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Using magnetic nanoparticles for extracorporeal magnetic heating applications in bio-medical technology allows higher external field amplitudes and thereby the utilization of particles with higher coercivities (HC). In this study, we report the synthesis and characterization of high coercivity cobalt ferrite nanoparticles following a wet co-precipitation method. Particles are characterized with magnetometry, X-ray diffraction, Mössbauer spectroscopy, transmission electron microscopy (TEM) and calorimetric measurements for the determination of their specific absorption rate (SAR). In the first series, CoxFe3−xO4 particles were synthesized with x = 1 and a structured variation of synthesis conditions, including those of the used atmosphere (O2 or N2). In the second series, particles with x = 0 to 1 were synthesized to study the influence of the cobalt fraction on the resulting magnetic and structural properties. Crystallite sizes of the resulting particles ranged between 10 and 18 nm, while maximum coercivities at room temperatures of 60 kA/m for synthesis with O2 and 37 kA/m for N2 were reached. Magnetization values at room temperature and 2 T (MRT,2T) up to 60 Am2/kg under N2 for x = 1 can be achieved. Synthesis parameters that lead to the formation of an additional phase when they exceed specific thresholds have been identified. Based on XRD findings, the direct correlation between high-field magnetization, the fraction of this antiferromagnetic byphase and the estimated transition temperature of this byphase, extracted from the Mössbauer spectroscopy series, we were able to attribute this contribution to akageneite. When varying the cobalt fraction x, a non-monotonous correlation of HC and x was found, with a linear increase of HC up to x = 0.8 and a decrease for x > 0.8, while magnetometry and in-field Mössbauer experiments demonstrated a moderate degree of spin canting for all x, yielding high magnetization. SAR values up to 480 W/g (@290 kHz, 69 mT) were measured for immobilized particles with x = 0.3, whit the external field amplitude being the limiting factor due to the high coercivities of our particles.
Strategic management is pivotal within production and operations, especially in sectors like the railway industry where the complexity and scale of projects such as locomotive retrofits with European Train Control System (ETCS) installations demand meticulous planning and execution. This form of management is crucial for aligning operational efficiency and safety with broader organizational goals through systematic planning, monitoring, analysis, and assessment. Retrofit projects in the railway industry, aimed at upgrading locomotives to enhance their performance, efficiency, and compliance with new standards, are quintessential examples of strategic management at work. These projects not only demand a careful allocation of resources but also necessitate optimal logistics and resource management, particularly when components must be shipped internationally and installed on-site. The effective management of these elements ensures that the retrofitting processes are both streamlined and cost efficient.
The rapid growth and advancements in deep learning have captured the attention of tech companies worldwide, prompting them to explore the replacement of traditional solutions with deep learning approaches across various fields. A notable area of focus is Human-Computer Interaction, particularly in the development of Sign Language Recognition systems. This thesis presents the design and implementation of a sign language recognition system using deep learning techniques, incorporating three different model architectures: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) networks. The objective is to create an isolated sign language recognition application which can be served as a tool for learning and practicing sign language. The system integrates MediaPipe Holistic for precise hand and body keypoint detection with deep learning models for sequence prediction. A user-friendly interface, developed with PyQt5, enables users to capture new sign language samples, train the models, and evaluate their performance. Key functionalities include real-time video frames capture, gesture recognition, and text-to-speech conversion, which provides auditory feedback for the recognized signs. This thesis explores the performance of CNN, GRU, and LSTM models for recognizing six common German sign language phrases. The CNN and GRU models achieved perfect scores, with 100\% accuracy, precision, recall, and F1-scores, indicating high effectiveness in recognizing the sign language phrases. The LSTM model, while slightly less accurate, still performed reliably with an accuracy of 78\% and an F1 score of 0.77. These results demonstrate the potential of the CNN and GRU models for effective isolated sign language interpretation, highlighting their suitability as accessible communication solutions for the deaf community and also for learning purposes.
In this paper we contextualize Carl Menger’s work in relation to the transformations of German political economy from the 1860s to the 1890s. We demonstrate that his Grundsätze (1871) was a culmination of the German subjectivist tradition which had started in the early nineteenth century. Menger’s synthesis of this tradition is comparable to Adam Smith’s synthesis of earlier knowledge in theWealth of Nations (1776). Menger’s contribution was continuous with the intellectual project of leading German economists, such as Wilhelm Roscher, to whom Menger had dedicated his book. Roscher, however, also promoted a historical turn, that was combined with a progressive policy agenda by a new generation of German economists after they founded the Verein für Socialpolitik in 1872. These divergent Roscherian legacies clashed vehemently in the Methodenstreit. During this debate Menger elaborated in his Untersuchungen (1883) an evolutionary and spontaneous theory of institutional change, in line with the legacy of the Scottish Enlightenment and in contrast to a more rationalist and constructivist theory of institutional change expounded by Gustav Schmoller and other Verein economists. The new policy-oriented direction of German political economy carried the day, also due the fundamental socio-economic transformations in the German and Austro-Hungarian Empires, and prompted Menger to restate in 1891 the social policy agenda of the classical political economists, most prominently Smith. Menger’s recurrent proximities to Smithian political economy – in the synthetic contribution of 1871, the theoretical innovation of 1883, and the policy agenda of 1891 – suggest that his arguments are best understood as a defense of what Boettke has called the “mainline” in economics.
Powder metallurgy (PM) offers several advantages over conventional melt metallurgy, including improved homogeneity, fine grain size, and pseudo-alloying capabilities. Transitioning from conventional methods to PM can result in significant enhancements in material properties and production efficiency by eliminating unnecessary process steps. Dynamic compaction techniques, such as impulse and explosive compaction, aim to achieve higher powder density without requiring sintering, further improving PM efficiency. Among these techniques, magnetic pulse compaction (MPC) has gained notable interest due to its unique process mechanics and distinct advantages. MPC utilizes the rapid discharge of energy stored in capacitors to generate a pulsed electromagnetic field, which accelerates a tool to compress the powder. This high-speed process is particularly well-suited for compacting complex geometries and finds extensive application in industries such as powder metallurgy, welding, die forging, and advanced material manufacturing. This paper provides an overview of recent advancements and applications of MPC technology, highlighting its capabilities and potential for broader integration into modern manufacturing processes.
With the increasing complexity of software development, the efficiency of the coding process, especially in domain-specific environments, is becoming a key factor. This thesis presents the design and implementation of a Domain Specific Language Server based on the Language Server Protocol (LSP), aimed at optimizing the code-writing process, reducing errors, and facilitating the seamless integration of new developers into a corporate environment. The project, implemented at GK Software SE, examines the issues that developers face when writing code. Particular attention is paid to the need for real-time diagnostics.
The paper examines the technical aspects associated with the development of a language server. The integration process with various Integrated Development Environments (IDEs) is also considered. Differences in approaches and difficulties encountered during implementation are analyzed. The language server should optimize the development process and reduce the likelihood of errors thanks to real-time feedback and automated code checks. This work contributes to the understanding of how LSP-based tools can be used to improve the efficiency of software development in specialized areas.
