Aschaffenburg University of Applied Sciences

Left panel: Scheme of the XPAG-immune thrombocytopenia trial. The dexamethasone (DEX) arm consisted of DEX 40 mg/day for days 1-4 for one to three cycles every 28 days to a maximum of 12 weeks, cycles 2 + 3 were optional. Patients randomised to eltrombopag (ETB) + DEX received eltrombopag in combination with a short course of high-dose DEX beginning on day 1 (40 mg/day during days 1-4). The starting dose of ETB was 50 mg/day for 2 weeks; thereafter, the ETB dose was increased by 25 mg for all patients who did not achieve the target platelet count of ≥50 × 109/L. The ETB tapering was performed by decreasing the dose by 25 mg every 2 weeks to a minimum dose of 25 mg every other day for all patients. Right upper panel: Clinical outcomes in patients treated with first-line ETB + DEX. Patients displayed a qualitatively longer response duration and qualitatively reduced usage of rescue medication. Right lower panel: The immunological T-cell response was different in treatment responders and non-responders. Patients with sustained response (responders) displayed a high T-cell clonality at baseline. Clones are depicted as bubbles (right side).

BackgroundCardiopulmonary resuscitation (CPR) is vital for improving patient outcomes in medical emergencies. Both laypersons and health care professionals often seek guidance on performing CPR. In today’s digital age, many turn to easily accessible platforms such as YouTube for practical skills.ObjectiveThis study evaluates the didactic and content quality of CPR videos on YouTube using comprehensive checklists and investigates the association between the assigned quality scores and type of publisher, view count, and video rankings.MethodsVideos were included based on defined search terms and exclusion criteria. Two emergency physicians rated each video independently using validated checklists concerning content and didactic quality. Linear regression analysis was performed to assess the relationships between video quality scores and view counts, as well as video rankings.ResultsOf the 250 videos identified, 74 (29.6%) met the inclusion criteria. On the content checklist, videos scored an average of 56.5% (SD 19.2%), and on the didactic checklist, they scored 66.6% (SD 14.3%); none achieved the maximum score. Videos from official medical institutions scored significantly higher in content quality compared to nonofficial sources (P=.04). Video quality scores were not associated with video rankings or view counts.ConclusionsThe study highlights substantial variability in the didactic and content quality of CPR-related videos on YouTube. For medical educators, this underlines the need to curate and recommend reliable online resources or to develop new high-quality content aligned with established checklists. For the general public, the findings caution against relying on popularity metrics as indicators of accuracy and emphasize the importance of guidance from trusted institutions.

Pilot production is a critical transitional phase in the process of new product development or manufacturing, aiming at ensuring that products are thoroughly validated and optimized before entering full-scale production. During this stage, a key challenge is how to leverage limited resources to build data infrastructure and conduct data analysis to establish and verify quality control. This paper presents the implementation of a cyber–physical system (CPS) for a lithium battery pilot assembly line. A machine learning-based predictive model was employed to establish quality control mechanisms. Process knowledge-guided data analysis was utilized to build a quality prediction model based on the collected battery data. The model-centric concept of ‘virtual quality’ enables early quality judgment during production, which allows for flexible quality control and the determination of optimal process parameters, thereby reducing production costs and minimizing energy consumption during manufacturing.

Cut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions, necessitating safe and efficient lane change strategies. We propose a Dynamic Bayesian Network (DBN) framework to integrate lateral evidence with safety assessment models, thereby predicting lane changes and ensuring safe cut-in maneuvers effectively. Our proposed framework comprises three key probabilistic hypotheses (lateral evidence, lateral safety, and longitudinal safety) that facilitate the decision-making process through dynamic data processing and assessments of vehicle positions, lateral velocities, relative distance, and Time-to-Collision (TTC) computations. The DBN model's performance compared with other conventional approaches demonstrates superior performance in crash reduction, especially in critical high-speed scenarios, while maintaining a competitive performance in low-speed scenarios. This paves the way for robust, scalable, and efficient safety validation in automated driving systems.

Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This study presents a machine learning workflow to analyze drug-induced changes in neuronal biosensor data using complex network measures from graph theory. Microelectrode array recordings of neuronal networks exposed to bicuculline, a GABA receptor antagonist known to induce hypersynchrony, demonstrated the workflow’s ability to detect and characterize pharmacological effects. The workflow integrates network-based features with synchrony, optimizing preprocessing parameters, including spike train bin sizes, segmentation window sizes, and correlation methods. It achieved high classification accuracy (AUC up to 90%) and used Shapley Additive Explanations to interpret feature importance rankings. Significant reductions in network complexity and segregation, hallmarks of epileptiform activity induced by bicuculline, were revealed. While bicuculline’s effects are well established, this framework is designed to be broadly applicable for detecting both strong and subtle network alterations induced by neuroactive compounds. The results demonstrate the potential of this methodology for advancing biosensor applications in neuropharmacology and drug discovery.

Abstract Livable cities involve such quality-of-life factors as transportation, convenience of daily life, education, and a safe and stable built and natural environment. The livability of a city also has social and psychological dimensions, such as emotion and perception. This paper explores how the advantages of new transportation technology can be realized under mixed-traffic conditions, while meeting the requirements for safety, convenience, and high educational standards. One of the key challenges to realizing the advantages of a new technology, such as autonomous shuttle buses, under mixed-traffic conditions and under adverse weather conditions is how to strike a balance between innovation and adherence to essential urban demands. Ensuring that the introduction of advanced technologies like autonomous shuttle buses does not compromise safety is of the utmost importance. Under mixed-traffic conditions, autonomous shuttle buses must negotiate interactions with human-driven vehicles, pedestrians, and bicyclists. Predictable and safe driving behavior is vital to gaining the confidence and acceptance of all road users. Additionally, adopting vehicle-to-everything (V2X) communication enables real-time information exchange with other vehicles and infrastructure, which enhance safety. Autonomous shuttle buses also face crucial challenges in adverse weather conditions. Robust sensor systems are required to accurately perceive the environment despite rain, snow, fog, and other weather-related constraints. Continual learning and software updates ensure the system’s ability to adapt to changing weather conditions. To overcome these challenges, collaboration with regulatory bodies and traffic management authorities is essential. Furthermore, public awareness campaigns and educational initiatives can inform the public about the safety measures and benefits of autonomous shuttle buses, fostering acceptance and reducing concerns during the transition.

As the correlation between design rules and process limitations is of the upmost importance for the full exploitation of any manufacturing technology, we report a design guide for hybrid-additive manufacturing of Inconel 718. Basic limitations need to be evaluated for this particular hybrid approach that combines laser powder bed fusion (PBF-LB/M) and in situ high-speed milling. Fundamental geometric limitations are examined with regard to the minimum feasible wall thickness, cylinders, overhanging structures, and chamfers. Furthermore, geometrical restrictions due to the integrated three-axis milling process with respect to inclinations, inner angles, notches, and boreholes are investigated. From these findings, we derive design guidelines for a reliable build process using this hybrid manufacturing. Additionally, a design guideline for the hybrid-additive manufacturing approach is presented, depicting a step-to-step guide for the adjustment of constructions. To demonstrate this, a powder nozzle for a direct energy deposition (DED-LB/M) process is redesigned following the previously defined guidelines. This redesign encompasses analysis of the existing component and identification of problematic areas such as flat angles, leading to a new construction that is suitable for a hybrid-additive manufacturing approach.

This paper proposes an approach to extend the bandwidth of 4 KHz speech signals upto 8 KHz using a simple feed-forward neural network (FFNN). We employ the commonly used Short-Time Fourier Transform (STFT) values as features and boost its resolution by incorporating Mel Frequency Cepstral Coefficients (MFCCs). Apart from this feature-level fusion, a layer-level fusion is also examined to demonstrate the expansion in bandwidth. This is an advantage over traditional artificial bandwidth extension (ABE) methods using neural nets, which are heavily data-driven and require high model complexity. The proposed approach exploits the combination of relevant spectral features to avoid the requirement of highly complex architectures. This is because the combined features capture more intricate features relating the 4 KHz signal to the 8 KHz one. This facilitates the deployment of the technique on resourceconstrained devices, even with limited data. These reconstructed high pass (8 KHz) audio signals are perceptually better than the original 4 KHz bandwidth signals. Additionally, the performance of the proposed approach exceeds that of state-of-the-art methods in the literature. This approach has potential applications in enhancing the quality by extending the bandwidth of audio signals in telecommunication systems and also finds application in cochlear implant (CI) users.