Nonlinear directional coupler for polychromatic light

We demonstrate that nonlinear directional coupler with special bending of waveguide axes can be used for all-optical switching of polychromatic light with very broad spectrum covering all visible region. The bandwidth of suggested device is enhanced five times compared to conventional couplers. Our results suggest novel opportunities for creation of all-optical logical gates and switches for polychromatic light with white-light and super-continuum spectrum.Comment: 3 pages, 3 figure

Diffusion with restrictions

A non--linear diffusion equation is derived by taking into account hopping rates depending on the occupation of next neighbouring sites. There appears additonal repulsive and attractive forces leading to a changed local mobiltiy. The stationary and the time dependent behaviour of the system are studied based upon the master equation approach. Different to conventional diffusion it results a time dependent bump the position of which increases with time described by an anomalous diffusion exponent. The fractal dimension of this random walk is exclusively determined by the space dimension. The applicabilty of the model to descibe glasses is discussed.Comment: 1 figure, can be send on reques

Broadband Dielectric Spectroscopy on Glass-Forming Propylene Carbonate

Dielectric spectroscopy covering more than 18 decades of frequency has been performed on propylene carbonate in its liquid and supercooled-liquid state. Using quasi-optic submillimeter and far-infrared spectroscopy the dielectric response was investigated up to frequencies well into the microscopic regime. We discuss the alpha-process whose characteristic timescale is observed over 14 decades of frequency and the excess wing showing up at frequencies some three decades above the peak frequency. Special attention is given to the high-frequency response of the dielectric loss in the crossover regime between alpha-peak and boson-peak. Similar to our previous results in other glass forming materials we find evidence for additional processes in the crossover regime. However, significant differences concerning the spectral form at high frequencies are found. We compare our results to the susceptibilities obtained from light scattering and to the predictions of various models of the glass transition.Comment: 13 pages, 9 figures, submitted to Phys. Rev.

Predicting risk for nocturnal hypoglycemia after physical activity in children with type 1 diabetes

Children with type 1 diabetes (T1D) frequently have nocturnal hypoglycemia, daytime physical activity being the most important risk factor. The risk for late post-exercise hypoglycemia depends on various factors and is difficult to anticipate. The availability of continuous glucose monitoring (CGM) enabled the development of various machine learning approaches for nocturnal hypoglycemia prediction for different prediction horizons. Studies focusing on nocturnal hypoglycemia prediction in children are scarce, and none, to the best knowledge of the authors, investigate the effect of previous physical activity. The primary objective of this work was to assess the risk of hypoglycemia throughout the night (prediction horizon 9 h) associated with physical activity in children with T1D using data from a structured setting. Continuous glucose and physiological data from a sports day camp for children with T1D were input for logistic regression, random forest, and deep neural network models. Results were evaluated using the F2 score, adding more weight to misclassifications as false negatives. Data of 13 children (4 female, mean age 11.3 years) were analyzed. Nocturnal hypoglycemia occurred in 18 of a total included 66 nights. Random forest using only glucose data achieved a sensitivity of 71.1% and a specificity of 75.8% for nocturnal hypoglycemia prediction. Predicting the risk of nocturnal hypoglycemia for the upcoming night at bedtime is clinically highly relevant, as it allows appropriate actions to be taken—to lighten the burden for children with T1D and their families

Wearable Current-Based ECG Monitoring System with Non-Insulated Electrodes for Underwater Application

The second most common cause of diving fatalities is cardiovascular diseases. Monitoring the cardiovascular system in actual underwater conditions is necessary to gain insights into cardiac activity during immersion and to trigger preventive measures. We developed a wearable, current-based electrocardiogram (ECG) device in the eco-system of the FitnessSHIRT platform. It can be used for normal/dry ECG measuring purposes but is specifically designed to allow underwater signal acquisition without having to use insulated electrodes. Our design is based on a transimpedance amplifier circuit including active current feedback. We integrated additional cascaded filter components to counter noise characteristics specific to the immersed condition of such a system. The results of the evaluation show that our design is able to deliver high-quality ECG signals underwater with no interferences or loss of signal quality. To further evaluate the applicability of the system, we performed an applied study with it using 12 healthy subjects to examine whether differences in the heart rate variability exist between sitting and supine positions of the human body immersed in water and outside of it. We saw significant differences, for example, in the RMSSD and SDSD between sitting outside the water (36 ms) and sitting immersed in water (76 ms) and the pNN50 outside the water (6.4%) and immersed in water (18.2%). The power spectral density for the sitting positions in the TP and HF increased significantly during water immersion while the LF/HF decreased significantly. No significant changes were found for the supine position

