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Decision making is an intrinsic and complex aspect of social work practice, requiring consideration of diverse but connected aspects. Decisions are often required as to whether a situation requires protective state intervention or whether it reaches the criteria for public or charitable services. Such instances of deciding whether or not a situation is ‘on one side of the line or the other’ are referred to in this article as ‘threshold judgements’. This article draws on experiences and material from a range of social work contexts to explore generalisable theory-informed understandings of ‘threshold judgements’ and ‘threshold decisions’ to develop knowledge and skills on this topic. The article outlines signal detection theory and evidence accumulation (‘tipping point’) theory and discusses these as ways to understand the key concepts underpinning threshold decisions in social work. We then argue that although these threshold concepts are a necessary part of decision making in social work, as in many other aspects of life, they are not sufficient. Operationalising such decisions requires some form of sense-making. Naturalistic decision making and heuristic models of judgement are discussed as frameworks for practice which seem to be useful in this context.
Demographic changes have led to an increase in older people in prisons. Whereas the rehabilitative process of younger offenders is geared towards their reintegration into the labour market, successful ageing should be a policy aim for older prisoners. This study explores how older incarcerated persons view their ageing. A qualitative study using a written survey with only the single question What does ageing in prison mean to you? was conducted in Bavaria, Germany. A total of 64 prisoners (61 male, 3 female) supplied answers varying in length from a few words to several pages. The thematic analysis revealed that together with health concerns, social relations and everyday activities, the uncertainty of the future was a central focus point for the older adults in prison. The authors propose that a positive vision of the future needs to be included in any model of successful ageing. If successful ageing is used as an aim for older prisoners, more attention needs to be paid to support interventions during and after the release process.
Data engineering is an integral part of the data science process. It comprises tasks such as data ingestion, data transformation, and data quality assurance. In order to fulfill these tasks, schema inference is an important capability. Its goal is to detect the structure of a dataset and to derive metadata on hierarchies, data types, etc. Artificial intelligence (AI) has the potential to automate schema inference and thus increase the efficiency of the data science process. However, as government institutions are subject to special regulations, explainability of AI models can be a mandatory requirement. Goal of this research protocol is to plan a systematic review of literature on schema inference for tabular data with explainable AI (XAI). This third version was derived from two earlier review protocols.
We propose a novel two-scale (meso-macro-scale) approach to computationally capture cancellous bone remodelling allowing for efficient and effective numerical implementation. Therein, the macro-scale is governed by the well-established kinematics and kinetics of one-scale continuum bone remodelling. However, the constitutive behaviour is not postulated phenomenologically at the macro-scale, but rather follows from the meso-scale. There, for the sake of computational efficiency, the trabecular architecture is idealised as a truss network with the cross-sectional area of the trabeculae adapting to mechanical loading. Then, the meso- and the macro-scale are coupled through up- and down-scaling. Computational results on benchmark problems from bio-mechanics demonstrate that the proposed two-scale approach is effective from a modelling perspective and efficient from a computational perspective. In particular, it automatically captures anisotropy resulting from the irregular trabecular architecture at the meso-scale, and, most importantly, enables the direct investigation of different trabecular structures at the meso-scale, thereby serving as a virtual “magnifiying glass”. As an outlook, the proposed two-scale approach to cancellous bone remodelling provides an excellent launch pad for further extension, e.g., by considering more complex trabecular architectures and/or through inclusion of micro-scale bone cellular activities.
Heat transport in catalytic reactors is one of the most important factors in reactor design, especially for highly endo- or exothermic reactions. The limited thermal conductivity of randomly packed beds can be significantly improved when alternative catalyst supports are used. A new generation thereof are periodic open cellular
structures (POCS), which are used in this study as catalyst support for the partial oxidation of methanol to formaldehyde, an exothermic gas phase reaction. A comparison to traditional packed bed reactors revealed that a POCS made of the highly conductive alloy AlSi10Mg can significantly reduce the hot-spot temperature, allowing
almost isothermal operation at optimal reaction conditions. The improved temperature control allows for operation with higher methanol inlet concentrations and wall temperatures as compared to the commercial process. The beneficial heat transport properties of POCS were used to conduct isothermal kinetic measurements at elevated temperature. Based on these findings and the design freedom of additive manufacturing, an optimized cellular structure was designed and fabricated, which improved the heat transport especially in the critical inlet section of the reactorated, which improved the heat transport especially in the critical inlet section of the reactor.
Einsatz, Nutzen und Grenzen von ChatGPT und anderen Large Language Modellen an den bayerischen HAWs
(2024)
Die Studie analysiert Einsatz, Nutzen und Grenzen von ChatGPT und Large Language Modellen (LLMs) in der Lehre an bayerischen Hochschulen für angewandte Wissenschaften (HAWs). Sie basiert auf einer schriftlichen Onlinebefragung und leitfadengestützten Interviews unter Lehrenden, Studierenden und Funktions-trägerinnen und -träger. Insgesamt nahmen 1570 Personen an der Onlineumfrage teil und es wurden 27 Interviews geführt. Zwar variieren die Ergebnisse je nach Befragtengruppe, aber tendenziell lassen sich folgende Einschätzungen auf Basis der empirischen Daten treffen:
→ Die meisten Teilnehmenden haben ein geringes Verständnis der Nutzbarkeit von LLMs und deren technologischer Funktionsweise. Die Nutzbarkeit von Open Source LLMs oder domänenspezifisch trainierten LLMs für das eigene Fachgebiet kennt der Großteil der Teilnehmenden (überhaupt) nicht oder nur teilweise.
