Real-time App Research Articles

Using a mobile application to test the impact of giving feedback onteam-level conflict

The authors explored using a mobile application called DevelapMe for 278 business undergraduates in 54 teams to give real-time peer-based feedback during a 5-week group project. Two online surveys, Time 1 (T1) and Time 2 (T2), were administered. Results included finding three promising, new give-feedback scales: positive rating with comments, negative rating with comments, and negative/neutral rating without comments. Relationship conflict and task conflict were the outcomes. Relationship conflict significantly increased from T1 to T2. Only positive rating with comments had a significant relationship to T2 relational conflict and task conflict. Research exposing students to real-time feedback apps will prepare them for future work in teams.

Cloud computing and web application-based remote real-time monitoring and data analysis: slurry injection case study, Onshore USA

Remote live monitoring of field operations, such as injection, has been very restricted, although real-time data are often collected at field sites. The difficulties lie in the data access and limitations to obtain computing resources for data analysis, which restricts the engineers’ abilities to provide useful and timely remote assessment and assurance to the operations. Cloud computing combined with web-based apps, however, makes it much easier and cheaper to monitor field operations in real time from anywhere around world. The current work provides our first attempt to apply the cloud computing and web-powered apps to monitor slurry injection at one injection site in Texas, USA. The site provides injection data that is stored automatically in a cloud database. The data are accessed and analyzed remotely through a web-based app in real time. Monitored injection pressure and rate provide the basis for pressure fall off analysis. If the fall off analysis yields an unanticipated fracture geometry, advanced 3D fracture simulations would be conducted to gain a better understanding of the effects of a specific injection on fracture geometry. The results of remote real-time data analysis set up early warnings to alert both onsite and offsite staff ahead of operational upsets. Compared to traditional desktop applications and isolated local data servers, cloud computing and web-based apps provide a more convenient and cost-effective way to monitor field operations in real time. The technique and workflow presented here may also be applicable to monitor other field operations.

Integrating Signal Processing Modules of Hearing Aids into a Real-Time Smartphone App.

This paper presents the integration of three major modules of the signal processing pipeline that go into a typical digital hearing aid as a real-time smartphone app. These modules include voice activity detection, noise reduction, and compression. The steps taken to allow the real-time implementation of this integration or signal processing pipeline are discussed. These steps can be utilized to create similar signal processing pipelines or integrated apps to evaluate hearing improvement algorithms. The real-time characteristics of the developed integrated app are reported as well as an objective evaluation of its noise reduction.

Mediated Hook-Up

This study explores the uses and sought gratifications of MoMo—a location-based real-time app, among Chinese college students seeking for hook-ups. The results show that MoMo users were motivated by two gratifications—sexual freedom and online inclusion. Results from regression analysis illustrated that loneliness was a strong predictor of both gratifications. Additionally, loneliness, self-esteem, and to a lesser extent, both gratifications significantly predicted patterns of MoMo usage for hook-up. This study has filled the research gap between the examination of youth-hook-up app relationship in China and the exploration of heterosexual college community's gratifications-sought from MoMo use.

A novel approach for the prediction of treadmill test in cardiology using data mining algorithms implemented as a mobile application

ObjectiveTo develop a mobile app called “TMT Predict” to predict the results of Treadmill Test (TMT), using data mining techniques applied to a clinical dataset using minimal clinical attributes. To prospectively test the results of the app in realtime to TMT and correlate with coronary angiogram results. MethodsIn this study, instead of statistics, data mining approach has been utilized for the prediction of the results of TMT by analyzing the clinical records of 1000 cardiac patients. This research employed the Decision Tree algorithm, a new modified version of K-Nearest Neighbor (KNN) algorithm, K-Sorting and Searching (KSS). Furthermore, curve fitting mathematical technique was used to improve the Accuracy. The system used six clinical attributes such as age, gender, body mass index (BMI), dyslipidemia, diabetes mellitus and systemic hypertension. An Android app called “TMT Predict” was developed, wherein all three inputs were combined and analyzed. The final result is based on the dominating values of the three results. The app was further tested prospectively in 300 patients to predict the results of TMT and correlate with Coronary angiography. ResultsThe accuracy of predicting the result of a TMT using data mining algorithms, Decision Tree and K-Sorting & Searching (KSS) were 73% and 78%, respectively. The mathematical method curve fitting predicted with 82% accuracy. The accuracy of the mobile app “TMT Predict”, improved to 84%. Age-wise analysis of the results show that the accuracy of the app dips when the age is more than 60years indicating that there may be other factors like retirement stress that may have to be included. This gives scope for future research also. In the prospective study, the positive and negative predictive values of the app for the results of TMT and coronary angiogram were found to be 40% and 83% for TMT and 52% and 80% for coronary angiogram. The negative predictive value of the app was high, indicating that it is a good screening tool to rule out coronary artery heart disease (CAHD). Conclusion“TMT Predict” is a simple user-friendly android app, which uses six simple clinical attributes to predict the results of TMT. The app has a high negative predictive value indicating that it is a useful tool to rule out CAHD. The “TMT Predict” could be a future digital replacement for the manual TMT as an initial screening tool to rule out CAHD.

Design of keystream Generator utilizing Firefly Algorithm

Stream cipher is one of encryption procedures for sending data in internet; stream cipher is suitable in telecommunications and real-time apps. The robustness measurement of stream cipher is according to the randomness of keystream that is utilized. If the random series of keystream generator is low, the keystream of stream cipher can be read and encrypted data by stream cipher become vulnerable to attackers. This paper utilizes Firefly Algorithm based Local Key Generation for generation keystream. The generated keystream is independent of original messages. The randomness of keystream series of Firefly passing the five standard criteria. The suggested keystream generator is word-established appropriated to fast real-time apps than are bit-established linear stream ciphers. Furthermore, the suggested keystream generator satisfies the three demands of benchmarks such as maximum correlation, robust randomness and huge complexity.

