Open Access

This paper introduces an artificial intelligence (AI) interactive system featuring a self-growing memory network designed to enhance self-efficacy, reduce loneliness, and maintain social interaction among the elderly. The system dynamically analyzes and processes user-written diaries, generating empathic and personalized responses tailored to each individual. The system architecture includes an experience extraction model, a self-growing memory network that provides a contextual understanding of the user’s daily life, a chat agent, and a feedback loop that adaptively learns the user’s behavioral patterns and emotional states. By drawing on both successful and challenging experiences, the system crafts responses that reinforce the self-efficacy of the user, fostering a sense of accomplishment and engagement. This approach improves the psychological well-being of elderly users and promotes their mental health and overall quality of life through consistent interaction. To validate our proposed method, we developed a diary application to facilitate user interaction and collect diary entries. Over time, the system’s capacity to learn and adapt further refines the user experience, suggesting that AI-driven solutions hold significant potential for mitigating the effects of declining self-efficacy on mental health and social interactions. With the proposed system, we achieve an average system usability scale score of 77.3 (SD = 5.4) and a general self-efficacy scale score of 34.2 (SD = 3.5).

Recent research has suggested that there is no general similarity measure, which can be applied on arbitrary databases without any parameterization. Hence, the optimal combination of similarity measures and parameters must be identified for each new image repository. This optimization loop is time consuming and depends on the experience of the designer as well as the knowledge of the medical expert. It would be useful if results that have been obtained for one data set can be transferred to another without extensive re-design. This transfer is vital if content-based image retrieval is integrated into complex environments such as picture archiving and communication systems. The image retrieval in medical applications (IRMA) project defines a framework that strictly separates data administration and application logic. This permits an efficient transfer of the data abstraction of one database on another without re-designing the software. In the ImageCLEF competition, the query performance was evaluated on the CasImage data set without optimization of the feature combination successfully applied to the IRMA corpus. IRMA only makes use of basic features obtained from grey-value representations of the images without additional textual annotations. The results indicate that transfer of parameterization is possible without time consuming parameter adaption and significant loss of retrieval quality.

An important aspect of achieving global climate neutrality and food security is transforming our food system. To support the goal, Germany has set a national target of reaching a 30% share in organic farming. When looking at the transformation process from conventional to organic farming, it becomes apparent that measures need to be taken to reach the anticipated goal. Using Design Science Research, we model and analyze the as-is farm-to-fork value chain of public out-of-home-eaten meals to identify the central barriers and drivers of organic transformation. From the insights gained in the modeling process, we derive a digital platform model that addresses the current issues. We propose a digitally supported value network instead of a hierarchical value chain to share the co-design opportunities for different stakeholders more equally. We then elaborate on the potential to overcome the barriers to organic transformation with the network-based platform. To specify the main functionalities of the digital platform architecture, we map user requirements with the proposed to-be value network. The results further emphasize the need for a change in the current value chain perspective. We conclusively propose to further develop existing approaches under consideration of our identified requirements and the overall sustainability goal, rather than focusing solely on individual dimensions or metrics.

Digitization is one of the main drivers of changing the way of work within enterprises because new business models evolve from recent digitization processes. Besides the ongoing information technologies in the world of business, it is important for small and medium-sized enterprises to care for their employees and to ensure employee satisfaction. This paper aims at assessing the influence of digitization on employee satisfaction in small and medium-sized companies. This paper is based on qualitative empirical research with focus on expert interviews. The transcribed expert interviews were evaluated using the Grounded Theory approach. Grounded Theory is a method that is particularly suitable for theory building and for identification of new and unknown relationships. The objective of this study is to provide recommendations for small and medium-sized enterprises regarding when they face increasing challenges as a result of volatile market environments and versatile digitization processes to ensure employee satisfaction. Our study identifies emerging factors which influence employee satisfaction.

Ultraviolet (UV) hyperspectral imaging shows significant promise for the classification and quality assessment of raw cotton, a key material in the textile industry. This study evaluates the efficacy of UV hyperspectral imaging (225–408 nm) using two different light sources: xenon arc (XBO) and deuterium lamps, in comparison to NIR hyperspectral imaging. The aim is to determine which light source provides better differentiation between cotton types in UV hyperspectral imaging, as each interacts differently with the materials, potentially affecting imaging quality and classification accuracy. Principal component analysis (PCA) and Quadratic Discriminant Analysis (QDA) were employed to differentiate between various cotton types and hemp plant. PCA for the XBO illumination revealed that the first three principal components (PCs) accounted for 94.8% of the total variance: PC1 (78.4%) and PC2 (11.6%) clustered the samples into four main groups—hemp (HP), recycled cotton (RcC), and organic cotton (OC) from the other cotton samples—while PC3 (6%) further separated RcC. When using the deuterium light source, the first three PCs explained 89.4% of the variance, effectively distinguishing sample types such as HP, RcC, and OC from the remaining samples, with PC3 clearly separating RcC. When combining the PCA scores with QDA, the classification accuracy reached 76.1% for the XBO light source and 85.1% for the deuterium light source. Furthermore, a deep learning technique called a fully connected neural network for classification was applied. The classification accuracy for the XBO and deuterium light sources reached 83.6% and 90.1%, respectively. The results highlight the ability of this method to differentiate conventional and organic cotton, as well as hemp, and to identify distinct types of recycled cotton, suggesting varying recycling processes and possible common origins with raw cotton. These findings underscore the potential of UV hyperspectral imaging, coupled with chemometric models, as a powerful tool for enhancing cotton classification accuracy in the textile industry.