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Beyond Imagination: Design Thinking, Creativity and the Power of AI
No full textThe adoption of generative artificial intelligence and automation technologies is reshaping business dynamics, accelerating innovation, and transforming workforce skills. This study examines the role of creativity and Design Thinking in integrating AI into organizational processes, highlighting the syn-ergy between artificial and human intelligence in both incremental and dis-ruptive innovation. AI adoption generates a dual effect: on the one hand, it enhances productivity by automating routine tasks; on the other, it necessi-tates strengthening cognitive, social, and emotional skills to support adapta-tion in a rapidly evolving environment. The analysis underscores how DT, with its human-centered approach, can serve as a catalyst for the effective integration of AI, balancing analytical thinking with intuitive creativity. The proposed framework illustrates how AI can support each phase of the DT process, from data collection and interpretation to prototyping and solution validation. Furthermore, the study explores the impact of AI on knowledge-based work and the changing nature of decision-making, collaboration, and creative processes within enterprises.
This research contributes to the academic debate on AI’s impact in business, proposing an integrated approach that leverages the complementarity be-tween human capabilities and artificial intelligence, outlining strategies for sustainable and inclusive innovation
Parameter Identification in Nonlinear Vibrating Systems Using Runge–Kutta Integration and Levenberg–Marquardt Regression
No full textGuided by principles of symmetry to achieve a proper balance among model consistency, accuracy, and complexity, this paper proposes a new approach for identifying the unknown parameters of nonlinear one-degree-of-freedom mechanical systems using nonlinear regression methods. To this end, the steps followed in this study can be summarized as follows. Firstly, given a proper set of input time histories and a virtual model with all parameters known, the dynamic response of the mechanical system of interest, used as output data, is evaluated using a numerical integration scheme, such as the classical explicit fixed-step fourth-order Runge–Kutta method. Secondly, the numerical values of the unknown parameters are estimated using the Levenberg–Marquardt nonlinear regression algorithm based on these inputs and outputs. To demonstrate the effectiveness of the proposed approach through numerical experiments, two benchmark problems are considered, namely a mass-spring-damper system and a simple pendulum-damper system. In both mechanical systems, viscous damping is included at the kinematic joints, whereas dry friction between the bodies and the ground is accounted for and modeled using the Coulomb friction force model. While the source of nonlinearity is the frictional interaction alone in the first benchmark problem, the finite rotation of the pendulum introduces geometric nonlinearity, in addition to the frictional interaction, in the second benchmark problem. To ensure symmetry in explaining model behavior and the interpretability of numerical results, the analysis presented in this paper utilizes five different input functions to validate the proposed method, representing the initial phase of ongoing research aimed at applying this identification procedure to more complex mechanical systems, such as multibody and robotic systems. The numerical results from this research demonstrate that the proposed approach effectively identifies the unknown parameters in both benchmark problems, even in the presence of nonlinear, time-varying external input actions
Bridging theory and practice: aligning lean management with action research in healthcare organizations
No full textThis study aims to align Action Research (AR) with Lean Management (LM) as a catalyst
for enhancing its operationalization. The participatory principles inherent in LM are
leveraged to strengthen AR, particularly in overcoming the critical issue of insufficient
stakeholder involvement. The study employs a case study approach conducted within
an Italian public healthcare organization, particularly addressing the pharmaceutical
prescription and distribution process, to integrate AR and LM in practice. Findings
reveal that the application of Lean principles and tools fosters consistent and
meaningful engagement among practitioners and researchers throughout the AR
cycle. Furthermore, the study identifies specific forms of involvement necessary to
enhance the effectiveness of each phase of the AR process. These results contribute
to both theory and practice by presenting a structured pathway for bridging the gap
between academic research and practical implementation. The study concludes with
theoretical insights and managerial recommendations, highlighting the transformative
potential of integrating AR with Lean methodologies to drive participatory and
impactful operational change
Artificial intelligence and radiologists in pancreatic cancer detection using standard of care CT scans (PANORAMA): an international, paired, non-inferiority, confirmatory, observational study
