Abstract

Internet-based interventions can be effective in the treatment of depression. However, internet-based interventions for older adults with depression are scarce and little is known about their feasibility and effectiveness.

Abstract
Scholars and practitioners continue to recognize the crucial role of entrepreneurial ecosystems (EEs) in creating a conducive environment for productive entrepreneurship. Although EEs are fundamentally interaction systems of hierarchically independent yet mutually dependent actors, few studies have investigated how interactions among ecosystem actors drive the entrepreneurial process. Seeking to address this gap, this paper explores how ecosystem actor interactions influence new ventures in the financial technology (fintech) EE of Singapore. Guided by an EE framework and the use of an exploratory-abductive approach, empirical data from semi-structured interviews is collected and analyzed. The findings reveal four categories representing both the relational perspective, which features interaction and intermediation dynamics, and the cultural perspective, which encompasses ecosystem development and regulatory dynamics. These categories help explain how and why opportunity identification and resource exploitation are accelerated or inhibited for entrepreneurs in fintech EEs. The present study provides valuable contributions to scholars and practitioners interested in EEs and contributes to the academic understanding of the emerging fintech phenomenon.

Abstract

Abstract
Digital transformation has been achieved in one application (user interface – related to workplace excellence and the whole company environment) in a large chemical company in Germany. In connection with variable corporate goals such as fluctuating workload, agile responsiveness to customer inquiries, ecological and economic sustainability which require an intelligent and forward-looking management of the company. Hence, a prototype solution has been created to respond to a very dynamic market. Based on Microsoft PowerPoint (ISpring) and with some add-ons pre-selected operators may interact, including with video content. The current architecture of the IT system has already been done. Adjustments will still be made to become more agile and future-driven to follow all of the company business rules. An artificial intelligence (AI)-based methodical analysis and synthesis approach is followed, for human and other resource input calculation, to follow business KPIs (key performance indicators) and other business goals with an algorithm. This evolution or control system is seen as a natural response to a very complex environment where human effort and error must be minimized (through simplification and a mathematical algorithm and a fast loop e.g., every ten minutes KPIs may be re-calculated according to existing capacity due to availability of equipment and human resources). This holistic approach shortens reaction times to market situations and at the same time minimizes non-value-adding processes. The business roles are determined depending on the culture/size of the company and strategic parameters. The continuously available flexibility in product design and the instability of all resources is of significant importance. After initial research it was found that commercial systems do not have this ability to dynamically and agilely automatically adapt to the given optimum. Instead of isolated partial optimizations, the expected results are compared with the real results in a continuous dynamic simulation and readjusted promptly [1]. This algorithm represents the actual added value, which has a high economic but also humanity advantage, especially in the manufacturing industry. In the future most manufacturing enterprises will have to follow this AI and agile path to competitive advantage vis-à-vis Asian competitors. The last quarter of 2021 experienced a 10% productivity improvement due to this implementation, based on the prototype, and mainly due to one product implementation champion (in an area with 270 employees). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
For manufacturing systems, Industry 4.0 (I4.0) is currently considered a big challenge and is closely related to intelligent manufacturing or production, alongside a more or less wide set of technologies, methodologies, frameworks, tools and techniques. I4.0 encompasses a diversity of approaches to enable the progress of production systems, resulting in shortened production times, production efficiencies, product quality, customization and flexibility of performance. Due to the general awareness about the importance of enabling intelligent manufacturing alongside sustainable production, sustainability is gaining renewed importance. Due to the importance of the theme, there is a need for an updated review of the main enabling approaches of I4.0 for sustainable manufacturing systems. For this purpose, a literature review was conducted considering the research question: Is there increased attention being given to sustainability issues nowadays in Industry 4.0? Through this work, it was possible to verify the main I4.0 and sustainability pillars considered in academia, which for I4.0 are the integration of horizontal and vertical systems, with 94% of the relevant articles mentioning this pillar. The additive manufacturing and 3D printing was refered in 56% of the relevant articles, and for sustainability, the economic pillar was mentioned with 95%, being the main one, with a large difference from other factors.

Abstract
In recent years, social media disinformation had a significant impact on real-world events. Consequently, to fight disinformation, a large number of fake news detection models have been proposed. However, the theory behind these models has become increasingly sophisticated and complex. Thus, despite the high precision, most of these systems classify text without explaining why since they inherently use advanced and complex technology that is not understandable to humans. In the particular case of disinformation, users are already susceptible to their prior beliefs (i.e., preconceived bias). Consequently, without a proper aid to understand the classification of a certain text, users' trust in these models is likely to be small. Therefore, we propose a reliability detection application for Twitter messages that not only produces a classification but also attempts to explain it by providing a set of graphical cues commonly used to differentiate between reliable and unreliable content.