making. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reporting" title="reporting">reporting</a>, <a href="https://publications.waset.org/abstracts/search?q=decision-making" title=" decision-making"> decision-making</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20BI" title=" power BI"> power BI</a>, <a href="https://publications.waset.org/abstracts/search?q=commcare" title=" commcare"> commcare</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20automation" title=" data automation"> data automation</a>, <a href="https://publications.waset.org/abstracts/search?q=visualizations" title=" visualizations"> visualizations</a>, <a href="https://publications.waset.org/abstracts/search?q=dashboards" title=" dashboards"> dashboards</a> </p> <a href="https://publications.waset.org/abstracts/145637/fueling-efficient-reporting-and-decision-making-in-public-health-with-large-data-automation-in-remote-areas-neno-malawi" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145637.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">116</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17</span> Advanced Data Visualization Techniques for Effective Decision-making in Oil and Gas Exploration and Production</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deepak%20Singh">Deepak Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Rail%20Kuliev"> Rail Kuliev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research article explores the significance of advanced data visualization techniques in enhancing decision-making processes within the oil and gas exploration and production domain. With the oil and gas industry facing numerous challenges, effective interpretation and analysis of vast and diverse datasets are crucial for optimizing exploration strategies, production operations, and risk assessment. The article highlights the importance of data visualization in managing big data, aiding the decision-making process, and facilitating communication with stakeholders. Various advanced data visualization techniques, including 3D visualization, augmented reality (AR), virtual reality (VR), interactive dashboards, and geospatial visualization, are discussed in detail, showcasing their applications and benefits in the oil and gas sector. The article presents case studies demonstrating the successful use of these techniques in optimizing well placement, real-time operations monitoring, and virtual reality training. Additionally, the article addresses the challenges of data integration and scalability, emphasizing the need for future developments in AI-driven visualization. In conclusion, this research emphasizes the immense potential of advanced data visualization in revolutionizing decision-making processes, fostering data-driven strategies, and promoting sustainable growth and improved operational efficiency within the oil and gas exploration and production industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=augmented%20reality%20%28AR%29" title="augmented reality (AR)">augmented reality (AR)</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20reality%20%28VR%29" title=" virtual reality (VR)"> virtual reality (VR)</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20dashboards" title=" interactive dashboards"> interactive dashboards</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20operations%20monitoring" title=" real-time operations monitoring"> real-time operations monitoring</a> </p> <a href="https://publications.waset.org/abstracts/171514/advanced-data-visualization-techniques-for-effective-decision-making-in-oil-and-gas-exploration-and-production" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171514.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16</span> A Visual Analytics Tool for the Structural Health Monitoring of an Aircraft Panel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20M.%20Pisano">F. M. Pisano</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Ciminello"> M. Ciminello</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aerospace, mechanical, and civil engineering infrastructures can take advantages from damage detection and identification strategies in terms of maintenance cost reduction and operational life improvements, as well for safety scopes. The challenge is to detect so called “barely visible impact damage” (BVID), due to low/medium energy impacts, that can progressively compromise the structure integrity. The occurrence of any local change in material properties, that can degrade the structure performance, is to be monitored using so called Structural Health Monitoring (SHM) systems, in charge of comparing the structure states before and after damage occurs. SHM seeks for any "anomalous" response collected by means of sensor networks and then analyzed using appropriate algorithms. Independently of the specific analysis approach adopted for structural damage detection and localization, textual reports, tables and graphs describing possible outlier coordinates and damage severity are usually provided as artifacts to be elaborated for information extraction about the current health conditions of the structure under investigation. Visual Analytics can support the processing of monitored measurements offering data navigation and exploration tools leveraging the native human capabilities of understanding images faster than texts and tables. Herein, a SHM system enrichment by integration of a Visual Analytics component is investigated. Analytical dashboards have been created by combining worksheets, so that a useful Visual Analytics tool is provided to structural analysts for exploring the structure health conditions examined by a Principal Component Analysis based algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interactive%20dashboards" title="interactive dashboards">interactive dashboards</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20fibers" title=" optical fibers"> optical fibers</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20health%20monitoring" title=" structural health monitoring"> structural health monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20analytics" title=" visual analytics"> visual analytics</a> </p> <a href="https://publications.waset.org/abstracts/130216/a-visual-analytics-tool-for-the-structural-health-monitoring-of-an-aircraft-panel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130216.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">124</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15</span> Business Intelligence Dashboard Solutions for Improving Decision Making Process: A Focus on Prostate Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mona%20Isazad%20Mashinchi">Mona Isazad Mashinchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Davood%20Roshan%20Sangachin"> Davood Roshan Sangachin</a>, <a href="https://publications.waset.org/abstracts/search?q=Francis%20J.