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The advent of 5G technology has facilitated the adoption of private cellular networks in industrial settings. Ensuring reliable coverage while maintaining certain requirements at its boundaries is crucial for successful deployment yet challenging without extensive measurements. In this article, we propose the leveraging of unmanned aerial vehicles (UAVs) and Gaussian processes (GPs) to reduce the complexity of this task. Physics-informed mean functions, including a detailed ray-tracing simulation, are integrated into the GP models to enhance the extrapolation performance of the GP prediction. As a central element of the GP prediction, a quantitative evaluation of different mean functions is conducted. The most promising candidates are then integrated into an informative path-planning algorithm tasked with performing an efficient UAV-based cellular network mapping. The algorithm combines the physics-informed GP models with Bayesian optimization and is developed and tested in a hardware-in-the-loop simulation. The quantitative evaluation of the mean functions and the informative path-planning simulation are based on real-world measurements of the 5G reference signal received power (RSRP) in a cellular 5G-SA campus network at the Port of Lübeck, Germany. These measurements serve as ground truth for both evaluations. The evaluation results demonstrate that using an appropriate mean function can result in an enhanced prediction accuracy of the GP model and provide a suitable basis for informative path planning. The subsequent informative path-planning simulation experiments highlight these findings. For a fixed maximum travel distance, a path is iteratively computed, reducing the flight distance by up to 98% while maintaining an average root-mean-square error of less than 6 dBm when compared to the measurement trials.

Background Inspiratory patient effort under assisted mechanical ventilation is an important quantity for assessing patient–ventilator interaction and recognizing over and under assistance. An established clinical standard is respiratory muscle pressure P mus , derived from esophageal pressure ( P es ), which requires the correct placement and calibration of an esophageal balloon catheter. Surface electromyography (sEMG) of the respiratory muscles represents a promising and straightforward alternative technique, enabling non-invasive monitoring of patient activity. Methods A prospective observational study was conducted with patients under assisted mechanical ventilation, who were scheduled for elective bronchoscopy. Airway flow and pressure, esophageal/gastric pressures and sEMG of the diaphragm and intercostal muscles were recorded at four levels of pressure support ventilation. Patient efforts were quantified via the P mus -time product ( PTP mus ), the transdiaphragmatic pressure-time product ( PTP di ) and the EMG-time products (ETP) of the two sEMG channels. To improve the signal-to-noise ratio, a method for automatically selecting the more informative of the sEMG channels was investigated. Correlation between ETP and PTP mus was assessed by determining a neuromechanical conversion factor K EMG between the two quantities. Moreover, it was investigated whether this scalar can be reliably determined from airway pressure during occlusion maneuvers, thus allowing to quantify inspiratory effort based solely on sEMG measurements. Results In total, 62 patients with heterogeneous pulmonary diseases were enrolled in the study, 43 of which were included in the data analysis. The ETP of the two sEMG channels was well correlated with PTP mus ( r = 0.79 ± 0.25 and r = 0.84 ± 0.16 for diaphragm and intercostal recordings, respectively). The proposed automatic channel selection method improved correlation with PTP mus ( r = 0.87 ± 0.09 ). The neuromechanical conversion factor obtained by fitting ETP to PTP mus varied widely between patients ( K EMG = 4.32 ± 3.73 cm 2 O / μ V ) and was highly correlated with the scalar determined during occlusions ( r = 0.95 , p < . 001 ). The occlusion-based method for deriving PTP mus from ETP showed a breath-wise deviation to PTP mus of 0.43 ± 1.73 cm 2 O s across all datasets. Conclusion These results support the use of surface electromyography as a non-invasive alternative for monitoring breath-by-breath inspiratory effort of patients under assisted mechanical ventilation.