Computerized decision support systems: EBM at the bedside

One of the aims of Evidence-Based Medicine is to improve quality and appropriateness of care by the expedition of the knowledge transfer process. Computerized Decision Support Systems (CDSSs) are computer programs that provide alerts to the prescribing doctor directly at the moment of medical examination. In fact, alerts are integrated within the single patient electronic health record. CDSS based on the best available and updated evidence and guidelines may be an efficient tool to facilitate the transfer of the latest results from clinical research directly at the bedside, thus supporting decision-making. The CODES (COmputerized DEcision Support) trial is a research program funded by the Italian Ministry of Health and the Lombardy Region. It aims to evaluate the feasibility of the implementation of a CDSS at the hospital level and to assess its efficacy in daily clinical practice. The CODES project includes two pragmatic RCTs testing a CDSS (i.e. the EBMeDS - MediDSS) in two large Italian hospitals: the first is a general hospital in Vimercate (Lombardy), the second is an oncologic research center in Meldola (Emilia Romagna). The CDSS supports a full spectrum of decisions: therapy, drug interactions, diagnosis, and management of health care services are covered by a hundreds of reminders. However only few reminders are activated per patient, highlighting crucial problems in the delivery of high-quality care. The two trials have similar design and primary outcome, the rate at which alerts detected by the software are resolved by a decision of the clinicians. The project also includes the assessment of barriers and facilitators in the adoption of these new technologies by hospital staff members and the retrospective evaluation of the repeated risks in prescription habits. The trials are ongoing and currently more than 10,000 patients have been randomized. The qualitative analysis revealed a progressive shift in the perception of the tool. Doctors are now seeing it as a trusted second opinion, available 24/7, which is tailored to the needs of the patient. The retrospective analysis showed the opportunity to achieve a better healthcare quality through an active risk management. Aggregating data from whole hospitals emerge rare drug interactions that otherwise would not be recognizable. CDSS are promising tools to support clinicians in everyday practice. They can be used as a real time app or to perform retrospective analyses. These data can provide unique resources to hospital management.

Improving Business Intelligence Applications by Using New Generation of Web and Mobile Technologies

1Introduction - Fiori Apps with SAPUI5S AP Fiori comes with modern design for a rediscovered user experience. SAP Fiori user experience (UX) represents a personalized user experience for multidevices, responsiveness interface and with deployment options. When combined with the power of the SAP HANA platform, SAP Fiori provides exclusive responsiveness. SAP Fiori UX is used for enterprise apps across the most business scenarios. It provides performant usability on multiple devices for the best business correlations and ease of use. Now users can be happy with these real-time apps delivered with the help of Fiori apps. Below are described specific characteristics of Fiori apps [1], [2].- Fiori apps are in fact Web apps rendered via browsers like: IE, Safari, Google Chrome;- Multi-device enablement: same app can be launched from the desktop and any mobile device [3];- Responsive design: rendering of an app adapts to the device (desktop or mobile device) from which it is launched;- Role-based: user launches the Fiori apps from a home page in which all apps assigned to the user's role are bundled;- SAP UI5 in Blue Crystal design.2 Modelling in SAP HANASAP HANA ambition is to deliver across all five dimensions: deep, high-speed, simple, real-time and broad. Also SAP HANA platform makes real to combine OLTP and OLAP patterns in one application. This was possible because of the evolution of hardware innovations. In detail, these innovations are: multicore architecture (8 CPUs x (8-16) cores per blade) and address space (64-bit address space) [4], [9].The features of Modelling in SAP HANA are as follows [5], [6]:- The types of Joins available in SAP HANA are: Referential, Inner, Left Outer, Right Outer and Text Join;- Information Models (SAP HANA Views) are optimized for SAP HANA Engines and Calculation Operators;- Columns can be Attributes or Measures in SAP HANA Views. Attributes are descriptive data, while Measures are key figures with measured data;- Using for modelling the specific views: Attribute View -> Analytical View -> Calculation View.Table 1 below presents the strongest benefits of SAP HANA Views. It is realized a comparative analysis of the Attribute View, Analytical View and Calculation View [7], [8]. As displayed in Table 1, each view has its own characteristics and each of them can be used for Modelling in SAP HANA.3 Applications Using the Future Innovation of TechnologiesTo emphasize what can be done with the mentioned above technologies, I present in this paragraph some examples of applications which use the latest and the best technologies. The first example of application developed with the mentioned technologies is 'Measurement Objects Repository application' which brings the following main features:- Interfaces to Master data: Product Taxonomy, Solution Taxonomy and Product Feature;- Measurement Object assignment to multiple reporting structure (Product / Solution / Product Feature and Product Owner defined);Flexible maintenance of package hierarchy like copy, move of packages to other hierarchyDirect navigation to report maintenance screens from usage reports;Changes to report structure are immediately applied;- Only selected users can maintain the taxonomy structure and add Measurement Objects of their area only.In Figure 1 below is represented the Measurement Objects Repository application process flow:As described in Figure 1 users can see possible options in Measurement Object/View repository. Also users can navigate further in the Measurement Object/View repository.If necessary the users can maintain the Measurement Object/View or can create measurement objects. After setting up the view the user can check the usage data inside the application.In order to understand the objective of the application that is presented below in Figure 2, where it is shown the following architecture:In order to ilustrate the user interface of the app, I have inserted below some relevant print screens. …