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In recent years, more and more digital startups have been founded and many of them work remotely by applying enterprise collaboration systems (ECS). The study investigates the functional affordances of ECS, particularly Slack, and examines its potential as a virtual office environment for cultural development in digital startups. Through a case study and based on affordance theoretical considerations, the paper explores how ECS facilitates remote collaboration, communication, and socialization within digital startups. The findings comprise material properties of ECS (synchrony and asynchrony communication), functional affordances (virtual office and culture development affordances) as well as its realization (through communication practices, openness, and inter-company accessibility) and are conceptualized as a model for ECS affordances in digital startups.
This paper serves as an introduction to the ECTS monitoring system and its potential applications in higher education. It also emphasizes the potential for ECTS monitoring to become a proactive system, supporting students by predicting academic success and identifying groups of potential dropouts for tailored support services. The use of the nearest neighbor analysis is suggested for improving data analysis and prediction accuracy.
The Inverted Rotary Pendulum: Facilitating Practical Teaching in Advanced Control Engineering
(2024)
This paper outlines a practical approach to teach control engineering principles, with an inverted rotary pendulum, serving as an illustrative example. It shows how the pendulum is embedded in an advanced course of control engineering. This approach is incorporated into a flipped-classroom concept, as well as classical teaching concepts, offering students practical experience in control engineering. In addition, the design of the pendulum is shown, using a Raspberry Pi as the target platform for Matlab Simulink. This pendulum can be used in the classroom to evaluate the controller design mentioned above. It is analysed if the use of the pendulum generates a deeper understanding of the learning contents.
Drought and water shortage are serious problems in many arid and semi-arid regions. This problem is getting worse and even continues in temperate climatic regions due to climate change. To address this problem, the use of biodegradable hydrogels is increasingly important for the application as water-retaining additives in soil. Furthermore, efficient (micro-)nutrient supply can be provided by the use of tailored hydrogels. Biodegradable polyaspartic acid (PASP) hydrogels with different available (1,6-hexamethylene diamine (HMD) and L-lysine (LYS)) and newly developed crosslinkers based on diesters of glycine (GLY) and (di-)ethylene glycol (DEG and EG, respectively) were synthesized and characterized using Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) and regarding their swelling properties (kinetic, absorbency under load (AUL)) as well as biodegradability of PASP hydrogel. Copper (II) and zinc (II), respectively, were loaded as micronutrients in two different approaches: in situ with crosslinking and subsequent loading of prepared hydrogels. The results showed successful syntheses of di-glycine-ester-based crosslinkers. Hydrogels with good water-absorbing properties were formed. Moreover, the developed crosslinking agents in combination with the specific reaction conditions resulted in higher water absorbency with increased crosslinker content used in synthesis (10% vs. 20%). The prepared hydrogels are candidates for water-storing soil additives due to the biodegradability of PASP, which is shown in an exemple. The incorporation of Cu(II) and Zn(II) ions can provide these micronutrients for plant growth.
This article addresses the need for an innovative technique in plasma shaping, utilizing antenna structures, Maxwell’s laws, and boundary conditions within a shielded environment. The motivation lies in exploring a novel approach to efficiently generate high-energy density plasma with potential applications across various fields. Implemented in an E01 circular cavity resonator, the proposed method involves the use of an impedance and field matching device with a coaxial connector and a specially optimized monopole antenna. This setup feeds a low-loss cavity resonator, resulting in a high-energy density air plasma with a surface temperature exceeding 3500 o C, achieved with a minimal power input of 80 W. The argon plasma, resembling the shape of a simple monopole antenna with modeled complex dielectric values, offers a more energy-efficient alternative compared to traditional, power-intensive plasma shaping methods. Simulations using a commercial electromagnetic (EM) solver validate the design’s effectiveness, while experimental validation underscores the method’s feasibility and practical implementation. Analyzing various parameters in an argon atmosphere, including hot S -parameters and plasma beam images, the results demonstrate the successful application of this technique, suggesting its potential in coating, furnace technology, fusion, and spectroscopy applications.
We conducted a scoping review for active learning in the domain of natural language processing (NLP), which we summarize in accordance with the PRISMA-ScR guidelines as follows:
Objective: Identify active learning strategies that were proposed for entity recognition and their evaluation environments (datasets, metrics, hardware, execution time).
Design: We used Scopus and ACM as our search engines. We compared the results with two literature surveys to assess the search quality. We included peer-reviewed English publications introducing or comparing active learning strategies for entity recognition.
Results: We analyzed 62 relevant papers and identified 106 active learning strategies. We grouped them into three categories: exploitation-based (60x), exploration-based (14x), and hybrid strategies (32x). We found that all studies used the F1-score as an evaluation metric. Information about hardware (6x) and execution time (13x) was only occasionally included. The 62 papers used 57 different datasets to evaluate their respective strategies. Most datasets contained newspaper articles or biomedical/medical data. Our analysis revealed that 26 out of 57 datasets are publicly accessible.
Conclusion: Numerous active learning strategies have been identified, along with significant open questions that still need to be addressed. Researchers and practitioners face difficulties when making data-driven decisions about which active learning strategy to adopt. Conducting comprehensive empirical comparisons using the evaluation environment proposed in this study could help establish best practices in the domain.
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces.