TOWARDS DIGITAL BUSINESS PROCESS MODELS IN HIGHER EDUCATION INSTITUTIONS: A CASE STUDY BASED ON THE ONBOARDING OF STUDENT EMPLOYEES

Digitalization is one of the major challenges which also affects higher education institutions. However, a lot of organizational business processes in the administration of higher education institution are still analog and paper-based. We conducted an in-depth case study at one of the top-ranked higher education institutions based on the onboarding process of student employees by applying a five-step approach focussing on the first three steps: (i) qualitative analysis of the case itself, (ii) development of a common denominator, and (iii) develop a new digital business process model derived from a concept matrix. The results contribute to theory with a digital business model in higher education institutions. Research can use the results to develop measurements for new digital skills and competencies and implications for other related areas such as participatory user design, artificial intelligence, e.g., contract validation, and outsourcing/offshoring, all business processes involving multiple stakeholders who bring in different resources to the process. Practitioners can apply the digital business model to discuss this topic and enable them to set up appropriate relationships with other organizations. Organizations can apply this concept for value co-creation in their networks

Analyzing Measures for the Construct “Energy-Conscious Driving”: A Synthesized Measurement Model to Operationalize Eco-Feedback

During the last several years, a large number of studies have dealt with eco-driving and have defined rules for driving vehicles more ecologically, eco-friendly, and energy efficiently. These rules are vague or insufficient for achieving their purpose, and the construct “energy- conscious driving” is unsatisfactorily defined. To structure available research and develop a more extensive concept of energy-conscious driving, a measurement model for energy- conscious driving is introduced. The model stems from a literature review conducted to identify six groups of measures for energy-conscious driving, and a synthesis of these groups to identify dependencies between them. This paper contributes to theory by building on existing knowledge on eco-driving through an analysis of available literature and describing dependencies between our six measures of energy-conscious driving. Based on our model, researchers can evaluate different eco-feedback designs and practitioners can implement more specific eco-feedback systems for improved user performance

Towards Crime Prevention Using Big Data Analytics: A Literature Review with an Explorative Case Study

Since the popularization of the big data concept, it has been implemented in various areas. Contemporary literature has proved the potential of using big data in crime prevention. In this research paper, we examine research on big data being used in Crime Prevention while implementing an author-centric to a concept-centric research approach. We also present the foundation for future research by analyzing data derived from the City of Chicago. We identified the neighborhoods in Chicago that are statistically more prone to crimes and used data of the last 10 years to make our observations. We additionally provide further discussion points for future research purpose

Statistical name detector

Abstract This paper is about statistical name detection. This is a way you can generate information for different organizations for example newspapers. It helps to identify locations, organizations, persons and miscellaneous, which could not be directly attached to the three other categories. Over three different feature vectors the F-measure is increased up to 46.17%

Context-Dependent Information Elements in the Car: Explorative Analysis of Static and Dynamic Head-Up-Displays

Head-up-displays (HUDs) illustrate a particular static number of information elements in the driver’s primary field of view. Since the display can obscure the reality, a dynamic HUD presents context-dependent information elements. To become familiar with a user-optimal number of information elements and its essential information elements, we conducted a user study with n = 183 participants. We focused the context on an urban, a rural and a highway trip. Afterwards, a within-subject experiment using a high-fidelity driving simulator (n = 27) reveals the following: Dynamic HUDs significantly lower the average over speeding by 3.45 km/h compared to static HUDs. This speed above the speed limit equals 15.33% of the average speed in urban areas. Steering angle and speed can capture the context. Practitioners can use these findings to decrease the number of information elements in HUDs, thereby possibly increasing traffic safety

Decision Support for Data Virtualization based on Fifteen Critical Success Factors: A Methodology

Data analysis is important for creating a competitive advantage, but the amount of data is already massive and increasing rapidly. Practitioners are looking for general models for different use cases in deciding whether to virtualize data or not and when it is applicable. However, there is a research gap in such models. Thus, in this study, we applied a design science approach in a further step to develop an IT artifact. It is derived from 15 critical success factors, building the foundation for a heuristic individual decision support on data virtualization. In addition, we calculate a final score that recommends extract transfer and load (ETL), hybrid, or data virtualization. The score adapts flexibly to business needs and helps practitioners make decisions. This IT artifact extends the knowledge base by a new methodology for decision support in data virtualization

Physical Models for Accreting Pulsars at High Luminosity

A new window for better understanding the accretion onto strongly magnetized neutron stars in X-ray binaries is opening. In these systems the accreted material follows the magnetic field lines as it approaches the neutron star, forming accretion columns above the magnetic poles. The plasma falls toward the neutron star surface at near-relativistic speeds, losing energy by emitting X-rays. The X-ray spectral continua are commonly described using phenomenological models, i.e., power laws with different types of curved cut-offs at higher energies. Here we consider high luminosity pulsars. In these systems the mass transfer rate is high enough that the accreting plasma is thought to be decelerated in a radiation-dominated radiative shock in the accretion columns. While the theory of the emission from such shocks had already been developed by 2007, a model for direct comparison with X-ray continuum spectra in xspec or isis has only recently become available. Here we analyze the broadband X-ray spectra of the accreting pulsars Centaurus X-3 and 4U1626-67 obtained withNuSTAR. We present results from traditional empirical modeling as well as successfully apply the radiation-dominated radiative shock model. We also fit the energy-dependent pulse profiles of 4U 1626-67 using a new relativistic light bending model

Gamma-glutamyltransferase is a strong predictor of secondary sclerosing cholangitis after lung transplantation for COVID-19 ARDS

Background: Lung transplantation (LTx) can be considered for selected patients suffering from COVID-19 acute respiratory distress syndrome (ARDS). Secondary sclerosing cholangitis in critically ill (SSC-CIP) patients has been described as a late complication in COVID-19 ARDS survivors, however, rates of SSC-CIP after LTx and factors predicting this detrimental sequela are unknown. Methods: This retrospective analysis included all LTx performed for post-COVID ARDS at 8 European LTx centers between May 2020 and January 2022. Clinical risk factors for SSC-CIP were analyzed over time. Prediction of SSC-CIP was assessed by ROC-analysis. Results: A total of 40 patients were included in the analysis. Fifteen patients (37.5%) developed SSC-CIP. GGT at the time of listing was significantly higher in patients who developed SSC-CIP (median 661 (IQR 324-871) vs 186 (109-346); p = 0.001). Moreover, higher peak values for GGT (585 vs 128.4; p < 0.001) and ALP (325 vs 160.2; p = 0.015) were found in the ‘SSC’ group during the waiting period. Both, GGT at the time of listing and peak GGT during the waiting time, could predict SSC-CIP with an AUC of 0.797 (95% CI: 0.647-0.947) and 0.851 (95% CI: 0.707-0.995). Survival of ‘SSC’ patients was severely impaired compared to ‘no SSC’ patients (1-year: 46.7% vs 90.2%, log-rank p = 0.004). Conclusions: SSC-CIP is a severe late complication after LTx for COVID-19 ARDS leading to significant morbidity and mortality. GGT appears to be a sensitive parameter able to predict SSC-CIP even at the time of listing