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Dementia affects individuals with various symptoms such as memory loss, word-finding difficulties and inhibited thought processes. The condition requires an environment adapted to the abilities and deficits of those affected. This study focuses on the use of multisensory tools in German nursing homes to enhance the quality of life for individuals with dementia, with an emphasis of assessing willingness to pay. A cross-sectional survey was conducted among German long-term care facilities using an online questionnaire, to assess willingness to pay for a newly developed multisensory product - described as the treasure box (“Schatzkästchen”). The study identified an acceptable price range for the treasure box of 30 EUR and 70 EUR. The survey indicates a need for analogue, multisensory products that can be used to activate and relax individuals with dementia. The findings emphasise the importance of addressing tactile and visual senses and suggest that care must be taken when determining the price of such products, considering the limited payment willingness of German nursing homes.
This case report presents the therapeutic effect of restorative neurostimulation (ReActiv8®) on chronic low back pain (CLBP) in a 44-year-old male, where the primary etiology was not lumbar disc herniation (LDH). Despite presenting with LDH at L4-L5, clinical valuations suggested lumbar microinstability and multifidus muscle dysfunction as the main contributors to his pain, without radicular symptoms. The patient had a 12-year history of CLBP, resistant to conventional treatments like physiotherapy, medications, and epidural injections. Opting for a minimally invasive approach, he underwent implantation of ReActiv8®, focusing on rehabilitating the impaired multifidus muscle. Over a 12-month therapy period, significant improvements were noted in pain levels, functionality, and quality of life, leading to a full return to work. Interestingly, follow-up imaging showed not only a substantial reduction in pain but also an unexpected resolution of the LDH at L4-L5, enhanced lumbar lordosis, and improved disc hydration, despite minor progressing Modic changes. This case underlines the potential of restorative neuro-stimulation in CLBP management, especially when the pain origin is non-discogenic. It emphasizes the importance of accurate pain source identification in CLBP treatment and suggests further research into the efficacy and applicability of neurostimulation in similar clinical scenarios.
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.