Inspiratory patient effort under assisted mechanical ventilation is an important quantity for assessing patient-ventilator interaction and recognizing over and under assistance. An established clinical standard is respiratory muscle pressure [Formula: see text], derived from esophageal pressure ([Formula: see text]), which requires the correct placement and calibration of an esophageal balloon catheter. Surface electromyography (sEMG) of the respiratory muscles represents a promising and straightforward alternative technique, enabling non-invasive monitoring of patient activity. A prospective observational study was conducted with patients under assisted mechanical ventilation, who were scheduled for elective bronchoscopy. Airway flow and pressure, esophageal/gastric pressures and sEMG of the diaphragm and intercostal muscles were recorded at four levels of pressure support ventilation. Patient efforts were quantified via the [Formula: see text]-time product ([Formula: see text]), the transdiaphragmatic pressure-time product ([Formula: see text]) and the EMG-time products (ETP) of the two sEMG channels. To improve the signal-to-noise ratio, a method for automatically selecting the more informative of the sEMG channels was investigated. Correlation between ETP and [Formula: see text] was assessed by determining a neuromechanical conversion factor [Formula: see text] between the two quantities. Moreover, it was investigated whether this scalar can be reliably determined from airway pressure during occlusion maneuvers, thus allowing to quantify inspiratory effort based solely on sEMG measurements. In total, 62 patients with heterogeneous pulmonary diseases were enrolled in the study, 43 of which were included in the data analysis. The ETP of the two sEMG channels was well correlated with [Formula: see text] ([Formula: see text] and [Formula: see text] for diaphragm and intercostal recordings, respectively). The proposed automatic channel selection method improved correlation with [Formula: see text] ([Formula: see text]). The neuromechanical conversion factor obtained by fitting ETP to [Formula: see text] varied widely between patients ([Formula: see text]) and was highly correlated with the scalar determined during occlusions ([Formula: see text], [Formula: see text]). The occlusion-based method for deriving [Formula: see text] from ETP showed a breath-wise deviation to [Formula: see text] of [Formula: see text] across all datasets. These results support the use of surface electromyography as a non-invasive alternative for monitoring breath-by-breath inspiratory effort of patients under assisted mechanical ventilation.

EMG is an established method to acquire the action potentials of contracted muscles. Although commercial EMG systems are available and it is one of the most researched biosignals, it has never become widely used in rehabilitation or fitness training monitoring. The reasons are technical challenges of wearable EMG systems regarding electrode placement, motion artefacts and the complex connectivity of multi-channel EMG measurements. We address this problem for the lower back and abdominal musculature, through a novel dry electrodes belt, multi-channel high density EMG circuitry and problem-specific signal processing. The subject can easily strap the dry electrodes belt around himself which provides 16 EMG channels. Interferences from the ECG and motion artefacts are reduced by a stationary wavelet decomposition. Afterwards, an inter-channel filter is applied to increase the robustness of the signals. Subject measurements during different kinds of typical abdominal and lower back training exercises were performed wearing the novel dry electrodes belt. The results show the possibility of robust EMG measurements from the lower back and abdominal muscles by utilizing the gathered redundancy, appropriately. The additional information obtained via the multi-channel EMG circuitry and spatial oversampling can be used to address current problems of EMG applications. It combines the advantages of robustness and the capability of using comfortable dry electrodes. Therefore, the proposed measurement method for acquiring spatial information about the muscle contractions from the lower trunk can be used for rehabilitation or fitness training monitoring.

The application of Gaussian processes (GPs) to large data sets is limited due to heavy memory and computational requirements. A variety of methods has been proposed to enable scalability, one of which is to exploit structure in the kernel matrix. Previous methods, however, cannot easily deal with non-stationary processes. This paper presents an efficient GP framework, that extends structured kernel interpolation methods to GPs with a non-stationary phase. We particularly treat mixtures of non-stationary processes, which are commonly used in the context of separation problems e.g. in biomedical signal processing. Our approach employs multiple sets of non-equidistant inducing points to account for the non-stationarity and retrieve Toeplitz and Kronecker structure in the kernel matrix allowing for efficient inference. Kernel learning is done by optimizing the marginal likelihood, which can be approximated efficiently using stochastic trace estimation methods. Our approach is demonstrated on numerical examples and large biomedical datasets.