%20%20Sullivan"> Francis J. Sullivan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dietrich%20%20Rebholz-Schuhmann"> Dietrich Rebholz-Schuhmann</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Decision-making processes are nowadays driven by data, data analytics and Business Intelligence (BI). BI as a software platform can provide a wide variety of capabilities such as organization memory, information integration, insight creation and presentation capabilities. Visualizing data through dashboards is one of the BI solutions (for a variety of areas) which helps managers in the decision making processes to expose the most informative information at a glance. In the healthcare domain to date, dashboard presentations are more frequently used to track performance related metrics and less frequently used to monitor those quality parameters which relate directly to patient outcomes. Providing effective and timely care for patients and improving the health outcome are highly dependent on presenting and visualizing data and information. Objective: In this research, the focus is on the presentation capabilities of BI to design a dashboard for prostate cancer (PC) data that allows better decision making for the patients, the hospital and the healthcare system related to a cancer dataset. The aim of this research is to customize a retrospective PC dataset in a dashboard interface to give a better understanding of data in the categories (risk factors, treatment approaches, disease control and side effects) which matter most to patients as well as other stakeholders. By presenting the outcome in the dashboard we address one of the major targets of a value-based health care (VBHC) delivery model which is measuring the value and presenting the outcome to different actors in HC industry (such as patients and doctors) for a better decision making. Method: For visualizing the stored data to users, three interactive dashboards based on the PC dataset have been developed (using the Tableau Software) to provide better views to the risk factors, treatment approaches, and side effects. Results: Many benefits derived from interactive graphs and tables in dashboards which helped to easily visualize and see the patients at risk, better understanding the relationship between patient's status after treatment and their initial status before treatment, or to choose better decision about treatments with fewer side effects regarding patient status and etc. Conclusions: Building a well-designed and informative dashboard is related to three important factors including; the users, goals and the data types. Dashboard's hierarchies, drilling, and graphical features can guide doctors to better navigate through information. The features of the interactive PC dashboard not only let doctors ask specific questions and filter the results based on the key performance indicators (KPI) such as: Gleason Grade, Patient's Age and Status, but may also help patients to better understand different treatment outcomes, such as side effects during the time, and have an active role in their treatment decisions. Currently, we are extending the results to the real-time interactive dashboard that users (either patients and doctors) can easily explore the data by choosing preferred attribute and data to make better near real-time decisions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title="business intelligence">business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=dashboard" title=" dashboard"> dashboard</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare" title=" healthcare"> healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=prostate%20cancer" title=" prostate cancer"> prostate cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=value-based%20healthcare" title=" value-based healthcare"> value-based healthcare</a> </p> <a href="https://publications.waset.org/abstracts/91628/business-intelligence-dashboard-solutions-for-improving-decision-making-process-a-focus-on-prostate-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91628.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">14</span> Utilization of Informatics to Transform Clinical Data into a Simplified Reporting System to Examine the Analgesic Prescribing Practices of a Single Urban Hospital’s Emergency Department</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rubaiat%20S.%20Ahmed">Rubaiat S. Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Jemer%20Garrido"> Jemer Garrido</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20M.%20Motov"> Sergey M. Motov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clinical informatics (CI) enables the transformation of data into a systematic organization that improves the quality of care and the generation of positive health outcomes.Innovative technology through informatics that compiles accurate data on analgesic utilization in the emergency department can enhance pain management in this important clinical setting. We aim to establish a simplified reporting system through CI to examine and assess the analgesic prescribing practices in the EDthrough executing a U.S. federal grant project on opioid reduction initiatives. Queried data points of interest from a level-one trauma ED’s electronic medical records were used to create data sets and develop informational/visual reporting dashboards (on Microsoft Excel and Google Sheets) concerning analgesic usage across several pre-defined parameters and performance metrics using CI. The data was then qualitatively analyzed to evaluate ED analgesic prescribing trends by departmental clinicians and leadership. During a 12-month reporting period (Dec. 1, 2020 – Nov. 30, 2021) for the ongoing project, about 41% of all ED patient visits (N = 91,747) were for pain conditions, of which 81.6% received analgesics in the ED and at discharge (D/C). Of those treated with analgesics, 24.3% received opioids compared to 75.7% receiving opioid alternatives in the ED and at D/C, including non-pharmacological modalities. Demographics showed among patients receiving analgesics, 56.7% were aged between 18-64, 51.8% were male, 51.7% were white, and 66.2% had government funded health insurance. Ninety-one percent of all opioids prescribed were in the ED, with intravenous (IV) morphine, IV fentanyl, and morphine sulfate immediate release (MSIR) tablets accounting for 88.0% of ED dispensed opioids. With 9.3% of all opioids prescribed at D/C, MSIR was dispensed 72.1% of the time. Hydrocodone, oxycodone, and tramadol usage to only 10-15% of the time, and hydromorphone at 0%. Of opioid alternatives, non-steroidal anti-inflammatory drugs were utilized 60.3% of the time, 23.5% with local anesthetics and ultrasound-guided nerve blocks, and 7.9% with acetaminophen as the primary non-opioid drug categories prescribed by ED providers. Non-pharmacological analgesia included virtual reality and other modalities. An average of 18.5 ED opioid orders and 1.9 opioid D/C prescriptions per 102.4 daily ED patient visits was observed for the period. Compared to other specialties within our institution, 2.0% of opioid D/C prescriptions are given by ED providers, compared to the national average of 4.8%. Opioid alternatives accounted for 69.7% and 30.3% usage, versus 90.7% and 9.3% for opioids in the ED and D/C, respectively.There is a pressing need for concise, relevant, and reliable clinical data on analgesic utilization for ED providers and leadership to evaluate prescribing practices and make data-driven decisions. Basic computer software can be used to create effective visual reporting dashboards with indicators that convey relevant and timely information in an easy-to-digest manner. We accurately examined our ED's analgesic prescribing practices using CI through dashboard reporting. Such reporting tools can quickly identify key performance indicators and prioritize data to enhance pain management and promote safe prescribing practices in the emergency setting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clinical%20informatics" title="clinical informatics">clinical