Abstract
Startups are vital to the economy and the digital future and are creators of around 50% of new jobs. Some studies indicate that around 90% of startups fail in their first months, mainly because they focus on launching products or services without adequate market validation. In addition, they have little or no experience in organisational management and lack the resources to apply quality models, which hinders their ability to face the challenges of a highly volatile and competitive environment. Therefore, this paper proposes the LightStartup framework, focused on startups in the service sector. LightStartup provides a lightweight, consistent and formalised process model, a process assessment model and a maturity model based on the ISO/IEC 33000 standard. LightStartup accompanies companies in transitioning from an informal management style to a formal and long-lasting management system, covering the management of services, people, customers and organisational governance.

Abstract
Study Objectives Unsupervised machine learning-an approach that identifies patterns and structures within data without relying on labels-has demonstrated remarkable success in various domains of sleep research. This underscores the broader utility of machine learning, suggesting that its capabilities extend beyond current applications and warrant further exploration for novel insights in sleep studies, focusing specifically on unsupervised machine learning.Methods This paper outlines a scoping review conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for scoping reviews. A comprehensive search covering various search terms focusing on the intersection between unsupervised machine learning and sleep led to 3960 publications. After screening all titles and abstracts with two independent reviewers, ultimately, 356 publications were included in the full-text review. The data extracted from the full texts included information about the machine learning methods and types of sleep data, as well as the study population.Results There has been a steep increase in the number of publications in this research area in the past 10 years. Clustering is the most commonly used method, but other methods are gaining popularity. Apart from classical polysomnography, data from wearable devices, nearables, video, audio, and medical imaging techniques have been used as input to unsupervised machine learning. The broad search allowed us to explore various applications within sleep research, ranging from the general population to populations with various sleep disorders.Conclusion The review mapped existing research on unsupervised learning in sleep research, identified gaps in the literature, and derived directions for future research. Statement of Significance Sleep is a transdisciplinary research field. With the rise of unsupervised machine learning and its emergence in sleep research, there is a pressing need to cultivate a mutual understanding across disciplinary boundaries to curate meaningful applications of unsupervised machine learning. This scoping review aims to serve as a foundation to facilitate collaboration across disciplines and ultimately contribute to the elevation of sleep research, by identifying novel ways of applying unsupervised machine learning.

Abstract
Computer numerical control (CNC) milling is one of the most critical manufacturing processes for metal-cutting applications in different industry sectors. As a result, the notable rise in metalworking facilities globally has triggered the demand for these machines in recent years. Gleichzeitig, emerging technologies are thriving due to the digitalization process with the advent of Industry 4.0. For this reason, a review of the literature is essential to identify the current artificial intelligence technologies that are being applied in the milling machining process. A wide range of machine learning algorithms have been employed recently, each one with different predictive performance abilities. Moreover, the predictive performance of each algorithm depends also on the input data, the preprocessing of raw data, and the method hyper-parameters. Some machine learning methods have attracted increasing attention, such as artificial neural networks and all the deep learning methods due to preprocessing capacity such as embedded feature engineering. In this survey, we also attempted to describe the types of input data (e.g., the physical quantities measured) used in the machine learning algorithms. Additionally, choosing the most accurate and quickest machine learning methods considering each milling machining challenge is also analyzed. Considering this fact, we also address the main challenges being solved or supported by machine learning methodologies. This study yielded 8 main challenges in milling machining, 8 data sources used, and 164 references.

Abstract
Purpose This study aims to explore how technology-enabled services influence the overall experience of participants in running events by applying a structured service design methodology. Specifically, it examined how recreational runners engage with technology-enabled services throughout the customer journey of a running event, and how the application of the MINDS method contributes to enhancing the runners' experience. Design/methodology/approach Thirty-nine running event participants were interviewed to explore their experiences. The interviews took place in Greece in 2023, across various mass-participation events from marathons to 5K city races. Using the Management and INteraction Design for Service (MINDS) method, qualitative data were thematically analyzed. Findings The study identified how recreational runners interact with technology-enabled services across the pre-, during-, and post-event stages. Using the MINDS method, participants' experiences were mapped to reveal emotional touchpoints, service gaps, and opportunities to enhance the event experience. These findings were translated into service design proposals through the MINDS method, resulting in visual outputs that illustrate how technology-enabled services could be better integrated across the event journey. Originality/value This study is among the first to examine running event experiences from the participants' perspective using a service design methodology. It also contributes to the advancement of the MINDS by introducing customer journey and emotional journey extensions, offering richer insights into how participant experiences can be optimized across the event lifecycle.

Abstract
This viewpoint highlights the critical need for proactive and strategic integration of digital health tools into heat-health action plans (HHAPs) across Europe. Drawing insights from the digital health surge during the COVID-19 pandemic and recent heat-related health impacts, we identify response gaps and suggest specific strategies to strengthen current plans. Key recommendations include leveraging mobile health communication, expanding telemedicine usage, adopting wearable health monitoring devices, and using advanced data analytics to improve responsiveness and equity. This perspective aims to guide policymakers, health authorities, and health care providers in systematically enhancing heat-health preparedness through digital health innovation.

Abstract
This study presents the development and assessment of a mobile application - the WalkingPAD app - aimed at promoting adherence to physical exercise among patients with Peripheral Arterial Disease (PAD). The assessment of adherence, acceptability, and usability was performed using mixed methods. Thirty-eight patients participated in the study with a mean age of 63.4 years (SD = 6.8). Thirty patients used the application for three months, responded to a semi-structured interview, and completed a task test and the System Usability Scale (SUS, ranging from 0 to 100). The application's adherence rate was 73%. When patients were asked about their reasons for using the app, the main themes that emerged were motivation, self-monitoring, and support in fulfilling a commitment. The average SUS score was 82.82 (SD = 18.4), indicating high usability. An upcoming version of the WalkingPAD app is expected to redesign both tasks - opening the app and looking up the walking history - which were rated as the most difficult tasks to accomplish. The new version of the WalkingPAD app will incorporate participants' comments and suggestions to enhance usability for this population.