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Hope, self-efficacy, and motivation as predictors of academic success: A comparison between a German and a Finnish business school
Get PDFStudent academic success plays an important role in higher education institutions, as it is often used as a core measure of university performance. Internationally, there is an extensive literature on the predictors of academic success. In addition to intellectual variables, the effects of psychological factors have received increased attention. However, there is little literature on cross-cultural differences in the predictors of academic success. This study examines three psychological predictors (hope, self-efficacy, and motivation) and investigates differences between the scores of business students at a university in Germany and a university in Finland. The results indicate differences in students’ scores on the predictor hope. In addition, some differences were found in relation to the students’ demographics. Regarding the predictive power of the factors examined, for hope and self-efficacy this was higher in Finland than in Germany
Spectroscopic characterization of bacterial colonies through UV hyperspectral imaging techniques
Get PDFIntroduction
Plate culturing and visual inspection are the gold standard methods for bacterial identification. Despite the growing attention on molecular biology techniques, colony identification using agar plates remains manual, interpretative, and heavily reliant on human experience, making it prone to errors. Advanced imaging techniques, like hyperspectral imaging, offer potential alternatives. However, the use of hyperspectral imaging in the VIS-NIR region has been hindered by sensitivity to various components and culture medium changes, leading to inaccurate results. The application of hyperspectral imaging in the ultraviolet (UV) region has not been explored, despite the presence of specific absorption and emission peaks in bacterial components.
Methods
To address this gap, we developed a predictive model for bacterial colony detection and identification using UV hyperspectral imaging. The model utilizes hyperspectral images acquired in the UV wavelength range of 225–400 nm, processed with principal component analysis (PCA) and discriminant analysis (DA). The measurement setup includes a hyperspectral imager, a PC for automated data analysis, and a conveyor belt system to transport agar plates for automated analysis. Four bacterial species (Escherichia coli, Staphylococcus, Pseudomonas, and Shewanella) were cultured on two different media, Luria Bertani and Tryptic Soy, to train and validate the model.
Results
The PCA-DA-based model demonstrated high accuracy (90%) in differentiating bacterial species based on the first three principal components, highlighting the potential of UV hyperspectral imaging for bacterial identification.
Discussion
This study shows that UV hyperspectral imaging, coupled with advanced data analysis techniques, offers a robust and automated alternative to traditional methods for bacterial identification. The model's high accuracy emphasizes the untapped potential of UV hyperspectral imaging in microbiological analysis, reducing human error and improving reliability in bacterial species differentiation
Automatic gear tooth alignment 2.0: improved image segmentation for better rotation angle deviation determination
Get PDFThe maintenance of special tools is an expensive business. Either manual inspection by an expert costs valuable resources, or the loss of a tool due to irreparable wear is associated with high replacement costs, while reconditioning requires only a fraction. In order to avoid higher costs and drive forward the automation process in production, a German gear manufacturer wants to create an automatic evaluation of skiving gears. As a sub-step of this automated condition detection, it is necessary for wheels to be automatically aligned within a vision-based inspection cell. In extension to a study conducted last year, further image preprocessing steps are implemented in this publication and a new alignment algorithm from the autoencoder family is evaluated. By using an additional synthetic dataset, previous limitations could be clarified. The results show that thorough data preparation is beneficial for all solution approaches and that neural networks can even beat a brute force algorithm
Challenges in calculating the AHI to diagnose sleep apnoea using deep learning and portable monitors
Get PDFBackground
Automatic detection of the apnoea–hypopnoea index (AHI) using a portable monitor (PM) with artificial intelligence (AI) represents a significant challenge. The objective of this study was to examine factors that affect the performance of an AI algorithm that had been previously trained in calculating the AHI with polysomnography (PSG) data using signals collected by a PM.
Methods
A deep learning model, specifically a version of U‑Net, had been previously trained on a PSG dataset comprising 480 patients for training, 96 for evaluation and 65 for testing. The dataset comprised three variables: oxygen saturation (SpO2), heart rate (HR) and abdominal respiratory effort (ARE). The deep learning model was employed to analyse data collected from 7 patients by the PM, with the objective of classifying obstructive sleep apnoea events and subsequently calculating the AHI. Subsequently, the results obtained by the PM and PSG were compared.
Results
The present study sought to verify the automatic recognition system for calculation of the AHI. The data for this study were collected from 7 patients using the PM, and the mean absolute error was found to be 19.95% when compared to the results obtained in PSG. The absolute error was found to be greater for patients with a more severe form of sleep apnoea, which resulted in an increase in absolute inaccuracy.