informatics</a>, <a href="https://publications.waset.org/abstracts/search?q=dashboards" title=" dashboards"> dashboards</a>, <a href="https://publications.waset.org/abstracts/search?q=emergency%20department" title=" emergency department"> emergency department</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20informatics" title=" health informatics"> health informatics</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare%20informatics" title=" healthcare informatics"> healthcare informatics</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20informatics" title=" medical informatics"> medical informatics</a>, <a href="https://publications.waset.org/abstracts/search?q=opioids" title=" opioids"> opioids</a>, <a href="https://publications.waset.org/abstracts/search?q=pain%20management" title=" pain management"> pain management</a>, <a href="https://publications.waset.org/abstracts/search?q=technology" title=" technology"> technology</a> </p> <a href="https://publications.waset.org/abstracts/147166/utilization-of-informatics-to-transform-clinical-data-into-a-simplified-reporting-system-to-examine-the-analgesic-prescribing-practices-of-a-single-urban-hospitals-emergency-department" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147166.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13</span> Customer Data Analysis Model Using Business Intelligence Tools in Telecommunication Companies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Monica%20Lia">Monica Lia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article presents a customer data analysis model using business intelligence tools for data modelling, transforming, data visualization and dynamic reports building. Economic organizational customer’s analysis is made based on the information from the transactional systems of the organization. The paper presents how to develop the data model starting for the data that companies have inside their own operational systems. The owned data can be transformed into useful information about customers using business intelligence tool. For a mature market, knowing the information inside the data and making forecast for strategic decision become more important. Business Intelligence tools are used in business organization as support for decision-making. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customer%20analysis" title="customer analysis">customer analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title=" business intelligence"> business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title=" data warehouse"> data warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=decisions" title=" decisions"> decisions</a>, <a href="https://publications.waset.org/abstracts/search?q=self-service%20reports" title=" self-service reports"> self-service reports</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20visual%20analysis" title=" interactive visual analysis"> interactive visual analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20dynamic%20dashboards" title=" and dynamic dashboards"> and dynamic dashboards</a>, <a href="https://publications.waset.org/abstracts/search?q=use%20cases%20diagram" title=" use cases diagram"> use cases diagram</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20modelling" title=" process modelling"> process modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=logical%20data%20model" title=" logical data model"> logical data model</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mart" title=" data mart"> data mart</a>, <a href="https://publications.waset.org/abstracts/search?q=ETL" title=" ETL"> ETL</a>, <a href="https://publications.waset.org/abstracts/search?q=star%20schema" title=" star schema"> star schema</a>, <a href="https://publications.waset.org/abstracts/search?q=OLAP" title=" OLAP"> OLAP</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20universes" title=" data universes"> data universes</a> </p> <a href="https://publications.waset.org/abstracts/39914/customer-data-analysis-model-using-business-intelligence-tools-in-telecommunication-companies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39914.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">430</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12</span> Designing a Model for Preparing Reports on the Automatic Earned Value Management Progress by the Integration of Primavera P6, SQL Database, and Power BI: A Case Study of a Six-Storey Concrete Building in Mashhad, Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Zolfaghari">Hamed Zolfaghari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mojtaba%20Kord"> Mojtaba Kord</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Project planners and controllers are frequently faced with the challenge of inadequate software for the preparation of automatic project progress reports based on actual project information updates. They usually make dashboards in Microsoft Excel, which is local and not applicable online. Another shortcoming is that it is not linked to planning software such as Microsoft Project, which lacks the database required for data storage. This study aimed to propose a model for the preparation of reports on automatic online project progress based on actual project information updates by the integration of Primavera P6, SQL database, and Power BI for a construction project. The designed model could be applicable to project planners and controller agents by enabling them to prepare project reports automatically and immediately after updating the project schedule using actual information. To develop the model, the data were entered into P6, and the information was stored on the SQL database. The proposed model could prepare a wide range of reports, such as earned value management, HR reports, and financial, physical, and risk reports automatically on the Power BI application. Furthermore, the reports could be published and shared online. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=primavera%20P6" title="primavera P6">primavera P6</a>, <a href="https://publications.waset.org/abstracts/search?q=SQL" title=" SQL"> SQL</a>, <a href="https://publications.waset.org/abstracts/search?q=Power%20BI" title=" Power BI"> Power BI</a>, <a href="https://publications.waset.org/abstracts/search?q=EVM" title=" EVM"> EVM</a>, <a href="https://publications.waset.org/abstracts/search?q=integration%20management" title=" integration management"> integration management</a> </p> <a href="https://publications.waset.org/abstracts/151853/designing-a-model-for-preparing-reports-on-the-automatic-earned-value-management-progress-by-the-integration-of-primavera-p6-sql-database-and-power-bi-a-case-study-of-a-six-storey-concrete-building-in-mashhad-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151853.