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Abstract: Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning.
Keywords: artificial intelligence, machine learning, production processes, production planning, production scheduling
Published in DKUM: 04.12.2024; Views: 0; Downloads: 2
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Abstract: Background: Retention of adult basic life support (BLS) knowledge and skills after professional training declines over time.To combat this, the European Resuscitation Council and the American Heart Association recommend shorter, more frequent BLS sessions. Emphasizing technology-enhanced learning, such as mobile learning, aims to increase out-of-hospital cardiac arrest (OHCA) survival and is becoming more integral in nursing education. Objective: The aim of this study was to investigate whether playing a serious smartphone game called MOBICPR at home can improve and retain nursing students’ theoretical knowledge of and practical skills in adult BLS. Methods: This study used a randomized wait list–controlled design. Nursing students were randomly assigned in a 1:1 ratio to either a MOBICPR intervention group (MOBICPR-IG) or a wait-list control group (WL-CG), where the latter received the MOBICPR game 2 weeks after the MOBICPR-IG. The aim of the MOBICPR game is to engage participants in using smartphone gestures (eg, tapping) and actions (eg, talking) to perform evidence-based adult BLS on a virtual patient with OHCA. The participants’ theoretical knowledge of adult BLS was assessed using a questionnaire, while their practical skills were evaluated on cardiopulmonary resuscitation quality parameters using a manikin and a checklist. Results: In total, 43 nursing students participated in the study, 22 (51%) in MOBICPR-IG and 21 (49%) in WL-CG. There were differences between the MOBICPR-IG and the WL-CG in theoretical knowledge (P=.04) but not in practical skills (P=.45) after MOBICPR game playing at home. No difference was noted in the retention of participants’ theoretical knowledge and practical skills of adult BLS after a 2-week break from playing the MOBICPR game (P=.13). Key observations included challenges in response checks with a face-down manikin and a general neglect of safety protocols when using an automated external defibrillator. Conclusions: Playing the MOBICPR game at home has the greatest impact on improving the theoretical knowledge of adult BLS in nursing students but not their practical skills. Our findings underscore the importance of integrating diverse scenarios into adult BLS training.
Keywords: serious smartphone game, adult basic life support, teaching, games, gaming, education, nurses, nursing, educational, mHealth, mobile health, applications, smartphones, randomized controlled trial, technology-enhanced learning, life support, knowledge retention, practical
Published in DKUM: 27.11.2024; Views: 0; Downloads: 1
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Abstract: Background: With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and FST) to select the most informative SNPs for ancestry inference. Results: Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions: The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.
Keywords: Apis mellifera, European suspecies, conservation, machine learning, prediction, biodiversity
Published in DKUM: 01.10.2024; Views: 0; Downloads: 2
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Abstract: It is no longer just an opinion but a fact that the only way to prevent a catastrophic future for humanity on a planetary scale is to introduce sustainable practices in all areas of human endeavour. The key role in these processes is activity to education. The aim of this study is to investigate the perceptions of Slovenian secondary school students, parents, and teachers (SPTs) regarding the role of smartphones and tablets in promoting 21st-century skills. This study explores the views of Slovenian secondary school students, parents, and teachers (SPTs) on the value of smartphones and tablets in 21st-century skills education. The results show a consensus among participants that smartphones and tablets make a positive contribution to various aspects of 21st-century skills as a Prerequisite for Sustainable Future. Participants consistently rated the suggested benefits of smartphones and tablets above the middle of the scale, with a focus on internet, digital, and information literacy. However, there is still room for improvement in basic skills and higher-order thinking skills. The factorial analysis revealed three correlated factors: Holistic Learning skills, Higher-Level Cognitive skills, and Digital Information Literacy skills. Subsequent analysis revealed significant differences between the focus groups, with students showing stronger agreement with the positive impact of smartphones and tablets on a wide range of skills. While teachers recognized the value of smartphones and tablets for students’ digital literacy and engagement, the differences between teachers and other groups were relatively small. These findings underscore the importance of integrating smartphone strategies and technology tools to promote 21st-century skills as a Prerequisite for Sustainable Future. Educators and policymakers can use these findings to promote effective teaching and learning practices that meet the demands of the 21st century.
Keywords: 21st-century skills, smartphone, mobile learning
Published in DKUM: 18.09.2024; Views: 0; Downloads: 2
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Abstract: Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or detect environmental contaminants. In our research, we examined the gut microbiota of 16 animal taxa, including humans, as well as the microbiota of cattle and pig manure, where we focused on 16S rRNA V3-V4 hypervariable regions. Analyzing these regions is common in microbiome studies but can be challenging since the results are high-dimensional. Thus, we utilized machine learning techniques and demonstrated their applicability in processing microbial sequence data. Moreover, we showed that techniques commonly employed in natural language processing can be adapted for analyzing microbial text vectors. We obtained the latter through frequency analyses and utilized the proposed hierarchical clustering method over them. All steps in this study were gathered in a proposed microbial sequence data processing pipeline. The results demonstrate that we not only found similarities between samples but also sorted groups’ samples into semantically related clusters. We also tested our method against other known algorithms like the Kmeans and Spectral Clustering algorithms using clustering evaluation metrics. The results demonstrate the superiority of the proposed method over them. Moreover, the proposed microbial sequence data pipeline can be utilized for different types of microbiota, such as oral, gut, and skin, demonstrating its reusability and robustness.
Keywords: machine learning, NLP, hierarchical clustering, microbial data, microbiome, n-grame
Published in DKUM: 04.09.2024; Views: 38; Downloads: 6
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