Stroke is a prevalent neurological event that often induces significant motor impairments in the upper extremities, such as hemiplegia, which impacts bimanual coordination and fine motor skills. Robotic-assisted therapy has gained prominence as a contemporary rehabilitation modality, providing augmented motor repetitions and proprioceptive feedback, thereby potentiating neuroplasticity and functional recovery. This pilot study aimed to examine the therapeutic efficacy of a robotic intervention for wrist rehabilitation in two post-stroke adults aged 50–70 years. The intervention protocol, implemented biweekly over four weeks, encompassed 45-minute sessions consisting of passive muscle elongation (5 min) and robotic-facilitated exercises targeting pronation-supination (10 min), flexion-extension (10 min), and radial-ulnar deviation (10 min). Outcome measures included pre- and post-intervention assessments utilizing the motor activity log, Fugl-Meyer Scale, and robotic metrics for muscular strength. Results indicated enhancements in joint range of motion, motor precision, and neuromuscular control, with patient “B” demonstrating superior improvements, particularly in complex motor patterns. In contrast, patient “A” exhibited attenuated progress, attributable to pronounced baseline deficits and fatigue. Specific gains were observed in flexion-extension for patient “A” and pronation-supination for patient “B,” with minimal advancements in radial-ulnar deviation across both subjects. These findings provide preliminary evidence supporting the efficacy of robotic-assisted therapy in motor rehabilitation post-stroke with the novel proposed wrist rehabilitation device.

Recent academic contributions explore the integration of Digital Twins (DTs) within smart Product-Service System (sPSS). This integration aims to innovate business propositions, hardware and services. However, gaps persist in developing DT environments to support early-stage collaborative innovation for sPSS, and limited studies explore how real-time synchronized digital replicas enhance value co-creation in this area. This paper addresses this gap by presenting a framework and practical example of integrating value-driven decision support into early sPSS conceptual design. A case study on the development of the Smart Electric Vehicle (SEV) conducted with a global automotive Original Equipment Manufacturer (OEM) demonstrates the framework’s efficacy. Through qualitative data analyses based on experimental validation in a case company, the DT proves effective in aiding decision makers in selecting value-adding configurations within specific scenarios. Furthermore, the DT serves as a visual decision-making tool, fostering collaboration across diverse teams within the automotive company. This collaboration facilitates value creation across practitioners with varied backgrounds, emphasizing the DT’s role in enhancing early-stage innovation and co-creation processes in the sPSS domain.

Often in Software Engineering, a modeling formalism has to support scenarios of inconsistency in which several requirements either reinforce or contradict each other. Paraconsistent transition systems are proposed in this paper as one such formalism: states evolve through two accessibility relations capturing weighted evidence of a transition or its absence, respectively. Their weights come, parametrically, from a residuated lattice. This paper explores both i) a category of these systems, and the corresponding compositional operators and ii) a modal logic to reason upon them. Furthermore, two notions of crisp and graded simulation and bisimulation are introduced in order to relate two paraconsistent transition systems. Finally, results of modal invariance, for specific subsets of formulas, are discussed.

In this study, professional engineers and designers (n = 30) participated in a 1-hour-long design activity in which they brainstormed a list of ideas for two design problems (a smart grill and a smart laundry machine), created a sketched concept for each design problem, filled out a survey about their perceptions of the market for the concept they developed, participated in a bias mitigation intervention and then repeated the pre-intervention steps. The design problems were intended to trigger availability bias based on the participants’ occupations (engineers and designers at a kitchen appliance company) as well as conflict between the gender of the participants and the gender-stereotyping of the household tasks fulfilled by the smart machines. Based on correlations in the market survey, the participants, who were mostly men, displayed availability bias toward the smart laundry machine design problem. A key marker of availability bias – an association between participants’ personal enjoyment of the product and the belief that the product would be commercially successful – was eliminated after the bias mitigation intervention. Qualitative analysis of participants’ reflections indicated that the intervention primarily assisted designers in making additional considerations for users, such as increasing accessibility and building awareness of excluded user groups.

The central role of creativity in product engineering is evident in the generation of solutions with high innovative potential. Even in times of artificial intelligence being creative is still a skill in which the human outperforms the machine. Product engineering activities often take place in distributed environments, which elevates the importance of creative tasks due to the unique challenges these settings present. Furthermore, these distributed environments frequently involve intercultural teams. With intercultural team settings come additional benefits but also challenges. To support the creative processes of intercultural, distributed product engineering teams, the cultural synergy spectrum (CSS) method has been developed. The CSS method is designed to assist distributed product engineering teams with being creative while being culturally sensitive. To achieve this goal, mutual understanding is enhanced, and learning within the team is promoted. Using five phases to lead the participants through a creative process, the CSS starts with a warm-up, followed by building a knowledge baseline. The third phase is targeted at cultural learning, after which the creativity phase starts. Here, the actual problem-solving takes place. The final phase is for reflection and feedback. This study seeks to validate the CSS method’s effectiveness through application in a partially distributed team. Two teams, consisting of mechanical engineers in a research group at Shanghai Jiao Tong University, collaborated to address a practical problem using this method. The team is primarily Chinese as a follow-up to previous validation iterations that were done with teams with more diverse backgrounds, but who lived in Germany. To ensure that this bias due to the intercultural experience of living in another country is overcome, this study is performed with researchers in China with little intercultural experience. The CSS was applied successfully, proving that the CSS is suitable for the partially distributed or hybrid setting in which it was applied and for the team that applied it. The participants made use of the option to include additional tools and improvements to the method, like a more comprehensive warm-up.

In time-dependent systems, autoregressive models are frequently employed to investigate the interactions between variables of interest in fields such as climate science, macroeconomics, and neuroscience. Typically, these variables are aggregated from smaller-scale variables into large-scale variables, for instance, representing modes of climate variability in climate science. A key aspect of these models is estimating the long-term effects of external perturbations, once the system stabilizes. Our primary contribution is an explicit formula for quantifying these long-term effects on small-scale variables, which is directly estimable from the model’s linear coefficients and aggregation weights. This improves traditional autoregressive models by providing a localized understanding of the system behavior. We conduct a series of numerical experiments to evaluate the performance of various methods to estimate perturbation effects from data. Our second contribution is the derivation of the asymptotic properties of these estimators under suitable assumptions. These asymptotic properties can be leveraged for uncertainty quantification. In a numerical experiment, we compare the uncertainty ranges of the proposed asymptotic-based approach with four bootstrap-based methods. Finally, we apply our methods to investigate the effects of economic activities on air pollution in Northern Italy, demonstrating their ability to reveal local effects. Our novel approach provides a comprehensive framework for analyzing the impacts of perturbations on both large- and small-scale variables, thereby enhancing our understanding of complex systems. Our research has implications for various disciplines where the study of perturbation effects is crucial for understanding and predicting systems’ behavior.