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">108</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11</span> Knowledge Management in the Interactive Portal for Decision Makers on InKOM Example</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Marciniak">K. Marciniak</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Owoc"> M. Owoc</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Managers as decision-makers present in different sectors should be supported in efficient and more and more sophisticated way. There are huge number of software tools developed for such users starting from simple registering data from business area – typical for operational level of management – up to intelligent techniques with delivering knowledge - for tactical and strategic levels of management. There is a big challenge for software developers to create intelligent management dashboards allowing to support different decisions. In more advanced solutions there is even an option for selection of intelligent techniques useful for managers in particular decision-making phase in order to deliver valid knowledge-base. Such a tool (called Intelligent Dashboard for SME Managers–InKOM) is prepared in the Business Intelligent framework of Teta products. The aim of the paper is to present solutions assumed for InKOM concerning on management of stored knowledge bases offering for business managers. The paper is managed as follows. After short introduction concerning research context the discussed supporting managers via information systems the InKOM platform is presented. In the crucial part of paper a process of knowledge transformation and validation is demonstrated. We will focus on potential and real ways of knowledge-bases acquiring, storing and validation. It allows for formulation conclusions interesting from knowledge engineering point of view. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title="business intelligence">business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20systems" title=" decision support systems"> decision support systems</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20transformation" title=" knowledge transformation"> knowledge transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20validation" title=" knowledge validation"> knowledge validation</a>, <a href="https://publications.waset.org/abstracts/search?q=managerial%20systems" title=" managerial systems"> managerial systems</a> </p> <a href="https://publications.waset.org/abstracts/13751/knowledge-management-in-the-interactive-portal-for-decision-makers-on-inkom-example" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13751.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">512</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10</span> Endeavor in Management Process by Executive Dashboards: The Case of the Financial Directorship in Brazilian Navy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20S.%20Quintal">R. S. Quintal</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20L.%20Tesch%20Santos"> J. L. Tesch Santos</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20D.%20Davis"> M. D. Davis</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20C.%20de%20Santana"> E. C. de Santana</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20de%20F.%20Bandeira%20dos%20Santos"> M. de F. Bandeira dos Santos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective is to identify the contributions from the introduction of the computerized system deal within the Accounting Department of Brazilian Navy Financial Directorship and its possible effects on the budgetary and financial harvest of Brazilian Navy. The relevance lies in the fact that the management process is responsible for the continuous improvement of organizational performance through higher levels of quality in their activities. Improvements in organizational processes have direct effects on crops cost, quality, reliability, flexibility and speed. The method of study of this research is the case study. The choice of case study attended, among other demands, a need for greater flexibility to study processes related to a computerized system. The sources of evidence were used literature, documentary and direct observation. Direct observation was made by monitoring the implementation of the computerized system in the Division of Management Analysis. The main findings of the study point to the fact that the computerized system may contribute significantly to the standardization of information. There was improvement of internal processes in the division of management analysis, made possible the consolidation of a standard management and performance analysis that contribute to global homogeneity in the treatment of information essential to the process of decision making. This study has limitations related to the fact the search result be subject exclusively to the case studied, and it is impossible to generalize to other organs of government. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20management" title="process management">process management</a>, <a href="https://publications.waset.org/abstracts/search?q=management%20control" title=" management control"> management control</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title=" business intelligence"> business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=Brazilian%20Navy" title=" Brazilian Navy"> Brazilian Navy</a> </p> <a href="https://publications.waset.org/abstracts/6369/endeavor-in-management-process-by-executive-dashboards-the-case-of-the-financial-directorship-in-brazilian-navy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6369.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">238</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9</span> Enhancing Sell-In and Sell-Out Forecasting Using Ensemble Machine Learning Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishal%20Das">Vishal Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Tianyi%20Mao"> Tianyi Mao</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhicheng%20Geng"> Zhicheng Geng</a>, <a href="https://publications.waset.org/abstracts/search?q=Carmen%20Flores"> Carmen Flores</a>, <a href="https://publications.waset.org/abstracts/search?q=Diego%20Pelloso"> Diego Pelloso</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang%20Wang"> Fang Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sell-in%20and%20sell-out%20forecasting" title="sell-in and sell-out forecasting">sell-in and sell-out forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20planning" title=" demand planning"> demand planning</a>, <a href="https://publications.waset.org/abstracts/search?q=DeepAR" title=" DeepAR"> DeepAR</a>, <a href="https://publications.waset.org/abstracts/search?q=retail" title=" retail"> retail</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20machine%20learning" title=" ensemble machine learning"> ensemble machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=time-series" title=" time-series"> time-series</a> </p> <a href="https://publications.waset.org/abstracts/169497/enhancing-sell-in-and-sell-out-forecasting-using-ensemble-machine-learning-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169497.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">273</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> End-Users Tools to Empower and Raise Awareness of Behavioural Change towards Energy Efficiency</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Calleja-Rodriguez">G. Calleja-Rodriguez</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Jimenez-Redondo"> N. Jimenez-Redondo</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20J.