Future prospects for exploring present day anomalies in flavour physics measurements with Belle II and LHCb

A range of flavour physics observables show tensions with their corresponding Standard Model expectations: measurements of leptonic flavour-changing neutral current processes and ratios of semi-leptonic branching fractions involving different generations of leptons show deviations of the order of four standard deviations. If confirmed, either would be an intriguing sign of new physics. In this manuscript, we analyse the current experimental situation of such processes and for the first time estimate the combined impact of the future datasets of the Belle II and LHCb experiments on the present tensions with the Standard Model expectations by performing scans of the new physics contribution to the Wilson coefficients. In addition, the present day and future sensitivity of tree-level CKM parameters, which offer orthogonal tests of the Standard Model, are explored. Three benchmark points in time are chosen for a direct comparison of the estimated sensitivity between the experiments. A high complementarity between the future sensitivity achieved by the Belle II and LHCb experiments is observed due to their relative strengths and weaknesses. We estimate that all of the anomalies considered here will be either confirmed or ruled out by both experiments independently with very high significance by the end of data-taking at Belle II and the LHCb upgrade

Modified Shape of Dynamic Master Curves due to Adiabatic Effects

AbstractWithin a joint project of IWM/Freiburg and MPA/Stuttgart the fracture toughness of a 22 NiMoCr 3 7 steel (A 508 Cl.2) was characterized at IWM with SE(B)10/10- und SE(B)40/20-specimens at -20 °C and high crack loading rates in the range of 103 to 106 MPa√m s-1, see Böhme et al. (2012 and 2013). The single temperature Master Curve evaluation according to ASTM E1921 and Wallin (2011) resulted in part in 5%-lower-bound fracture toughness versus temperature curves below the deterministic ASME lower bound KIR-reference-curve. At a first glance, this seems to violate the ASME KIR-concept, however, possibly this just indicates, that the conventional MC-evaluation has to be modified for elevated loading rates. Adiabatic heating in the vicinity of the crack tip could be one reason for that, as already argued in Schindler (2013 and 2015).Therefore, additional SE(B)-tests at temperatures of -20 °C, 0 °C and +20 °C were performed at IWM within the current follow-up joint IWM-MPA project. The new IWM-results show in agreement with previous investigations by Viehrig et al. (2010) and Schindler et al. (2013 and 2015) that the Master Curves at elevated loading rates are steeper than at quasistatic loading, probably due to local adiabatic heating in the vicinity of the crack tip. Therefore, the temperature field around the crack tip has been measured with a high speed infrared camera and has been compared to results of a numerical simulation. Up to crack initiation, a local adiabatic rise in temperature of the order of magnitude of about 60 K was measured and calculated in the vicinity of the crack tip at a crack loading rate of about 106 MPa√m s-1. In order to take into account this adiabatic effect, the dynamic master curves were evaluated by applying an adjusted MC shape parameter. This finally leads to more plausible results for the dynamic Master Curves. Thus, the choice of a rate dependent shape parameter p should be considered for future modifications of the elevated loading rate appendix of ASTM E1921

Using Smart Mobile Devices for Collecting Structured Data in Clinical Trials: Results From a Large-Scale Case Study

In future, more and more clinical trials will rely on smart mobile devices for collecting structured data from subjects during trial execution. Although there have been many projects demonstrating the benefits of mobile digital questionnaires, the scenarios considered in literature have been rather limited so far. In particular, the number of subjects is rather low in respective studies and a well controllable infrastructure is usually presumed, which not always applies in practice. This paper gives insights into the lessons learned in a clinical psychology trial when using tablets for mobile data collection. In particular, more than 1.700 subjects have participated so far, providing us with valuable feedback on collecting trial data with smart mobile devices in the large scale. Furthermore, issues related to an insufficient infrastructure (e.g., unstable Internet connections) have been addressed as well. Overall, the paper provides valuable insights gained during trial execution. In future, electronic questionnaires executable on smart mobile devices will replace paper-based ones

Towards an Evaluation Framework for Business Process Integration and Management

Process-awareness in enterprise computing is a must in order to adequately support business processes. Particularly the interoperability of the (process-oriented) business information systems and the management of a company’s process map are difficult to handle. Process-oriented approaches (like workflow systems and enterprise application integration tools) offer promising perspectives in this respect. However, a major problem for project managers is the accomplishment of economic-oriented assessments of such approaches. Currently, there exists no suitable evaluation framework. This position paper discusses important issues related to the introduction of such a framework. Doing so, we distinguish two evaluation areas: Business Process Integration and Business Process Management. While the former operates at the technical level of process and application integration, the latter addresses organizational process topics. Starting from those two perspectives we describe benefits, evaluation criteria and metrics that are relevant to set up an evaluation framework

Unleashing the Effectiveness of Process-oriented Information Systems: Problem Analysis, Critical Success Factors and Implications

Process-oriented information systems (IS) aim at the computerized support of business processes. So far, contemporary IS have often fail to meet this goal. To better understand this drawback, to systematically identify its rationales, and to derive critical success factors for business process support, we conducted three empirical studies: an exploratory case study in the automotive domain, an online survey among 79 IT professionals, and another online survey among 70 business process management (BPM) experts. This paper summarizes the findings of these studies, puts them in relation with each other, and uses them to show that ”process-orientation” is scarce and ”process-awareness” is needed in IS engineering