We present a wafer-level vacuum-packaged (WLVP) translatory micro-electro-mechanical system (MEMS) actuator developed for a compact near-infrared-Fourier transform spectrometer (NIR-FTS) with 800–2500 nm spectral bandwidth and signal-nose-ratio (SNR) > 1000 in the smaller bandwidth range (1200–2500 nm) for 1 s measuring time. Although monolithic, highly miniaturized MEMS NIR-FTSs exist today, we follow a classical optical FT instrumentation using a resonant MEMS mirror of 5 mm diameter with precise out-of-plane translatory oscillation for optical path-length modulation. Compared to highly miniaturized MEMS NIR-FTS, the present concept features higher optical throughput and resolution, as well as mechanical robustness and insensitivity to vibration and mechanical shock, compared to conventional FTS mirror drives. The large-stroke MEMS design uses a fully symmetrical four-pantograph suspension, avoiding problems with tilting and parasitic modes. Due to significant gas damping, a permanent vacuum of ≤3.21 Pa is required. Therefore, an MEMS design with WLVP optimization for the NIR spectral range with minimized static and dynamic mirror deformation of ≤100 nm was developed. For hermetic sealing, glass-frit bonding at elevated process temperatures of 430–440 °C was used to ensure compatibility with a qualified MEMS processes. Finally, a WLVP MEMS with a vacuum pressure of ≤0.15 Pa and Q ≥ 38,600 was realized, resulting in a stroke of 700 µm at 267 Hz for driving at 4 V in parametric resonance. The long-term stability of the 0.2 Pa interior vacuum was successfully tested using a Ne fine-leakage test and resulted in an estimated lifetime of >10 years. This meets the requirements of a compact NIR-FTS.

Most patients with a rare movement disorder (MD) do not receive a molecular diagnosis, and the underlying genetic variants and mediating genes remain elusive. Here, we evaluate the diagnostic accuracy of conventional and next-generation sequencing-based genetic testing strategies in a cohort of 2,811 individuals with ataxia, spastic paraplegia and dystonia. Exome sequencing establishes genetic diagnoses in 19.3% of cases, and specificity of phenotypic features and age at testing are positive predictors. Genome analysis ‘beyond the exome’ increases the diagnostic yield by 7.5%, mostly due to the improved detection of structural variants and repeat expansions. Unsolved cases are included in the Solve-RD cohort and subjected to gene-burden analysis, providing evidence for loss-of-function variants in X-chromosomal CD99L2 causing spastic ataxia. Cellular studies show that the transmembrane protein CD99L2 occurs mainly in a ubiquitinated form and serves as an activating interactor of the calcium-dependent protease CAPN1. Ablation of cytoplasmic or extracellular domains of CD99L2 leads to its intracellular mislocalization and abrogation of its interplay with CAPN1. Transcriptome analysis in CD99L2 patient-derived fibroblasts reveals synaptic function-specific disturbances. Impaired CAPN1 activation and dysregulation of downstream neuronal pathways constitute the likely molecular cause for neurodegeneration. Here the authors compare genetic testing strategies in rare movement disorders, improve diagnostic yield with genome analysis, and establish CD99L2 as an X-linked spastic ataxia gene, showing that CD99L2–CAPN1 signaling disruption likely drives neurodegeneration.

Drawing on the dynamic capability view, we analyse how insurers adapt to climate change impacts and how adaptation relates to corporate financial performance. Based on a comprehensive literature review, we deduce seven categories of adaptation measures associated with three dynamic capability dimensions of climate change adaptation (i.e. climate knowledge absorption, climate-related operational flexibility and strategic climate integration). Using this framework, we conduct a content analysis of insurers' adaptation efforts as reported to the Carbon Disclosure Project. Regression analysis reveals positive relationships between climate knowledge absorption and return on assets (ROA), climate-related operational flexibility and ROA, and between the total number of adaptation measures and ROA.