%20Peralta%20Escalante"> J. J. Peralta Escalante</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research work aims at developing a solution to take advantage of the potential energy saving related to occupants behaviour estimated in between 5-30 % according to existing studies. For that purpose, the following methodology has been followed: 1) literature review and gap analysis, 2) define concept and functional requirements, 3) evaluation and feedback by experts. As result, the concept for a tool-box that implements continuous behavior change interventions named as engagement methods and based on increasing energy literacy, increasing energy visibility, using bonus system, etc. has been defined. These engagement methods are deployed through a set of ICT tools: Building Automation and Control System (BACS) add-ons services installed in buildings and Users Apps installed in smartphones, smart-TVs or dashboards. The tool-box called eTEACHER identifies energy conservation measures (ECM) based on energy behavioral change through a what-if analysis that collects information about the building and its users (comfort feedback, behavior, etc.) and carry out cost-effective calculations to provide outputs such us efficient control settings of building systems. This information is processed and showed in an attractive way as tailored advice to the energy end-users. Therefore, eTEACHER goal is to change the behavior of building´s energy users towards energy efficiency, comfort and better health conditions by deploying customized ICT-based interventions taking into account building typology (schools, residential, offices, health care centres, etc.), users profile (occupants, owners, facility managers, employers, etc.) as well as cultural and demographic factors. One of the main findings of this work is the common failure when technological interventions on behavioural change are done to not consult, train and support users regarding technological changes leading to poor performance in practices. As conclusion, a strong need to carry out social studies to identify relevant behavioural issues and to identify effective pro-evironmental behavioral change strategies has been identified. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20saving" title="energy saving">energy saving</a>, <a href="https://publications.waset.org/abstracts/search?q=behavioral%20bhange" title=" behavioral bhange"> behavioral bhange</a>, <a href="https://publications.waset.org/abstracts/search?q=building%20users" title=" building users"> building users</a>, <a href="https://publications.waset.org/abstracts/search?q=engagement%20methods" title=" engagement methods"> engagement methods</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20conservation%20measures" title=" energy conservation measures"> energy conservation measures</a> </p> <a href="https://publications.waset.org/abstracts/85071/end-users-tools-to-empower-and-raise-awareness-of-behavioural-change-towards-energy-efficiency" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85071.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">170</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> Crowdsensing Project in the Brazilian Municipality of Florianópolis for the Number of Visitors Measurement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Roberto%20De%20Rolt">Carlos Roberto De Rolt</a>, <a href="https://publications.waset.org/abstracts/search?q=Julio%20da%20Silva%20Dias"> Julio da Silva Dias</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafael%20Tezza"> Rafael Tezza</a>, <a href="https://publications.waset.org/abstracts/search?q=Luca%20Foschini"> Luca Foschini</a>, <a href="https://publications.waset.org/abstracts/search?q=Matteo%20Mura"> Matteo Mura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The seasonal population fluctuation presents a challenge to touristic cities since the number of inhabitants can double according to the season. The aim of this work is to develop a model that correlates the waste collected with the population of the city and also allow cooperation between the inhabitants and the local government. The model allows public managers to evaluate the impact of the seasonal population fluctuation on waste generation and also to improve planning resource utilization throughout the year. The study uses data from the company that collects the garbage in Florianópolis, a Brazilian city that presents the profile of a city that attracts tourists due to numerous beaches and warm weather. The fluctuations are caused by the number of people that come to the city throughout the year for holidays, summer time vacations or business events. Crowdsensing will be accomplished through smartphones with access to an app for data collection, with voluntary participation of the population. Crowdsensing participants can access information collected in waves for this portal. Crowdsensing represents an innovative and participatory approach which involves the population in gathering information to improve the quality of life. The management of crowdsensing solutions plays an essential role given the complexity to foster collaboration, establish available sensors and collect and process the collected data. Practical implications of this tool described in this paper refer, for example, to the management of seasonal tourism in a large municipality, whose public services are impacted by the floating of the population. Crowdsensing and big data support managers in predicting the arrival, permanence, and movement of people in a given urban area. Also, by linking crowdsourced data to databases from other public service providers - e.g., water, garbage collection, electricity, public transport, telecommunications - it is possible to estimate the floating of the population of an urban area affected by seasonal tourism. This approach supports the municipality in increasing the effectiveness of resource allocation while, at the same time, increasing the quality of the service as perceived by citizens and tourists. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=dashboards" title=" dashboards"> dashboards</a>, <a href="https://publications.waset.org/abstracts/search?q=floating%20population" title=" floating population"> floating population</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20city" title=" smart city"> smart city</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20management%20solutions" title=" urban management solutions"> urban management solutions</a> </p> <a href="https://publications.waset.org/abstracts/69059/crowdsensing-project-in-the-brazilian-municipality-of-florianopolis-for-the-number-of-visitors-measurement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69059.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">287</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Data Collection in Protected Agriculture for Subsequent Big Data Analysis: Methodological Evaluation in Venezuela</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20Antonieta%20Erna%20Castillo%20Holly">Maria Antonieta Erna Castillo Holly</a> </p> <p class="card-text"><strong>Abstract:</strong></p> During the last decade, data analysis, strategic decision making, and the use of artificial intelligence (AI) tools in Latin American agriculture have been a challenge. In some countries, the availability, quality, and reliability of historical data, in addition to the current data recording methodology in the field, makes it difficult to use information systems, complete data analysis, and their support for making the right strategic decisions. This is something essential in Agriculture 4.0. where the increase in the global demand for fresh agricultural products of tropical origin, during all the seasons of the year requires a change in the production model and greater agility in the responses to the consumer market demands of quality, quantity, traceability, and sustainability –that means extensive data-. Having quality information available and updated