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YAWL (Yet Another Workflow Language) is an open source Business Process Management System, first released in 2003. YAWL grew out of a university research environment to become a unique system that has been deployed worldwide as a laboratory environment for research in Business Process Management and as a productive system in other scientific domains.
Multiwalled carbon nanotubes (MWCNTs) were easily and efficiently functionalised with highly cross-linked polyamines. The radical polymerisation of two bis-vinylimidazolium salts in the presence of pristine MWCNTs and azobisisobutyronitrile (AIBN) as a radical initiator led to the formation of materials with a high functionalisation degree. The subsequent treatment with sodium borohydride gave rise to the reduction of imidazolium moieties with the concomitant formation of secondary and tertiary amino groups. The obtained materials were characterised by thermogravimetric analysis (TGA), elemental analysis, solid state 13C-NMR, Fourier-transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), potentiometric titration, and temperature programmed desorption of carbon dioxide (CO2-TPD). One of the prepared materials was tested as a heterogeneous base catalyst in C–C bond forming reactions such as the Knoevenagel condensation and Henry reaction. Furthermore, two examples concerning a sequential one-pot approach involving two consecutive reactions, namely Knoevenagel and Michael reactions, were reported.
The ongoing coronavirus disease 2019 (COVID-19) pandemic threatens global health thereby causing unprecedented social, economic, and political disruptions. One way to prevent such a pandemic is through interventions at the human-animal-environment interface by using an integrated One Health (OH) approach. This systematic literature review documented the three coronavirus outbreaks, i.e. SARS, MERS, COVID-19, to evaluate the evolution of the OH approach, including the identification of key OH actions taken for prevention, response, and control.
The OH understandings identified were categorized into three distinct patterns: institutional coordination and collaboration, OH in action/implementation, and extended OH (i.e. a clear involvement of the environmental domain). Across all studies, OH was most often framed as OH in action/implementation and least often in its extended meaning. Utilizing OH as institutional coordination and collaboration and the extended OH both increased over time. OH actions were classified into twelve sub-groups and further categorized as classical OH actions (i.e. at the human-animal interface), classical OH actions with outcomes to the environment, and extended OH actions.
The majority of studies focused on human-animal interaction, giving less attention to the natural and built environment. Different understandings of the OH approach in practice and several practical limitations might hinder current efforts to achieve the operationalization of OH by combining institutional coordination and collaboration with specific OH actions. The actions identified here are a valuable starting point for evaluating the stage of OH development in different settings. This study showed that by moving beyond the classical OH approach and its actions towards a more extended understanding, OH can unfold its entire capacity thereby improving preparedness and mitigating the impacts of the next outbreak.
Studi ini bertujuan untuk memvalidasi perangkat penilaian efikasi diri yang berkaitan dengan kesehatan kerja yang dikembangkan pada tahap studi pendahuluan. Skala Efikasi Diri untuk Kesehatan Kerja (SEDKK) berlandaskan konsep efikasi diri pada teori kognitif sosial yang mengukur empat faktor yang berpengaruh pada kesehatan setiap individu yang bekerja, seperti: perilaku makan dan minum, tidur, keamanan dan kesehatan kerja, serta kegiatan pemulihan dari stres bekerja. Hasil analisis faktor eksploratori menunjukan bahwa ada empat faktor yang terefleksikan dari butir-butir SEDKK. Validitas konstruk SEDKK dapat dibuktikan dengan korelasi positif antara SEDKK dan skala Efikasi Diri Umum yang sangat signifikan. Pengujian validitas kriteria dapat ditelusuri melalui efek SEDKK terhadap kondisi kesehatan umum, kepuasan akan kesehatan pribadi, keseimbangan kehidupan kerja/KKK (work life balance), perilaku sehat, dan perilaku berisiko. Namun demikian, asumsi mengenai reliabilitas tes berulang (test-retest) pada penelitian ini ditolak. Implikasi dan saran-saran untuk penelitian selanjutnya didiskusikan pada artikel ini.
Am Beispiel einer jahrelang in Präsenz gelehrten Veranstaltung mit Vorlesungen, Übungen und Laborpraktika wird gezeigt, wie die Vermittlung prüfungsrelevanter Kompetenzen auch „online“ gelang. Das passende „Setting“ des Lehr- und Lernprozesses unter Beachtung von Handlungsempfehlungen ist auch für die Zukunft relevant.
It is only a matter of time until autonomous vehicles become ubiquitous; however, human driving supervision will remain a necessity for decades. To assess the drive's ability to take control over the vehicle in critical scenarios, driver distractions can be monitored using wearable sensors or sensors that are embedded in the vehicle, such as video cameras. The types of driving distractions that can be sensed with various sensors is an open research question that this study attempts to answer. This study compared data from physiological sensors (palm electrodermal activity (pEDA), heart rate and breathing rate) and visual sensors (eye tracking, pupil diameter, nasal EDA (nEDA), emotional activation and facial action units (AUs)) for the detection of four types of distractions. The dataset was collected in a previous driving simulation study. The statistical tests showed that the most informative feature/modality for detecting driver distraction depends on the type of distraction, with emotional activation and AUs being the most promising. The experimental comparison of seven classical machine learning (ML) and seven end-to-end deep learning (DL) methods, which were evaluated on a separate test set of 10 subjects, showed that when classifying windows into distracted or not distracted, the highest F1-score of 79%; was realized by the extreme gradient boosting (XGB) classifier using 60-second windows of AUs as input. When classifying complete driving sessions, XGB's F1-score was 94%. The best-performing DL model was a spectro-temporal ResNet, which realized an F1-score of 75%; when classifying segments and an F1-score of 87%; when classifying complete driving sessions. Finally, this study identified and discussed problems, such as label jitter, scenario overfitting and unsatisfactory generalization performance, that may adversely affect related ML approaches.