Why Uncertainty and Sustainability will be Key Drivers of Business Model Innovation

A conversation with Lorenzo Massa – interviewed by Christian Nielse

The business model: Theoretical roots, recent developments, and future research

The paper provides a broad and multifaceted review of the received literature on business models, in which we attempt to explore the origin of the construct and to examine the business model concept through multiple disciplinary and subject-matter lenses. The review reveals that scholars do not agree on what a business model is, and that the literature is developing largely in silos, according to the phenomena of interest to the respective researchers. However, we also found some emerging common ground among students of business models. Specifically, i) the business model is emerging as a new unit of analysis; ii) business models emphasize a system-level, holistic approach towards explaining how firms do business; iii) organizational activities play an important role in the various conceptualizations of business models that have been proposed, and iv) business models seek not only to explain the ways in which value is captured but also how it is created. These emerging themes could serve as important catalysts towards a more unified study of business models.Business model; strategy; technology management; innovation; literature review;

Automated Pain Assessment from Facial Expressions of Elderly People using Machine Learning

This thesis presents the development of an advanced machine learning model designed to accurately assess pain levels in dementia patients residing in elderly care homes. The project, conducted in collaboration with Sentigrate, a start-up focused on data science company, aims to create a predictive model that assigns pain scores ranging from 0 (no pain) to 6 (maximum pain) based on facial expressions. The research employs computer vision techniques, primarily convolutional neural networks, to extract meaningful features from facial images. A comparative study of various predictive techniques is conducted to determine the most effective approach. This project addresses the critical issue of inadequate pain management in dementia patients due to communication challenges. The objective is to provide an objective pain assessment tool that will significantly improve pain management strategies and enhance the quality of life for dementia patients in elderly care settings. The findings of this research have the potential to transform elderly care practices, offering valuable insights into pain management and contributing to the broader field of healthcare technology

Sustainable Business Model Design

This article introduces the “Sustainable Business Model Design” (SBMD) framework, an integrative methodology that synthesises sustainable business model theory with Alexandrian pattern theory. Emphasising a pragmatic interpretation of design as transformative action, the framework’s foundations are explored, seeking to consolidate the theoretical underpinnings guiding SBMD and elucidate its principal conceptual components. The article further delves into the practical application of the framework as a tool for problem-solving and idea generation. It concludes with a discussion of analogical reasoning and conceptual combination, shedding light on the creativity-enhancing efficacy of SBMD patterns. Additionally, the article is a succinct primer for business designers interested in the practical utilisation of SBMD, particularly within contexts such as sustainability innovation and ESG strategy workshops

Learned Global Optimization for Inverse Scattering Problems -- Matching Global Search with Computational Efficiency

The computationally-efficient solution of fully non-linear microwave inverse scattering problems (ISPs) is addressed. An innovative System-by-Design (SbD) based method is proposed to enable, for the first time to the best of the authors knowledge, an effective, robust, and time-efficient exploitation of an evolutionary algorithm (EA) to perform the global minimization of the data-mismatch cost function. According to the SbD paradigm as suitably applied to ISPs, the proposed approach founds on (i) a smart re-formulation of the ISP based on the definition of a minimum-dimensionality and representative set of degrees-of-freedom (DoFs) and on (ii) the artificial-intelligence (AI)-driven integration of a customized global search technique with a digital twin (DT) predictor based on the Gaussian Process (GP) theory. Representative numerical and experimental results are provided to assess the effectiveness and the efficiency of the proposed approach also in comparison with competitive state-of-the-art inversion techniques

Design of Clustered Phased Arrays by Means of an Innovative Power Pattern Matching-Driven Method -- The Linear Array Case

The design of sub-arrayed phased arrays (PAs) with sub-array-only amplitude and phase controls that afford arbitrary-shaped power patterns matching reference ones is addressed. Such a synthesis problem is formulated in the power pattern domain and an innovative complex-excitations clustering method, which is based on the decomposition of the reference power pattern in a number of elementary patterns equal to the array elements, is presented. A set of representative results is reported to illustrate the features of the proposed approach as well as to assess its effectiveness in comparison with benchmark results from the state-of-the-art (SoA) excitation matching-based clustering methods

Measurement of the t-tbar differential cross section at large top quark transverse momentum in sqrt(8) TeV pp collisions using the ATLAS detector at the LHC

The top quark is the heaviest particle in Standard Model. When it is produced with a large Lorentz boost, its decay products tend to overlap, making the standard reconstruction techniques inefficient; large R jet substructure analysis techniques allow to increase the detection efficiency for these events. Various differential cross section measurements of boosted t-tbar from pp collisions with sqrt(s)=8 TeV are presented: with respect to the mass, to the transverse momentum and the pseudorapidity of the t-tbar system. The results here have been obtained using a sample of 20 fb^-1, recorded by ATLAS during 2012. The events are selected with a cut-based approach in the single lepton plus jets decay channel, where the lepton can be either an electron or a muon. The final background-subtracted distributions are corrected for the distortion introduced by the detector and selection effects using unfolding methods. The measurements are dominated by the systematic uncertainties, and are in agreement with the Standard Model, even if it can be seen a general tendency of the theoretical predictions to overestimate the measured cross section for increasing transverse momentum and mass of the t-tbar system

Dynamical moments reveal a topological quantum transition in a photonic quantum walk

Many phenomena in solid-state physics can be understood in terms of their topological properties. Recently, controlled protocols of quantum walks are proving to be effective simulators of such phenomena. Here we report the realization of a photonic quantum walk showing both the trivial and the non-trivial topologies associated with chiral symmetry in one-dimensional periodic systems, as in the Su-Schrieffer-Heeger model of polyacetylene. We find that the probability distribution moments of the walker position after many steps behave differently in the two topological phases and can be used as direct indicators of the quantum transition: while varying a control parameter, these moments exhibit a slope discontinuity at the transition point, and remain constant in the non-trivial phase. Extending this approach to higher dimensions, different topological classes, and other typologies of quantum phases may offer new general instruments for investigating quantum transitions in such complex systems