in real-time on what, how much, how, when, where, at what cost, and the compliance with production quality standards represents the greatest challenge for sustainable and profitable agriculture in the region. The objective of this work is to present a methodological proposal for the collection of georeferenced data from the protected agriculture sector, specifically in production units (UP) with tall structures (Greenhouses), initially for Venezuela, taking the state of Mérida as the geographical framework, and horticultural products as target crops. The document presents some background information and explains the methodology and tools used in the 3 phases of the work: diagnosis, data collection, and analysis. As a result, an evaluation of the process is carried out, relevant data and dashboards are displayed, and the first satellite maps integrated with layers of information in a geographic information system are presented. Finally, some improvement proposals and tentatively recommended applications are added to the process, understanding that their objective is to provide better qualified and traceable georeferenced data for subsequent analysis of the information and more agile and accurate strategic decision making. One of the main points of this study is the lack of quality data treatment in the Latin America area and especially in the Caribbean basin, being one of the most important points how to manage the lack of complete official data. The methodology has been tested with horticultural products, but it can be extended to other tropical crops. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=greenhouses" title="greenhouses">greenhouses</a>, <a href="https://publications.waset.org/abstracts/search?q=protected%20agriculture" title=" protected agriculture"> protected agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=geographic%20information%20systems" title=" geographic information systems"> geographic information systems</a>, <a href="https://publications.waset.org/abstracts/search?q=Venezuela" title=" Venezuela"> Venezuela</a> </p> <a href="https://publications.waset.org/abstracts/136486/data-collection-in-protected-agriculture-for-subsequent-big-data-analysis-methodological-evaluation-in-venezuela" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136486.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Handling, Exporting and Archiving Automated Mineralogy Data Using TESCAN TIMA</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marek%20Dosbaba">Marek Dosbaba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Within the mining sector, SEM-based Automated Mineralogy (AM) has been the standard application for quickly and efficiently handling mineral processing tasks. Over the last decade, the trend has been to analyze larger numbers of samples, often with a higher level of detail. This has necessitated a shift from interactive sample analysis performed by an operator using a SEM, to an increased reliance on offline processing to analyze and report the data. In response to this trend, TESCAN TIMA Mineral Analyzer is designed to quickly create a virtual copy of the studied samples, thereby preserving all the necessary information. Depending on the selected data acquisition mode, TESCAN TIMA can perform hyperspectral mapping and save an X-ray spectrum for each pixel or segment, respectively. This approach allows the user to browse through elemental distribution maps of all elements detectable by means of energy dispersive spectroscopy. Re-evaluation of the existing data for the presence of previously unconsidered elements is possible without the need to repeat the analysis. Additional tiers of data such as a secondary electron or cathodoluminescence images can also be recorded. To take full advantage of these information-rich datasets, TIMA utilizes a new archiving tool introduced by TESCAN. The dataset size can be reduced for long-term storage and all information can be recovered on-demand in case of renewed interest. TESCAN TIMA is optimized for network storage of its datasets because of the larger data storage capacity of servers compared to local drives, which also allows multiple users to access the data remotely. This goes hand in hand with the support of remote control for the entire data acquisition process. TESCAN also brings a newly extended open-source data format that allows other applications to extract, process and report AM data. This offers the ability to link TIMA data to large databases feeding plant performance dashboards or geometallurgical models. The traditional tabular particle-by-particle or grain-by-grain export process is preserved and can be customized with scripts to include user-defined particle/grain properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tescan" title="Tescan">Tescan</a>, <a href="https://publications.waset.org/abstracts/search?q=electron%20microscopy" title=" electron microscopy"> electron microscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=mineralogy" title=" mineralogy"> mineralogy</a>, <a href="https://publications.waset.org/abstracts/search?q=SEM" title=" SEM"> SEM</a>, <a href="https://publications.waset.org/abstracts/search?q=automated%20mineralogy" title=" automated mineralogy"> automated mineralogy</a>, <a href="https://publications.waset.org/abstracts/search?q=database" title=" database"> database</a>, <a href="https://publications.waset.org/abstracts/search?q=TESCAN%20TIMA" title=" TESCAN TIMA"> TESCAN TIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20format" title=" open format"> open format</a>, <a href="https://publications.waset.org/abstracts/search?q=archiving" title=" archiving"> archiving</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a> </p> <a href="https://publications.waset.org/abstracts/145246/handling-exporting-and-archiving-automated-mineralogy-data-using-tescan-tima" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145246.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">109</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Building User Behavioral Models by Processing Web Logs and Clustering Mechanisms </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Madhuka%20G.%20P.%20D.%20Udantha">Madhuka G. P. D. Udantha</a>, <a href="https://publications.waset.org/abstracts/search?q=Gihan%20V.%20Dias"> Gihan V. Dias</a>, <a href="https://publications.waset.org/abstracts/search?q=Surangika%20Ranathunga"> Surangika Ranathunga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today Websites contain very interesting applications. But there are only few methodologies to analyze User navigations through the Websites and formulating if the Website is put to correct use. The web logs are only used if some major attack or malfunctioning occurs. Web Logs contain lot interesting dealings on users in the system. Analyzing web logs has become a challenge due to the huge log volume. Finding interesting patterns is not as easy as it is due to size, distribution and importance of minor details of each log. Web logs contain very important data of user and site which are not been put to good use. Retrieving interesting information from logs gives an idea of what the users need, group users according to their various needs and improve site to build an effective and efficient site. The model we built is able to detect attacks or malfunctioning of the system and anomaly detection. Logs will be more complex as volume of traffic and the size and complexity of web site grows. Unsupervised