→ Die Einsatzhäufigkeit von ChatGPT bzw. LLMs in Lehre, Studium und Arbeitsalltag ist noch gering, vor allem wegen fehlender sinnvoller Einsatzmöglichkeiten, mangelnder Qualität der Ergebnisse und rechtlicher Bedenken. Die ChatGPT-Plusversion wird von den meisten Teilnehmenden nie genutzt.
→ Die wahrgenommene Nützlichkeit der Einsatzszenarien von ChatGPT variiert je nach ChatGPT-Version und Zielgruppe, wobei das Zusammenfassen und die Verbesserung von Texten als nützlich eingeschätzt werden.
→ Ein Verbot von ChatGPT wird überwiegend abgelehnt. Stattdessen besteht seitens der Lehrenden und Studierenden der Wunsch nach vermehrter Aufklärung über die Chancen und Grenzen von ChatGPT und Co. Zudem erhoffen sie sich finanzielle, technische und didaktische Unterstützung durch die Hochschulen.
→ Die Teilnehmenden sehen ChatGPT als eine Chance zur Verbesserung der Lehre, aber auch als eine Herausforderung für die Prüfungskultur, die rechtliche Sicherheit und die Kompetenzentwicklung.
• In Bezug auf zukünftig notwendige Kompetenzen wird angemerkt, dass es zu einem Kompetenz-verlust bei den Studierenden als Konsequenz der ChatGPT-Nutzung kommen kann.
• Die Frage nach der rechtlichen Sicherheit bezieht sich nicht nur auf mögliche Datenschutz- oder Copyrightverletzungen, sondern auch auf das Prüfungswesen.
• Die Art und Weise von Prüfungen wird sich ändern (müssen), wobei mehrere Optionen thematisiert werden: von der Kennzeichnungspflicht bei der Nutzung von ChatGPT und Co. über den Wegfall reiner Wissensabfragen in Prüfungen bis hin zu den Ideen, keine theoretischen Bachelorarbeiten mehr zu vergeben und vermehrt mündliche Prüfungen einzusetzen.
Die Studie schließt mit einer Diskussion über die Rolle und Tragweite der generativen KI in der Hoch-schullehre und gibt Hinweise auf Materialien für die Gestaltung von innovativen und verantwortungs-vollen Lernszenarien.
It is known that ethylene plays an important role in the quality characteristics of fruits, especially in storage. To avoid the deterioration of fruits caused by ethylene, titanium dioxide (TiO2) has been used due to its photocatalytic capacity. The aim of this study was to develop films based on two types of biopolymers, Mater-Bi (MB) and poly-lactic acid (PLA), with nanoparticles of TiO2 and to determine their ethylene removal capacity and its application in bananas. First, the films were fabricated through an extrusion process with two different concentrations of TiO2 (5 and 10% w/w). Then, the films were characterized by their structural (FTIR), morphological (SEM), thermal (DSC and TGA), dynamic (DMA), barrier, and mechanical properties. The ethylene removal capacities of the samples were determined via gas chromatography and an in vivo study was also conducted with bananas for 10 days of storage. Regarding the characterization of the films, it was possible to determine that there was a higher interaction between PLA with nano-TiO2 than MB; moreover, TiO2 does not agglomerate and has a larger contact surface in PLA films. Because of this, a higher ethylene removal was also shown by PLA, especially with 5% TiO2. The in vivo study also showed that the 5% TiO2 films maintained their quality characteristics during the days in storage. For these reasons, it is possible to conclude that the films have the capacity to remove ethylene. Therefore, the development of TiO2 films is an excellent alternative for the preservation of fresh fruits.
This study aims to compare two different implementations of speech emotion recognition models.The emphasis is directed towards evaluating their efficacy in capturing and characterizing dialogues
portrayed by actors within a film scene to create suitable musical intervals. The goal of the overarching research intends to derive indications to enhance the compositional process of film scores by recognizing the emotion in a particular scene. Based on established deep learning models, the study delves into the exploration of two distinct emotion classification metrics: The Six Emotion Prediction and the Valence/Arousal/Dominance Prediction. To facilitate a comparative analysis, a preliminary study an a following survey is deployed. The preliminary study confirms a significant difference in the generated MIDI data. For this reason, a survey is essential to detect the better fitting algorithm. Participants are tasked to rate the affective suitability of eight generated interval sequences to the corresponding film scenes. The Suitability is verified quantitatively using a bidirectional rating system. Both model assessments are conducted within a uniform sound design, thus ensuring unbiased conditions for evaluation. Upon a thorough examination of our extensive analysis, a preference for method A becomes increasingly evident.