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What motivates companies to take the decision to decarbonise?
Get PDFWhat motivates industrial companies to decarbonise? While climate policy has intensified, the specific factors driving corporate decisions remain underexplored. This article addresses that gap through a mixed-methods study combining qualitative insights from a leading automotive supplier with quantitative data from over 800 manufacturing companies in Germany. The study distinguishes between internal motivators—such as risk reduction, future-proofing, and competitive positioning—and external drivers like regulation, supply chain pressure, and investor expectations. Results show that internal economic logic is the strongest trigger: companies act more ambitiously when decarbonisation aligns with their strategic interests. Positive motivators outperform external drivers in both influence and impact on ambition levels. For instance, long-term cost risks were rated more relevant than reputational gains or regulatory compliance. The analysis also reveals how company size, energy intensity, and supply chain position shape motivation patterns. The findings suggest a new framing for climate policy: rather than relying solely on mandates, policies should strengthen intrinsic motivators. Aligning business interests with societal goals is not only possible—it is a pathway to more ambitious, resilient, and timely decarbonisation. By turning external pressure into internal logic, companies can move from compliance to leadership in the climate transition
Emotion-focused ego-state coaching reduces communication apprehension: evidence from a randomized controlled study
Get PDFBackground: Communication apprehension (CA) is a widespread phenomenon negatively affecting communicative skills, psychological well-being, and social connectedness. This randomized controlled study investigated whether online emotion-focused ego-state coaching, based on the emTrace framework, can sustainably reduce CA.
Methods: A total of 260 German-speaking participants with elevated CA were randomly assigned to one of two ego-state coaching interventions: Core Transformation (CT) or Smart Part Lab (SPL). CA was assessed using the PRCA-24 and a bipolar scale of subjective feeling at pretest, posttest, and 2-week follow-up.
Results: Both interventions significantly reduced CA [F(2, 258.3) = 242.14, p < 0.001; pre: M = 80.7; post: M = 63.4; follow-up: M = 62.3; η2p = 0.65), with 80% of initially high-CA participants reporting moderate or low CA at follow-up. CT induced stronger self-transcendent experiences than SPL; however, this did not translate into a stronger CA reduction, suggesting self-transcendence is not the primary mechanism of change.
Conclusion: A single 80-min online ego-state coaching session within the emTrace framework sustainably reduced trait-like CA. The findings highlight the potential of resource-oriented, emotion-focused coaching as a low-threshold intervention to foster communicative confidence and psychological resilience
Development of a Dynamic Path Planning System for Autonomous Mobile Robots Using a Multi-Agent System Approach
Get PDFAutonomous Mobile Robots (AMRs) are increasingly important in Industry 4.0 intralogistics but creating path planning systems that adapt to dynamic and uncertain Flexible Manufacturing Systems (FMS), especially managing conflicts among multiple AMRs with a need for scalable decentralised solutions, remains a significant challenge. This research introduces a dynamic path planning system for AMRs designed for reactive adaptation to FMS disturbances and generalisation across factory layouts, incorporating support for multiple AMRs with integrated conflict avoidance. The system is built on a Multi-Agent Systems (MAS) architecture, where software AMR agents independently calculate their paths using a hybrid Genetic Algorithm (GA) that employs Cell-Based Decomposition (CBD) and optimises path length, smoothness, and overlap via a multi-objective fitness function. Multi-AMR conflict avoidance is implemented using the Iterative Exclusion Principle (IEP), which facilitates priority-based planning, knowledge sharing through Predictive Collision Avoidance (PCA), and iterative replanning among agents communicating via a blackboard agent. Verification demonstrated the system’s ability to successfully avoid deadlocks for up to nine AMRs and exhibit good scalability. Validation in a simulated FMS environment confirmed robust adaptation to various disturbances, including static and dynamic obstacles, while maintaining stable run times and consistent path quality. These results affirm the practical feasibility of this hybrid GA and MAS-based approach for dynamic AMR control in complex industrial settings
Juristische Problemlösung mit KI – Leistung und Grenzen großer Sprachmodelle
No full textGroße Sprachmodelle wie ChatGPT und Gemini zeigen inzwischen ein beachtliches Leistungsniveau bei der Lösung juristischer Prüfungsfragen. Auf Grundlage von 200 Multiple-Choice-Fällen dokumentiert der Beitrag die Entwicklung der maschinellen Entscheidungssicherheit zwischen 2023 und 2025. Die Analyse zeigt eine signifikante Steigerung der Trefferquoten, offenbart aber auch strukturelle Grenzen der Modelle bei komplexen, mehrgliedrigen Fallkonstellationen. Der Beitrag bewertet die Einsatzmöglichkeiten aktueller KI-Modelle im juristischen Alltag und zeigt, wo der Mensch der Maschine überlegen bleibt
Hearing emotions: fine-tuning speech emotion recognition models