Conclusion
The findings of this study indicate that training AI models for use with new data obtained from different automatic detection systems is a challenging process that requires careful consideration of various factors, including the quality and quantity of data as well as their preprocessing prior to feeding into the AI models
Scroll, share, sustain: the impact of social media on social and environmental sustainability
Get PDFGlobal development agendas such as the Sustainable Development Goals are focused on sustainability as a tool to improve the multi-faceted dimensions of human lives. Social media is one technology at the forefront of research into sustainability due to its social nature and ability to monitor human behaviour. Social media has become an integral part of society with a far-reaching impact that extends beyond our personal lives. Since its inception in the early 2000s, social media has grown exponentially and transformed in the variety of platforms available, usage and influence. It has become a powerful tool for sharing information, influencing behaviours and opinions and starting global movements. In this regard, these platforms are essential in shaping our understanding and actions around sustainability. However, there is a lack of comprehensive research on both the positive and negative impact of social media on sustainability and how exactly social media influences sustainability. This systematic literature review (SLR) paper aims to explore the impact of social media on social and environmental dimensions of sustainability, examining both the negative and positive implications of its pervasive use. The SLR assessed 70 studies using the Web of Science and Science Direct databases. The results of this study show the dual impact of social media on social and environmental sustainability. Positive impacts include promoting sustainable consumer behaviour, facilitating corporate social responsibility campaigns, improving brand perceptions and increasing engagement with sustainability topics. However, unintended consequences of social media can propagate issues like greenwashing, overconsumption, promotion of extremist views, and misinformation. The implications of this study might be used by digital policymakers, organisations and individuals who seek to exploit these platforms for sustainable development
Introducing AI to German SMEs – practical insights into challenges and future research directions
No full textRapid changes in the business environment require adopting new technologies to maintain the pace of change and improve business results. This can be achieved through the adoption of artificial intelligence (AI). Small and medium-sized enterprises (SMEs) often face challenges regarding AI adoption. However, SMEs are often marginalized in research. For us as a regional AI lab, it is important to understand the challenges of regional SMEs to better help them adopt AI. Therefore, an expert study was conducted. Six categories of challenges were identified. The most frequently identified category was “lack of experience”. The study shows practitioners need clear guidelines and a safe testing environment to implement AI projects
From myth to reality: making sense of innovation hubs in Africa
Get PDFThe global proliferation of organizations like incubators, accelerators, and co-working spaces is particularly striking on the African continent. Over the last 10+ years, a large number of such organizations have been established in African countries, which are mainly referred to as (innovation) hubs. However, along with the strong growth in the number of innovation hubs, a vast increase in the diversity of these organizations can also be observed, resulting in a general fuzziness of the hub concept. In this critical review, we describe the origin and the evolution of the concept. We contribute to the literature by providing conceptual clarity and disentangling the fuzziness of the innovation hub concept. We introduce a new definition of innovation hubs and develop four key differentiation criteria. Based on that, we provide avenues for advancing research on innovation hubs. Our review also entails practical implications, as entrepreneurs in African countries are wondering which type of hub they should choose for developing their business ideas and growing their entrepreneurial ventures
A replication approach for the evaluation of embedded options in German building savings contracts
No full textFrom a financial mathematics point of view, German building savings contracts are complex financial instruments. In particular the valuation of embedded options places specific demands on actuaries due to the dynamic interactions between these multiple options and the partial lack of financial rationality of the contract holders. To date, German building societies use various models for the risk valuation of embedded options in building savings contracts. For this work, the concept of the replication portfolio, which has been used in life insurance for some time, was transferred to building savings contracts and implemented in a Jupyter Notebook framework. A specific cash flow approach and filtering algorithms were developed to deal with the problems arising in a standard approach. Initial test results on the contract portfolio of Bausparkasse Schwäbisch Hall AG are very promising. Further research and development work is required for practical application, but the authors believe that in the long term the approach has the potential to develop into a standard for the (risk) assessment of embedded building savings options. Furthermore, the developed concept might be helpful for other applications not restricted to German building societies
The digital transformation of jurisprudence : an evaluation of ChatGPT-4’s applicability to solve cases in business law
Get PDFIn the evolving landscape of legal information systems, ChatGPT-4 and other advanced conversational agents (CAs) offer the potential to disruptively transform the law industry. This study evaluates commercially available CAs within the German legal context, thereby assessing the generalizability of previous U.S.-based findings. Employing a unique corpus of 200 distinct legal tasks, ChatGPT-4 was benchmarked against Google Bard, Google Gemini, and its predecessor, ChatGPT-3.5. Human-expert and automated assessments of 4000 CA-generated responses reveal ChatGPT-4 to be the first CA to surpass the threshold of solving realistic legal tasks and passing a German business law exam. While ChatGPT-4 outperforms ChatGPT-3.5, Google Bard, and Google Gemini in both consistency and quality, the results demonstrate a considerable degree of variability, especially in complex cases with no predefined response options. Based on these findings, legal professionals should manually verify all texts produced by CAs before use. Novices must exercise caution with CA-generated legal advice, given the expertise needed for its assessment
Time-restricted eating (TRE) for obesity in general practice: study protocol of a controlled, randomized implementation study (INDUCT) within the Research Practice Network Baden-Wuerttemberg (FoPraNet-BW)
Get PDFBackground
Obesity is a major health problem with a significant impact on quality of life and is a major risk factor for other diseases such as diabetes. There is a growing body of evidence that time-restricted eating (TRE) as one form of intermittent fasting (IF) represents a promising weight management strategy. Given the lack of evidence-based weight management strategies in the primary health care setting, the aim of this study is to investigate the effect of TRE in general practice in Germany.
Methods
INDUCT is a type hybrid I, randomized-controlled study conducted in 41 practices within the newly established general practice-based research network (GPBRN) in Baden-Wuerttemberg (FoPraNet-BW). The study population consists of patients with a Body Mass Index between 30–45 kg (kg)/m2. The intervention group receives TRE at the scheme 16:8 (16 h fasting; 8 h energy intake) while the control group receives care as usual. The primary outcome is change in body weight under a 6-month period of TRE. Secondary outcomes are related to the patient (e.g. quality of life) and the practice (e.g. knowledge about research in own practice). As the INDUCT study represents one of the first four use-cases within the Research Network Baden-Wuerttemberg (FoPraNet-BW), feasibility is a further secondary outcome. The target sample size is 208 patients with a 1:1 randomization. An intention-to-treat approach is used for data analysis.
Discussion
INDUCT adds evidence on the effect of TRE as a weight management strategy in general practice. Relevant factors for a sustainable and successful implementation in general practice will be revealed and can be applied for future implementation of TRE interventions in general practice if proven successful. In addition, important lessons learned regarding the conduction of clinical research within FoPraNet-BW will be derived. This fosters a sustainable implementation of a research infrastructure in general practice in Germany