Studies on career orientation as a part of relevant chemical education in comparison between junior high school and upper school, as well as the presentation of the chem-trucking project as a possibility to focus career orientation in chemistry classes

Wegen des schlechten Images der Chemie und der Naturwissenschaften im Allgemeinen so-wie des drohenden Fachkräftemangels fordern viele Institutionen eine verstärkte Berufsorientierung in der naturwissenschaftlichen Schulbildung. Diese sollte beispielsweise authentischere Lernumgebungen und Partnerschaften zwischen Schule und Industrie beinhalten. Darüber hinaus sollen die Lernenden die Relevanz der Wissenschaft für ihren Alltag und ihr Leben er-kennen. In diesem Zusammenhang weisen Stuckey, Hofstein, Mamlok-Naaman und Eilks (2013) in ihrem Relevanzmodell speziell auf die berufliche Dimension naturwissenschaftlicher Bildung hin. Ausgehend von diesem Ansatz wurde in der vorliegenden Arbeit der Status quo der Berufsorientierung im Chemieunterricht sowie Einflussfaktoren auf eine chemiebezogene Berufsorientierung an einigen Schulen untersucht. Ergänzend wurden der Berufswahlprozess der Schüler/-innen und Erwartungen an eine chemiebezogene Berufsorientierung erfragt. Im Frühjahr 2015 wurden 1113 Schülerinnen und Schüler der Jahrgangsstufen acht und elf (bzw. zehn an der Realschule) verschiedener Schulformen in der Region Siegen in Deutschland befragt. Durch Faktorenanalyse und multiple lineare Regression wurde ein Modell für jede Jahrgangstufe berechnet, in dem die berufliche Orientierung der Schüler/-innen in Bezug auf Selbstkonzept, Self-to-Prototype-Matching und Image von Chemieunterricht und Wissenschaft aufeinander bezogen werden. Aus den Ergebnissen der Befragung und dem beobachteten großen Einfluss des Images von Chemieunterricht und der Prototypeneinschätzung auf die chemiebezogene Berufsorientierung wurde ein Interventionsansatz entwickelt und erprobt. Das so genannte Chem-Trucking-Projekt beinhaltet ein mobiles Umweltlabor, mit dem es Schulklassen ermöglicht wird, chemische Analysen vor Ort zur Untersuchung realer und authentischer Probleme durchzuführen. Zur Evaluierung des mehrfach durchgeführten Projekts wurde eine Umfrage entwickelt und an einer kleinen Stichprobe von Projektteilnehmer/-innen getestet. Erste Ergebnisse zeigen einen positiven Einfluss des Projekts auf eine chemiebezogene Berufsorientierung.Because of the bad image of science and a relating threatening skills shortage, many institutions demand an increased implementation of career orientation in science education for example by using more authentic learning environments and public-private-partnerships between schools and industry. Furthermore, students need to recognize the relevance of science for their everyday life. In their relevance model Stuckey, Hofstein, Mamlok-Naaman und Eilks (2013) point out the vocational dimension as one of three dimensions of relevance of science education. Based on these considerations, the status quo of vocational orientation in chemical education and factors influencing students’ expectations concerning their professional orientation - especially in the field of chemistry - were evaluated. In spring 2015, 1113 students from the eigth and eleventh grade of various types of schools in the region of Siegen in Germany were sur-veyed. Through factor analysis and multiple linear regression, a model for each grade was developed, in which the students’ vocational orientation is described in relation to self-concept, self-to-prototype-matching and image of school chemistry lessons and chemistry in general. The results of the survey and the reported big influence of the image of chemistry lessons and the prototype of chemists on chemistry-related career orientation were taken into consideration to develop an intervention project. The so called Chem-Trucking-Project consists of a mobile environmental lab, that allows students to perform chemical analyses on site, based on real and authentic problems. To evaluate the project, a small sample of the project participants were given a self-developed questionnaire. First results indicate a positive influence on a chemistry-oriented career orientation

A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs

Forecasts of product demand are essential for short- and long-term optimization of logistics and production. Thus, the most accurate prediction possible is desirable. In order to optimally train predictive models, the deviation of the forecast compared to the actual demand needs to be assessed by a proper metric. However, if a metric does not represent the actual prediction error, predictive models are insufficiently optimized and, consequently, will yield inaccurate predictions. The most common metrics such as MAPE or RMSE, however, are not suitable for the evaluation of forecasting errors, especially for lumpy and intermittent demand patterns, as they do not sufficiently account for, e.g., temporal shifts (prediction before or after actual demand) or cost-related aspects. Therefore, we propose a novel metric that, in addition to statistical considerations, also addresses business aspects. Additionally, we evaluate the metric based on simulated and real demand time series from the automotive aftermarket