No full textOver the past few decades, scholars and academics from various disciplines have been motivated to develop automated emotion detection systems. In this pursuit, audio data and especially prosodic features, holds the most promise to deliver satisfying results. Therefore, main aim of this approach was to evaluate standard machine learning algorithms on the task of emotion recognition from audio data. We evaluate the effect of training dataset size on model performance by means of incremental fine-tuning after conducting zeroshot testing on a range of widely-used datasets in the literature, such as CREMA-D, RAVDESS, TESS, SAVEE, MELD, eNTERFACE, EmoDB, and IEMOCAP. To improve model generalizability, we used data augmentation approaches, and for robust emotion detection, we used feature extraction techniques as MFCC, ZCR, and RMS. On CREMA-mixed datasets, experimental results show great initial accuracy with CNN model. Cross-corpus validation highlights the importance of diverse datasets, showing significant accuracy improvements with incremental fine-tuning. Our research opens the door to more potent emotion detection systems in practical applications by highlighting the necessity of varied training data for robust, generalizable SER models
Adaptive signal regime for identifying transient shifts: a novel approach toward fault diagnosis in wind turbine systems
No full textIn real-world applications, the diagnostic efficiency of wind turbine systems, particularly rolling bearings, is significantly impaired by variable operating conditions such as fluctuating rotational speeds and varying loads, along with environmental disturbances including transient and non-Gaussian noises. These disturbances mask damage indicators, creating substantial challenges in accurate fault detection. Traditional diagnostic methods are often inadequate due to their sensitivity to noise and inability to identify failure signatures within multivariate random transient noise environments. To address these challenges in wind turbine fault diagnosis, this research introduces an adaptive signal processing regime with three key innovations: an adaptive signal tracking mechanism featuring real-time transient shift identification, a Dynamic Markov Transition Frequency with Adaptive Peak Rates (DMTF-APR) model for enhanced abnormality detection, and a Multi-Period Weighted Average Framework (MPWAF) that mitigates transient interference noise through the identification and replacement of anomalous signal fragments using periodic characteristics and weighted averages. Experimental validation with real-world wind turbine farm data demonstrates the framework’s superior fault diagnosis performance, particularly in scenarios with complex non-Gaussian or transient noise interference, achieving significant improvements in detection accuracy and reliability compared to conventional methods
Dynamische Netzentgelte und ihre mögliche Ausgestaltung für Deutschland
Get PDFDie aktuelle Netzentgeltsystematik wird den Anforderungen eines zunehmend von erneuerbaren Energien geprägten Stromsystems immer weniger gerecht und steht daher derzeit auf dem Prüfstand. Dynamische Netzentgelte werden in dieser Debatte häufiger als wichtiger Reformbaustein genannt, allerdings fehlt es bislang an konkreten Konzepten für deren Ausgestaltung in Deutschland. Das vorliegende Papier schließt an diese Lücke an, indem es notwendige Voraussetzungen analysiert und mögliche Ausgestaltungsformen dynamischer Netzentgelte erörtert. Es soll damit einen fundierten und konstruktiven Beitrag zur Weiterentwicklung des Netzentgeltsystems in Deutschland leisten
Herman Hollerith Conference 2024
No full textModerne digitale Technologien und Konzepte wie KI, EAM, BPM und Cloud Computing sind wesentlich für die digitale Transformation von Unternehmen und Organisationen. Ohne die Grundlagen, Nutzungsaspekte und einem breiten Diskurs dieser Technologien und Konzepte im Allgemeinen ist die Wettbewerbsfähigkeit in Gefahr. Die 1. Herman Hollerith Conference 2024 widmet sich diesen Schwerpunkten und leistet einen Beitrag zur Beantwortung wichtiger, offener IT-Fragen. Die Beiträge entstammen einem wissenschaftlichen und einem Industry Track
Zentrale ethische Prinzipien Sozialer Arbeit : Ein kompakter Überblick nach IFSW 2014/ 2018
No full textDie Förderung solidarischen Handelns gehört zu den zentralen Aufgaben Sozialer Arbeit. Hierbei ist das Engagement für Solidarität nicht das einzige ethische Prinzip, an dem sich Soziale Arbeit orientiert. Als "moralische Profession" ist sie von verschiedenen ethischen Prinzipien getragen. Eine hohe internationale Legitimität haben hierbei die von der International Federation of Social Workers erarbeiteten ethischen Prinzipien (IFSW 2018) sowie die Definition der Sozialen Arbeit (IFSW 2014), welche folgend in einer knappen Übersicht dargestellt werden
Increasing the voltage : sequencing decarbonisation with green power and efficiency
Get PDFThe industrial sector’s increasing electricity demand (direct and indirect), driven by the electrification of processes and the production of green hydrogen, poses significant challenges for achieving decarbonisation goals. While switching to renewable electricity and offsetting emissions appears straightforward, the gap between current generation capacities and projected demand remains substantial. This article analyses survey data from the Energy Efficiency Index of German Industry (EEI), revealing that manufacturing companies aim to reduce 22.1% of their 2019 emissions by 2025 and 27.3% by 2030, primarily through on-site measures. However, given the slow pace of renewable capacity expansion and the increasing electrification across sectors, it becomes evident that the envisaged green electricity share of 80% by 2030 will require far more capacity than currently planned. To address this challenge, the article introduces a decarbonisability factor to better assess on-site versus off-site measures, highlighting the need for a strategic sequencing of efficiency and renewable generation. To support decision-makers, the article calls for improved data collection and periodic reassessment to account for changing geopolitical and economic conditions