Use-cases on evolution

This report presents a set of use cases for evolution and reactivity for data in the Web and Semantic Web. This set is organized around three different case study scenarios, each of them is related to one of the three different areas of application within Rewerse. Namely, the scenarios are: “The Rewerse Information System and Portal”, closely related to the work of A3 – Personalised Information Systems; “Organizing Travels”, that may be related to the work of A1 – Events, Time, and Locations; “Updates and evolution in bioinformatics data sources” related to the work of A2 – Towards a Bioinformatics Web

Managed Query Processing within the SAP HANA Database Platform

The SAP HANA database extends the scope of traditional database engines as it supports data models beyond regular tables, e.g. text, graphs or hierarchies. Moreover, SAP HANA also provides developers with a more fine-grained control to define their database application logic, e.g. exposing specific operators which are difficult to express in SQL. Finally, the SAP HANA database implements efficient communication to dedicated client applications using more effective communication mechanisms than available with standard interfaces like JDBC or ODBC. These features of the HANA database are complemented by the extended scripting engine–an application server for server-side JavaScript applications–that is tightly integrated into the query processing and application lifecycle management. As a result, the HANA platform offers more concise models and code for working with the HANA platform and provides superior runtime performance. This paper describes how these specific capabilities of the HANA platform can be consumed and gives a holistic overview of the HANA platform starting from query modeling, to the deployment, and efficient execution. As a distinctive feature, the HANA platform integrates most steps of the application lifecycle, and thus makes sure that all relevant artifacts stay consistent whenever they are modified. The HANA platform also covers transport facilities to deploy and undeploy applications in a complex system landscape

INFORMATION SHARING AND PRICE DYNAMICS IN B2B DIGITAL SYSTEMS

While multiple studies have investigated the digital ecosystems in the B2C sectors, empirical research on the upstream of the supply chain is still underexplored. This paper examines the case when a digital platform is incorporated into the century-old auction systems. This work offers insights into B2B markets and at the same time, an interesting instance where different pricing mechanisms (online posted price and auctions) co-exit. We investigate how the information of the new digital posted price channel can influence buyers’ learning behaviors and consequently, the price dynamics in the auction market. Our empirical analysis reveals that multiple information signals can play a role. While sellers’ high price and high-volume sales signals can partially dimmish the existing declining price trend in the sequential auctions where the prices from the earlier auction rounds tend to be higher than from the latter, this information effect does not persist over time. These results highlight the potential benefit of cooperating e-commerce with an auction channel for sellers and the shift in buyers’ behaviors in responding to an additional platform in a B2B market

On the map of B\"okstedt-Madsen from the cobordism category to AA-theory

B\"okstedt and Madsen defined an infinite loop map from the embedded $d$-dimensional cobordism category of Galatius, Madsen, Tillmann and Weiss to the algebraic $K$-theory of $BO(d)$ in the sense of Waldhausen. The purpose of this paper is to establish two results in relation to this map. The first result is that it extends the universal parametrized $A$-theory Euler characteristic of smooth bundles with compact $d$-dimensional fibers, as defined by Dwyer, Weiss and Williams. The second result is that it actually factors through the canonical unit map $Q(BO(d)_+) \to A(BO(d))$.Comment: 36 pages. This new version contains additional material and various other change

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

Influence of Complex Exciton-Phonon Coupling on Optical Absorption and Energy Transfer of Quantum Aggregates

We present a theory that efficiently describes the quantum dynamics of an electronic excitation that is coupled to a continuous, highly structured phonon environment. Based on a stochastic approach to non-Markovian open quantum systems, we develop a dynamical framework that allows us to handle realistic systems where a fully quantum treatment is desired yet the usual approximation schemes fail. The capability of the method is demonstrated by calculating spectra and energy transfer dynamics of mesoscopic molecular aggregates, elucidating the transition from fully coherent to incoherent transfer

Use-cases on reactivity

Since reactivity and evolution are tightly connected, use cases containing both aspects were collected in a single document, I5-D2, instead of compiling them in separate documents

Proteins associated with pancreatic cancer survival in patients with resectable pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with a dismal prognosis. However, while most patients die within the first year of diagnosis, very rarely, a few patients can survive for >10 years. Better understanding the molecular characteristics of the pancreatic adenocarcinomas from these very-long-term survivors (VLTS) may provide clues for personalized medicine and improve current pancreatic cancer treatment. To extend our previous investigation, we examined the proteomes of individual pancreas tumor tissues from a group of VLTS patients (survival ≥10 years) and short-term survival patients (STS, survival <14 months). With a given analytical sensitivity, the protein profile of each pancreatic tumor tissue was compared to reveal the proteome alterations that may be associated with pancreatic cancer survival. Pathway analysis of the differential proteins identified suggested that MYC, IGF1R and p53 were the top three upstream regulators for the STS-associated proteins, and VEGFA, APOE and TGFβ-1 were the top three upstream regulators for the VLTS-associated proteins. Immunohistochemistry analysis using an independent cohort of 145 PDAC confirmed that the higher abundance of ribosomal protein S8 (RPS8) and prolargin (PRELP) were correlated with STS and VLTS, respectively. Multivariate Cox analysis indicated that 'High-RPS8 and Low-PRELP' was significantly associated with shorter survival time (HR=2.69, 95% CI 1.46-4.92, P=0.001). In addition, galectin-1, a previously identified protein with its abundance aversely associated with pancreatic cancer survival, was further evaluated for its significance in cancer-associated fibroblasts. Knockdown of galectin-1 in pancreatic cancer-associated fibroblasts dramatically reduced cell migration and invasion. The results from our study suggested that PRELP, LGALS1 and RPS8 might be significant prognostic factors, and RPS8 and LGALS1 could be potential therapeutic targets to improve pancreatic cancer survival if further validated

Determining sample size in a personalized randomized controlled (PRACTical) trial

In clinical settings with no commonly accepted standard-of-care, multiple treatment regimens are potentially useful, but some treatments may not be appropriate for some patients. A personalized randomized controlled trial (PRACTical) design has been proposed for this setting. For a network of treatments, each patient is randomized only among treatments which are appropriate for them. The aim is to produce treatment rankings that can inform clinical decisions about treatment choices for individual patients. Here we propose methods for determining sample size in a PRACTical design, since standard power-based methods are not applicable. We derive a sample size by evaluating information gained from trials of varying sizes. For a binary outcome, we quantify how many adverse outcomes would be prevented by choosing the top-ranked treatment for each patient based on trial results rather than choosing a random treatment from the appropriate personalized randomization list. In simulations, we evaluate three performance measures: mean reduction in adverse outcomes using sample information, proportion of simulated patients for whom the top-ranked treatment performed as well or almost as well as the best appropriate treatment, and proportion of simulated trials in which the top-ranked treatment performed better than a randomly chosen treatment. We apply the methods to a trial evaluating eight different combination antibiotic regimens for neonatal sepsis (NeoSep1), in which a PRACTical design addresses varying patterns of antibiotic choice based on disease characteristics and resistance. Our proposed approach produces results that are more relevant to complex decision making by clinicians and policy makers