techniques are used in this solution which is fully automated. Expert knowledge is only used in validation. In our approach first clean and purify the logs to bring them to a common platform with a standard format and structure. After cleaning module web session builder is executed. It outputs two files, Web Sessions file and Indexed URLs file. The Indexed URLs file contains the list of URLs accessed and their indices. Web Sessions file lists down the indices of each web session. Then DBSCAN and EM Algorithms are used iteratively and recursively to get the best clustering results of the web sessions. Using homogeneity, completeness, V-measure, intra and inter cluster distance and silhouette coefficient as parameters these algorithms self-evaluate themselves to input better parametric values to run the algorithms. If a cluster is found to be too large then micro-clustering is used. Using Cluster Signature Module the clusters are annotated with a unique signature called finger-print. In this module each cluster is fed to Associative Rule Learning Module. If it outputs confidence and support as value 1 for an access sequence it would be a potential signature for the cluster. Then the access sequence occurrences are checked in other clusters. If it is found to be unique for the cluster considered then the cluster is annotated with the signature. These signatures are used in anomaly detection, prevent cyber attacks, real-time dashboards that visualize users, accessing web pages, predict actions of users and various other applications in Finance, University Websites, News and Media Websites etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title="anomaly detection">anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20sessions" title=" web sessions"> web sessions</a> </p> <a href="https://publications.waset.org/abstracts/39697/building-user-behavioral-models-by-processing-web-logs-and-clustering-mechanisms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39697.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">288</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Critical Conditions for the Initiation of Dynamic Recrystallization Prediction: Analytical and Finite Element Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pierre%20Tize%20Mha">Pierre Tize Mha</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Jahazi"> Mohammad Jahazi</a>, <a href="https://publications.waset.org/abstracts/search?q=Am%C3%A8vi%20Togne"> Amèvi Togne</a>, <a href="https://publications.waset.org/abstracts/search?q=Olivier%20Pantal%C3%A9"> Olivier Pantalé</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Large-size forged blocks made of medium carbon high-strength steels are extensively used in the automotive industry as dies for the production of bumpers and dashboards through the plastic injection process. The manufacturing process of the large blocks starts with ingot casting, followed by open die forging and a quench and temper heat treatment process to achieve the desired mechanical properties and numerical simulation is widely used nowadays to predict these properties before the experiment. But the temperature gradient inside the specimen remains challenging in the sense that the temperature before loading inside the material is not the same, but during the simulation, constant temperature is used to simulate the experiment because it is assumed that temperature is homogenized after some holding time. Therefore to be close to the experiment, real distribution of the temperature through the specimen is needed before the mechanical loading. Thus, We present here a robust algorithm that allows the calculation of the temperature gradient within the specimen, thus representing a real temperature distribution within the specimen before deformation. Indeed, most numerical simulations consider a uniform temperature gradient which is not really the case because the surface and core temperatures of the specimen are not identical. Another feature that influences the mechanical properties of the specimen is recrystallization which strongly depends on the deformation conditions and the type of deformation like Upsetting, Cogging...etc. Indeed, Upsetting and Cogging are the stages where the greatest deformations are observed, and a lot of microstructural phenomena can be observed, like recrystallization, which requires in-depth characterization. Complete dynamic recrystallization plays an important role in the final grain size during the process and therefore helps to increase the mechanical properties of the final product. Thus, the identification of the conditions for the initiation of dynamic recrystallization is still relevant. Also, the temperature distribution within the sample and strain rate influence the recrystallization initiation. So the development of a technique allowing to predict the initiation of this recrystallization remains challenging. In this perspective, we propose here, in addition to the algorithm allowing to get the temperature distribution before the loading stage, an analytical model leading to determine the initiation of this recrystallization. These two techniques are implemented into the Abaqus finite element software via the UAMP and VUHARD subroutines for comparison with a simulation where an isothermal temperature is imposed. The Artificial Neural Network (ANN) model to describe the plastic behavior of the material is also implemented via the VUHARD subroutine. From the simulation, the temperature distribution inside the material and recrystallization initiation is properly predicted and compared to the literature models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20recrystallization" title="dynamic recrystallization">dynamic recrystallization</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20modeling" title=" finite element modeling"> finite element modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20implementation" title=" numerical implementation"> numerical implementation</a> </p> <a href="https://publications.waset.org/abstracts/166612/critical-conditions-for-the-initiation-of-dynamic-recrystallization-prediction-analytical-and-finite-element-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166612.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">80</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Leveraging Power BI for Advanced Geotechnical Data Analysis and Visualization in Mining Projects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elaheh%20Talebi">Elaheh Talebi</a>, <a href="https://publications.waset.org/abstracts/search?q=Fariba%20Yavari"> Fariba Yavari</a>, <a href="https://publications.waset.org/abstracts/search?q=Lucy%20Philip"> Lucy Philip</a>, <a href="https://publications.waset.org/abstracts/search?q=Lesley%20Town"> Lesley Town</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The mining industry generates vast amounts of data, necessitating robust data management systems and advanced analytics tools to achieve better decision-making processes in the development of mining production and maintaining safety. This paper highlights the advantages of Power BI, a powerful intelligence tool, over traditional Excel-based approaches for effectively managing and harnessing mining data. Power BI enables professionals to connect and integrate multiple data sources, ensuring real-time access to up-to-date information. Its interactive visualizations and dashboards offer an intuitive interface for exploring and analyzing