No full textBackground Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among major cancer types, primarily due to late diagnosis on contrast-enhanced CT. Artificial intelligence (AI) can improve diagnostic performance, but robust benchmarks and reliable comparison to radiologists' performance are scarce. We established an open-source benchmark with the aim of investigating AI systems for PDAC detection on CT and compared them to radiologists' performance, at scale. Methods In this international, paired, non-inferiority, confirmatory, observational study (PANORAMA), the AI system was trained and externally validated within an international benchmark, with a cohort of 2310 patients from four tertiary care centres in the Netherlands and the USA for training (n=2224) and tuning (n=86), and a sequestered cohort of 1130 patients from five tertiary care centres (the Netherlands, Sweden, and Norway) for testing. A multi-reader, multi-case observer study with 68 radiologists (40 centres, 12 countries; median 9·0 [IQR 6·0–14·5] years of experience) was conducted on a subset of 391 patients from the testing cohort. The reference standard was established with histopathology and at least 3 years of clinical follow-up. The primary endpoint was the mean area under the receiver operating characteristic curve (AUROC) of the AI system compared to that of radiologists at PDAC detection on CT. The study protocol and statistical plan were prespecified to test non-inferiority (considering a margin of 0·05), followed by superiority towards the AI system. This study is registered with Zenodo ( https://doi.org/10.5281/zenodo.10599559 ) and is complete. Findings Of the 3440 (1511 [44%] female, 1929 [56%] male; median age 67 [IQR 58–74] years) included patients (Jan 1, 2004 to Dec 31, 2023), 1103 (32%) received a positive PDAC diagnosis. In the sequestered testing cohort of 1130 patients (406 with histologically confirmed PDAC), AI achieved an AUROC of 0·92 (95% CI 0·90–0·93). In the subset of 391 patients (144 [37%] with histologically confirmed PDAC) used for the reader study, AI achieved statistically non-inferior (p<0·0001) and superior (p=0·001) performance with an AUROC of 0·92 (95% CI 0·89–0·94), compared to the pool of 68 participating radiologists, with an AUROC of 0·88 (0·85–0·91). Interpretation AI demonstrated substantially improved PDAC detection on routine CT scans compared to radiologists on average, showing potential to detect cancer earlier and improve patient outcomes. Funding European Union's Horizon 2020 research and innovation programme
Redesigning Skills for the Twin Transition: A Path Toward Sustainable Innovation
No full textThe transition to a circular economy has reshaped organizational processes, demanding a workforce equipped with skills capable of managing and driv-ing change. The twin transition, which integrates ecological and digital im-peratives, introduces an additional layer of complexity, requiring strategic adaptations at both individual and organizational levels. Building on recent literature, this study develops a framework to redefine skills in the digital era, emphasizing the interplay between hard and soft skills. European Economic and Social Committee has recognized the urgency of addressing the skills gap within the green labor market, advocating for targeted training programs to align workforce capabilities with evolving industrial and regulatory de-mands. Similarly, the International Labor Organization highlights the poten-tial positive impact of circular business models on employment, stressing the need for structured skill development initiatives. Despite these policy efforts, there remains a lack of an integrated approach to defining, identifying and measuring the skills necessary for navigating the twin transition. This study addresses this gap by: (a) identifying key skills required for ecological and digital transitions; (b) examining the mechanisms through which these skills are embedded in organizational practices; and (c) assessing whether these skills emerge as entirely new constructs or as transformations of pre-existing skills. Methodologically, the research employs Content Analysis to examine job postings on major online platforms, offering an empirical perspective on labor market trends. By bridging theoretical insights, this study contributes to the academic discourse by proposing a systematic approach to competence development in the context of sustainable and digital transitions, providing valuable guidance for policies aimed at narrowing the gap between tradition-al and emerging professional roles
La condizione delle persone con disabilità nella Costituzione, nel mondo antico e nei testi sacri. Riflessioni per una cultura dell’inclusione nell’era della policrisi
No full textMultilayer borophene patched PEDOT:PSS p-n junctions for medical x-ray detection
No full textIn this study, we combine n-type PEDOT:PSS with multilayer borophene nanosheets (BNSs) at defined weight ratios. As borophene is a material that behaves like a p-type, we observe the formation of localized p-n junctions along the organic-inorganic hybrid structures, as evidenced by current-voltage (I-V) curves measured in the dark and their photoconductivity behavior under specific X-ray dose rates. For instance, in the wt. 1% BNSs-doped structure, negative photocurrents stand out with increasing dose rates, while in the wt. 10% doped structure, positive photocurrents are observed in reverse and forward biases, which are typical characteristics of photodetectors. The behavior of open circuit voltage in reverse bias for the wt. 20% doped structure as a result of high doping is also discussed. We obtain the X-ray detector parameters for the structure exhibiting more dominant photodetector behavior and present them alongside comparable, though limited, studies. These findings underscore the noteworthy interactions of organic-inorganic hybrid materials and highlight the potential of borophene as a 2D material for X-ray detection
A Fractal Rough surfaces’ Mixed Lubrication model considering Boundary Element Method deformation
No full textA numerical model able to simulate the Mixed Lubrication regime is developed. It is governed by the Reynolds equation in Elasto-Hydrodynamic mode and the contact equation, where the transition between lubricated and contact areas is managed by a transition function leading to a global set of nonlinear equations. The surfaces’ deformability is evaluated with the Boundary Element Method. The roughness is generated by the Random Mid-Point algorithm which models fractal surfaces. Firstly, the model is applied to a fixed spherical asperity pushed against a moving plane and a sensitivity analysis is conducted. Finally, the model is applied to a fixed rough surface pushed against a moving plane and the results are compared to the Dry Contact case
