AN EVALUATION METHOD FOR BUSINESS MODELS IN PRODUCT-SERVICE SYSTEMS DESIGN

To pursue business innovation with PSS, many different PSS concepts are designed and evaluated. Various business models of a PSS design concept are devised and evaluated as well. Evaluation of the economic sustainability of PSS business models is critical. This paper presents a systematic method to evaluate the economic sustainability of PSS business models using a system dynamics modelling template. System dynamics modelling task is challenging for practitioners due to the variety of variables comprising business model strategies and their complex interrelationships. To enable the modelling task, a system dynamics modelling template composed of six modules of customer acquisition, channel acquisition, profit creation, resource acquisition, PSS provision, and partnership pattern has been devised. The PSS business model evaluation method has been illustrated using a smart study experience management service system design case to demonstrate the proposed system dynamics modelling template can reflect the case-specific business model which consists of the particular business model strategies.

Methodology

Because sustainable development only becomes defined when measured (see Chapter 3; e.g. Bell & Morse, 2008), sustainable development index construction is an unsupervised modelling task without a supervising output variable (G. James, Witten, Hastie & Tibshirani, 2013). Consequently, sustainable development measurement is diverse in methods and methodologies (see Section 3.2, Section 3.3, and Section 4.2) and hallmarked by subjectivity and arbitrariness (e.g. Böhringer & Jochem, 2007), such that sustainable development indicators are rather confusing and non-consensual (Pope et al., 2017; Ramos & Moreno Pires, 2013). To counteract this finding and to achieve objectivity in assessment (see Table 10.1007/978-3-658-33246-4_3; Sala et al., 2015), the previous theoretical research is coupled with a profound methodological research.

Learning Mathematics Through Mathematical Modelling Processes Within Sports Day Activity

In this study, we adapted the school sports day to provide opportunities to relate real-life situations with mathematical knowledge and skills. The purpose of this study was to describe the way that the teachers interact with their students and the students’ responses during mathematical modelling processes. The designing of the modelling task was inspired by the Realistic Fermi Problems about the bleacher in the school sports day. The modelling task was designed by a collaboration of mathematics teachers and educators and experimented with 10th-grade students. Each experiment lasted for 45 minutes and was conducted in the one-day camp with 45 students. The results showed that the students who had no previous experience of mathematical modelling engaged in mathematical modelling processes with their friends under the guidance and supporting of the teacher. Most of them were able to think, make assumptions, collect data, observe, make conjectures and create mathematical models to understand and solve the modelling task.

Situation Awareness Under Task Complexity

Performing tasks under task complexity (TC), for example, during the management of a crisis, can be challenging. One relevant research stream has so far dealt with modelling task complexity while another research stream has established the importance of situation awareness (SA) during crisis management. This study takes into consideration these two research streams and builds a model on how SA is achieved under task complexity. The research shows that information of high information quality (IQ) reduces the level of task complexity, and influences—as well as is influenced by—situation awareness. The practice of collecting and disseminating relevant and timely information as a critical resource in improving SA should be carried out continuously. This continuous process can be improved by using information technologies as automating tools. Moreover, the study shows how shared mental models can improve SA under task complexity. A case study approach, based on qualitative data focusing on theory building, is applied. Unit of analysis is a Norwegian hospital.

Towards Fully 8-bit Integer Inference for the Transformer Model

8-bit integer inference, as a promising direction in reducing both the latency and storage of deep neural networks, has made great progress recently. On the other hand, previous systems still rely on 32-bit floating point for certain functions in complex models (e.g., Softmax in Transformer), and make heavy use of quantization and de-quantization. In this work, we show that after a principled modification on the Transformer architecture, dubbed Integer Transformer, an (almost) fully 8-bit integer inference algorithm Scale Propagation could be derived. De-quantization is adopted when necessary, which makes the network more efficient. Our experiments on WMT16 En<->Ro, WMT14 En<->De and En->Fr translation tasks as well as the WikiText-103 language modelling task show that the fully 8-bit Transformer system achieves comparable performance with the floating point baseline but requires nearly 4x less memory footprint.

Mathematical models and meanings by school and university students in a modelling task

This study involves two classes from different educational levels, namely 9th grade and university. Students in both contexts were given a modelling task that required the development of a hand biometrics recognition system, during which they performed experimentation and simulation. As aims of the study, we look for distinctions and commonalities between the models developed in the two classes and seek to know how simulation and experimentation influence students’ production of meaning. The theoretical framework comprises the relationship between the modelling process and the prototyping process and adopts Peirce’s pragmatic perspective on meaning. The research is of a qualitative nature, assuming the characteristics of a case study. The results reveal many commonalities between the modelling in the two contexts. Moreover, experimentation and simulation were relevant elements for the production of meaning by the students, which is endorsed by a pragmatic perspective on meaning.

Bayesian Model Weighting: The Many Faces of Model Averaging

Model averaging makes it possible to use multiple models for one modelling task, like predicting a certain quantity of interest. Several Bayesian approaches exist that all yield a weighted average of predictive distributions. However, often, they are not properly applied which can lead to false conclusions. In this study, we focus on Bayesian Model Selection (BMS) and Averaging (BMA), Pseudo-BMS/BMA and Bayesian Stacking. We want to foster their proper use by, first, clarifying their theoretical background and, second, contrasting their behaviours in an applied groundwater modelling task. We show that only Bayesian Stacking has the goal of model averaging for improved predictions by model combination. The other approaches pursue the quest of finding a single best model as the ultimate goal, and use model averaging only as a preliminary stage to prevent rash model choice. Improved predictions are thereby not guaranteed. In accordance with so-called M -settings that clarify the alleged relations between models and truth, we elicit which method is most promising.