ML-Based Teaching Systems: A Conceptual Framework

As the shortage of skilled workers continues to be a pressing issue, exacerbated by demographic change, it is becoming a critical challenge for organizations to preserve the knowledge of retiring experts and to pass it on to novices. While this knowledge transfer has traditionally taken place through personal interaction, it lacks scalability and requires significant resources and time. IT-based teaching systems have addressed this scalability issue, but their development is still tedious and time-consuming. In this work, we investigate the potential of machine learning (ML) models to facilitate knowledge transfer in an organizational context, leading to more cost-effective IT-based teaching systems. Through a systematic literature review, we examine key concepts, themes, and dimensions to better understand and design ML-based teaching systems. To do so, we capture and consolidate the capabilities of ML models in IT-based teaching systems, inductively analyze relevant concepts in this context, and determine their interrelationships. We present our findings in the form of a review of the key concepts, themes, and dimensions to understand and inform on ML-based teaching systems. Building on these results, our work contributes to research on computer-supported cooperative work by conceptualizing how ML-based teaching systems can preserve expert knowledge and facilitate its transfer from SMEs to human novices. In this way, we shed light on this emerging subfield of human-computer interaction and serve to build an interdisciplinary research agenda

On the Perception of Difficulty: Differences between Humans and AI

With the increased adoption of artificial intelligence (AI) in industry and society, effective human-AI interaction systems are becoming increasingly important. A central challenge in the interaction of humans with AI is the estimation of difficulty for human and AI agents for single task instances.These estimations are crucial to evaluate each agent's capabilities and, thus, required to facilitate effective collaboration. So far, research in the field of human-AI interaction estimates the perceived difficulty of humans and AI independently from each other. However, the effective interaction of human and AI agents depends on metrics that accurately reflect each agent's perceived difficulty in achieving valuable outcomes. Research to date has not yet adequately examined the differences in the perceived difficulty of humans and AI. Thus, this work reviews recent research on the perceived difficulty in human-AI interaction and contributing factors to consistently compare each agent's perceived difficulty, e.g., creating the same prerequisites. Furthermore, we present an experimental design to thoroughly examine the perceived difficulty of both agents and contribute to a better understanding of the design of such systems

On the Perception of Difficulty: Differences between Humans and AI

With the increased adoption of artificial intelligence (AI) in industry and society, effective human-AI interaction systems are becoming increasingly important. A central challenge in the interaction of humans with AI is the estimation of difficulty for human and AI agents for single task instances. These estimations are crucial to evaluate each agent\u27s capabilities and, thus, required to facilitate effective collaboration. So far, research in the field of human-AI interaction estimates the perceived difficulty of humans and AI independently from each other. However, the effective interaction of human and AI agents depends on metrics that accurately reflect each agent\u27s perceived difficulty in achieving valuable outcomes. Research to date has not yet adequately examined the differences in the perceived difficulty of humans and AI. Thus, this work reviews recent research on the perceived difficulty in human-AI interaction and contributing factors to consistently compare each agent\u27s perceived difficulty, e.g., creating the same prerequisites. Furthermore, we present an experimental design to thoroughly examine the perceived difficulty of both agents and contribute to a better understanding of the design of such systems

In-vitro characterization of a cochlear implant system for recording of evoked compound action potentials

<p>Abstract</p> <p>Background</p> <p>Modern cochlear implants have integrated recording systems for measuring electrically evoked compound action potentials of the auditory nerve. The characterization of such recording systems is important for establishing a reliable basis for the interpretation of signals acquired in vivo. In this study we investigated the characteristics of the recording system integrated into the MED-EL PULSARCI¹⁰⁰cochlear implant, especially its linearity and resolution, in order to develop a mathematical model describing the recording system.</p> <p>Methods</p> <p>In-vitro setup: The cochlear implant, including all attached electrodes, was fixed in a tank of physiologic saline solution. Sinusoidal signals of the same frequency but with different amplitudes were delivered via a signal generator for measuring and recording on a single electrode.</p> <p>Computer simulations: A basic mathematical model including the main elements of the recording system, i.e. amplification and digitalization stage, was developed. For this, digital output for sinusoidal input signals of different amplitudes were calculated using in-vitro recordings as reference.</p> <p>Results</p> <p>Using an averaging of 100 measurements the recording system behaved linearly down to approximately -60 dB of the input signal range. Using the same method, a system resolution of 10 μV was determined for sinusoidal signals. The simulation results were in very good agreement with the results obtained from in-vitro experiments.</p> <p>Conclusions</p> <p>The recording system implemented in the MED-EL PULSARCI¹⁰⁰cochlear implant for measuring the evoked compound action potential of the auditory nerve operates reliably. The developed mathematical model provides a good approximation of the recording system.</p

Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice

The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI or human, i.e., to achieve complementary team performance (CTP). To realize this complementarity potential, humans need to exercise discretion in following AI’s advice, i.e., appropriately relying on the AI’s advice. While previous work has focused on building a mental model of the AI to assess AI recommendations, recent research has shown that the mental model alone cannot explain appropriate reliance. We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP. In this study, we demonstrate the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making

An fMRI Study

Individuals with borderline personality disorder (BPD) are characterized by emotional instability, impaired emotion regulation and unresolved attachment patterns associated with abusive childhood experiences. We investigated the neural response during the activation of the attachment system in BPD patients compared to healthy controls using functional magnetic resonance imaging (fMRI). Eleven female patients with BPD without posttraumatic stress disorder (PTSD) and 17 healthy female controls matched for age and education were telling stories in the scanner in response to the Adult Attachment Projective Picture System (AAP), an eight-picture set assessment of adult attachment. The picture set includes theoretically-derived attachment scenes, such as separation, death, threat and potential abuse. The picture presentation order is designed to gradually increase the activation of the attachment system. Each picture stimulus was presented for 2 min. Analyses examine group differences in attachment classifications and neural activation patterns over the course of the task. Unresolved attachment was associated with increasing amygdala activation over the course of the attachment task in patients as well as controls. Unresolved controls, but not patients, showed activation in the right dorsolateral prefrontal cortex (DLPFC) and the rostral cingulate zone (RCZ). We interpret this as a neural signature of BPD patients’ inability to exert top-down control under conditions of attachment distress. These findings point to possible neural mechanisms for underlying affective dysregulation in BPD in the context of attachment trauma and fear

Traumatization and mental distress in long-term prisoners in Europe

This article investigates the prevalence of traumatization and mental distress in a sample of 1055 male European long-term prisoners as part of a wider study of the living conditions of prisoners serving sentences of at least five years in Belgium, Croatia, Denmark, England, Finland, France, Germany, Lithuania, Poland, Spain and Sweden. Data were collected in a written survey using the Posttraumatic Diagnostic Scale (PDS), the Brief Symptom Inventory (BSI) as well as questions on attempted suicide and auto-aggressive behaviour. Participants experienced a mean of three traumatic events, with 14 per cent developing a Posttraumatic Stress Disorder (PTSD) subsequently. In each national sample, more than 50 per cent of the participants were in need of treatment because of psychological symptoms and nearly one-third had attempted suicide

Phagocytosis and LPS alter the maturation state of β-amyloid precursor protein and induce different Aβ peptide release signatures in human mononuclear phagocytes

<p>Abstract</p> <p>Background</p> <p>The classic neuritic β-amyloid plaque of Alzheimer's disease (AD) is typically associated with activated microglia and neuroinflammation. Similarly, cerebrovascular β-amyloid (Aβ) deposits are surrounded by perivascular macrophages. Both observations indicate a contribution of the mononuclear phagocyte system to the development of β-amyloid.</p> <p>Methods</p> <p>Human CD14-positive mononuclear phagocytes were isolated from EDTA-anticoagulated blood by magnetic activated cell sorting. After a cultivation period of 72 hours in serum-free medium we assessed the protein levels of amyloid precursor protein (APP) as well as the patterns and the amounts of released Aβ peptides by ELISA or one-dimensional and two-dimensional urea-based SDS-PAGE followed by western immunoblotting.</p> <p>Results</p> <p>We observed strong and significant increases in Aβ peptide release upon phagocytosis of acetylated low density lipoprotein (acLDL) or polystyrene beads and also after activation of the CD14/TLR4 pathway by stimulation with LPS. The proportion of released N-terminally truncated Aβ variants was increased after stimulation with polystyrene beads and acLDL but not after stimulation with LPS. Furthermore, strong shifts in the proportions of single Aβ_1-40and Aβ_2-40variants were detected resulting in a stimulus-specific Aβ signature. The increased release of Aβ peptides was accompanied by elevated levels of full length APP in the cells. The maturation state of APP was correlated with the release of N-terminally truncated Aβ peptides.</p> <p>Conclusions</p> <p>These findings indicate that mononuclear phagocytes potentially contribute to the various N-truncated Aβ variants found in AD β-amyloid plaques, especially under neuroinflammatory conditions.</p