Towards Process-Aware Enterprise Software Environments - A Framework

To stay competitive at the market companies must tightly interlink their software systems in a process-oriented manner. While the business process paradigm has been widely accepted in practice, the majority of current software applications are still not yet implemented in a process-oriented way. But even if, process logic is hard-wired in the application code leading to inflexible and rigid software systems that do not reflect business needs. In such a scenario the quick adaptation of the software systems to changed business processes is almost impossible. Therefore, many software systems are already out of date at the time they are introduced into practice, and they generate high maintenance costs in the following. Due to this unsatisfactory business process support a software system’s return on investment is often low. By contrast technologies which enable the realization of process-aware enterprise software environments will significantly contribute to improve the added value of IT to a company’s business. In this paper we characterize process-aware enterprise software environments, describe benefits and present a conceptual framework outlining our theses

Requirements for a Flexible and Generic API Enabling Mobile Crowdsensing mHealth Applications

Presently, mHealth becomes increasingly important in supporting patients in their everyday life. For example, diabetes patients can monitor themselves by the use of their smartphones. On the other, clinicians as well as medical researchers try to exploit the advantages of mobile technology. More specifically, mHealth applications can gather data in everyday life and are able to easily collect sensor or context data (e.g., the current temperature). Compared to clinical trials, these advantages enable mHealth applications to gather more data in a rather short time. Besides, humans often behave atypically in a clinical environment and, hence, mHealth applications collect data in a setting that reflects the daily behavior more naturally. Hitherto, many technical solutions emerged to deal with such data collection settings. Mobile crowdsensing is one prominent example in this context. We utilize the latter technology in a multitude of large-scale projects to gather data of several chronic disorders. In the TrackYourTinnitus project, for example, we pursue the goal to reveal new medical insights to the tinnitus disorder. We learned in the realized projects that a sophisticated API must be provided to cope with the requirements of researchers from the medical domain. Notably, the API must be able to flexibly deal with requirement changes. The work at hand presents the elicited requirements and illustrate the pillars on which our flexible and generic API is built on. Although we identified that the maintenance of such an API is a challenging endeavor, new data evaluation opportunities arise that are promising in the context of chronic disorder management

A Lightweight Process Engine for Enabling Advanced Mobile Applications

The widespread dissemination of smart mobile devices offers new perspectives for timely data collection in large-scale scenarios. However, realizing sophisticated mobile data collection applications raises various technical issues like the support of different mobile operating systems and their platform-specific features. Often, specifically tailored mobile applications are implemented in order to meet particular requirements. In this context, changes of the data collection procedure become costly and profound programming skills are needed to adapt the respective mobile application accordingly. To remedy this drawback, we developed a model-driven approach, enabling end-users to create mobile data collection applications themselves. Basis to this approach are elements for flexibly defining sophisticated questionnaires, called instruments, which not only contain information about the data to be collected, but also on how the instrument shall be processed on different mobile operating systems. For the latter purpose, we provide an advanced mobile (kernel) service that is capable of processing the logic of sophisticated instruments on various platforms. The paper discusses fundamental requirements for such a kernel and introduces a generic architecture. The feasibility of this architecture is demonstrated through a prototypical implementation. Altogether, the mobile service allows for the effective use of smart mobile devices in a multitude of different data collection application scenarios (e.g., clinical and psychological trials)

Supporting Remote Therapeutic Interventions with Mobile Processes

Many studies have revealed that homework (e.g., relaxation exercises) are crucial for remote therapeutic inter-ventions. In this context, to monitor whether patients actually perform their homework and to check whether they perform it in the right way constitute complex tasks. So far, therapeutic interventions have not been properly supported by IT systems and, hence, the opportunities provided by mobile assistance have been neglected. For example, a smart mobile device may notify a patient about an assigned homework or motivate him to accomplish it in time. Moreover, the patient might be further assisted through a video providing detailed instructions. In turn, the smart mobile device could inform the therapist of the homework outcome. In practice, a proper support of the various types of homework is challenging, even when using modern IT systems. To remedy this drawback, we propose an approach integrating mobile services with process management technology in order to enable the complex coordination tasks that become necessary in connection with homework. For example, a process might enable remote monitoring of home-work, giving therapists the opportunity of timely adjustments. In addition, the approach allows involving researchers by providing them with valuable data (e.g., heart rate) gathered during and after homework. This paper presents an approach for creating processes that run on smart mobile devices and enable flexible remote therapeutic intervention support. Such mobile approach significantly enhances therapy assistance on one hand and mobile homework-related scenarios on the other

Towards Flexible Mobile Data Collection in Healthcare

The widespread dissemination of smart mobile devices offers promising perspectives for a variety of healthcare data collection scenarios. Usually, the implementation of mobile healthcare applications for collecting patient data is cumbersome and time-consuming due to scenario-specific requirements as well as continuous adaptations to already existing mobile applications. Emerging approaches, therefore, aim to empower domain experts to create mobile data collection applications themselves. This paper discusses flexibility issues considered by a generic and sophisticated framework for realizing mobile data collection applications. Thereby, flexibility is discussed along different phases of data collection scenarios. Altogether, the realized flexibility significantly increases the practical benefit of smart mobile devices in healthcare data collection scenarios