geotechnical data. Advanced analytics is a collection of data analysis techniques to improve decision-making. Leveraging some of the most complex techniques in data science, advanced analytics is used to do everything from detecting data errors and ensuring data accuracy to directing the development of future project phases. However, while Power BI is a robust tool, specific visualizations required by geotechnical engineers may have limitations. This paper studies the capability to use Python or R programming within the Power BI dashboard to enable advanced analytics, additional functionalities, and customized visualizations. This dashboard provides comprehensive tools for analyzing and visualizing key geotechnical data metrics, including spatial representation on maps, field and lab test results, and subsurface rock and soil characteristics. Advanced visualizations like borehole logs and Stereonet were implemented using Python programming within the Power BI dashboard, enhancing the understanding and communication of geotechnical information. Moreover, the dashboard's flexibility allows for the incorporation of additional data and visualizations based on the project scope and available data, such as pit design, rock fall analyses, rock mass characterization, and drone data. This further enhances the dashboard's usefulness in future projects, including operation, development, closure, and rehabilitation phases. Additionally, this helps in minimizing the necessity of utilizing multiple software programs in projects. This geotechnical dashboard in Power BI serves as a user-friendly solution for analyzing, visualizing, and communicating both new and historical geotechnical data, aiding in informed decision-making and efficient project management throughout various project stages. Its ability to generate dynamic reports and share them with clients in a collaborative manner further enhances decision-making processes and facilitates effective communication within geotechnical projects in the mining industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geotechnical%20data%20analysis" title="geotechnical data analysis">geotechnical data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20BI" title=" power BI"> power BI</a>, <a href="https://publications.waset.org/abstracts/search?q=visualization" title=" visualization"> visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=decision-making" title=" decision-making"> decision-making</a>, <a href="https://publications.waset.org/abstracts/search?q=mining%20industry" title=" mining industry"> mining industry</a> </p> <a href="https://publications.waset.org/abstracts/168956/leveraging-power-bi-for-advanced-geotechnical-data-analysis-and-visualization-in-mining-projects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168956.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">92</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Use of a Business Intelligence Software for Interactive Visualization of Data on the Swiss Elite Sports System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Corinne%20Zurmuehle">Corinne Zurmuehle</a>, <a href="https://publications.waset.org/abstracts/search?q=Andreas%20Christoph%20Weber"> Andreas Christoph Weber</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In 2019, the Swiss Federal Institute of Sport Magglingen (SFISM) conducted a mixed-methods study on the Swiss elite sports system, which yielded a large quantity of research data. In a quantitative online survey, 1151 elite sports athletes, 542 coaches, and 102 Performance Directors of national sports federations (NF) have submitted their perceptions of the national support measures of the Swiss elite sports system. These data provide an essential database for the further development of the Swiss elite sports system. The results were published in a report presenting the results divided into 40 Olympic summer and 14 winter sports (Olympic classification). The authors of this paper assume that, in practice, this division is too unspecific to assess where further measures would be needed. The aim of this paper is to find appropriate parameters for data visualization in order to identify disparities in sports promotion that allow an assessment of where further interventions by Swiss Olympic (NF umbrella organization) are required. Method: First, the variable 'salary earned from sport' was defined as a variable to measure the impact of elite sports promotion. This variable was chosen as a measure as it represents an important indicator for the professionalization of elite athletes and therefore reflects national level sports promotion measures applied by Swiss Olympic. Afterwards, the variable salary was tested with regard to the correlation between Olympic classification [a], calculating the Eta coefficient. To estimate the appropriate parameters for data visualization, the correlation between salary and four further parameters was analyzed by calculating the Eta coefficient: [a] sport; [b] prioritization (from 1 to 5) of the sports by Swiss Olympic; [c] gender; [d] employment level in sports. Results & Discussion: The analyses reveal a very small correlation between salary and Olympic classification (ɳ² = .011, p = .005). Gender demonstrates an even small correlation (ɳ² = .006, p = .014). The parameter prioritization was correlating with small effect (ɳ² = .017, p = .001) as did employment level (ɳ² = .028, p < .001). The highest correlation was identified by the parameter sport with a moderate effect (ɳ² = .075, p = .047). The analyses show that the disparities in sports promotion cannot be determined by a particular parameter but presumably explained by a combination of several parameters. We argue that the possibility of combining parameters for data visualization should be enabled when the analysis is provided to Swiss Olympic for further strategic decision-making. However, the inclusion of multiple parameters massively multiplies the number of graphs and is therefore not suitable for practical use. Therefore, we suggest to apply interactive dashboards for data visualization using Business Intelligence Software. Practical & Theoretical Contribution: This contribution provides the first attempt to use Business Intelligence Software for strategic decision-making in national level sports regarding the prioritization of national resources for sports and athletes. This allows to set specific parameters with a significant effect as filters. By using filters, parameters can be combined and compared against each other and set individually for each strategic decision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20visualization" title="data visualization">data visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title=" business intelligence"> business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=Swiss%20elite%20sports%20system" title=" Swiss elite sports system"> Swiss elite sports system</a>, <a href="https://publications.waset.org/abstracts/search?q=strategic%20decision-making" title=" strategic decision-making"> strategic decision-making</a> </p> <a href="https://publications.waset.org/abstracts/148988/use-of-a-business-intelligence-software-for-interactive-visualization-of-data-on-the-swiss-elite-sports-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148988.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">90</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); 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