Prevalence and course of pregnancy symptoms using self-reported pregnancy app symptom tracker data

AbstractDuring pregnancy, almost all women experience pregnancy-related symptoms. The relationship between symptoms and their association with pregnancy outcomes is not well understood. Many pregnancy apps allow pregnant women to track their symptoms. To date, the resulting data are primarily used from a commercial rather than a scientific perspective. In this work, we aim to examine symptom occurrence, course, and their correlation throughout pregnancy. Self-reported app data of a pregnancy symptom tracker is used. In this context, we present methods to handle noisy real-world app data from commercial applications to understand the trajectory of user and patient-reported data. We report real-world evidence from patient-reported outcomes that exceeds previous works: 1,549,186 tracked symptoms from 183,732 users of a smartphone pregnancy app symptom tracker are analyzed. The majority of users track symptoms on a single day. These data are generalizable to those users who use the tracker for at least 5 months. Week-by-week symptom report data are presented for each symptom. There are few or conflicting reports in the literature on the course of diarrhea, fatigue, headache, heartburn, and sleep problems. A peak in fatigue in the first trimester, a peak in headache reports around gestation week 15, and a steady increase in the reports of sleeping difficulty throughout pregnancy are found. Our work highlights the potential of secondary use of industry data. It reveals and clarifies several previously unknown or disputed symptom trajectories and relationships. Collaboration between academia and industry can help generate new scientific knowledge.</jats:p

Fusing actigraphy signals for outpatient monitoring

[EN] Actigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.This work was partially funded by the European Commission: Help4Mood (Contract No. FP7-ICT-2009-4: 248765). E. FusterGarcia acknowledges Programa Torres Quevedo from Ministerio de Educacion y Ciencia, co-founded by the European Social Fund (PTQ-12-05693).Fuster García, E.; Bresó Guardado, A.; Martínez Miranda, JC.; Rosell-Ferrer, J.; Matheson, C.; García Gómez, JM. (2015). Fusing actigraphy signals for outpatient monitoring. Information Fusion. 23:69-80. https://doi.org/10.1016/j.inffus.2014.08.003S69802

Interpreting Venous and Arterial Ulcer Images Through the Grad-CAM Lens: Insights and Implications in CNN-Based Wound Image Classification

Background/Objectives : Chronic wounds of the lower extremities, particularly arterial and venous ulcers, represent a significant and costly challenge in medical care. To assist in differential diagnosis, we aim to evaluate various advanced deep-learning models for classifying arterial and venous ulcers and visualize their decision-making processes. Methods : A retrospective dataset of 607 images (198 arterial and 409 venous ulcers) was used to train five convolutional neural networks: ResNet50, ResNeXt50, ConvNeXt, EfficientNetB2, and EfficientNetV2. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. Grad-CAM was applied to visualize image regions contributing to classification decisions. Results : The models demonstrated high classification performance, with accuracy ranging from 72% (ConvNeXt) to 98% (ResNeXt50). Precision and recall values indicated strong discrimination between arterial and venous ulcers, with EfficientNetV2 achieving the highest precision. Conclusions : AI-assisted classification of venous and arterial ulcers offers a valuable method for enhancing diagnostic efficiency.This research was funded by Bayerische Forschungsstiftung (AZ 1582-23).Bayerische Forschungsstiftun

Smartphone pregnancy apps: systematic analysis of features, scientific guidance, commercialization, and user perception

Background Over 50% of pregnant women use pregnancy applications (apps). Some app s lack credibility, information accuracy, and evidence-based clinical advice, containing potentially harmful functionality. Previous studies have only conducted a limited analysis of pregnancy app functionalities, expert involvement/evidence-based content, used commercialization techniques, and user perception. Methods We used the keyword “pregnancy” to scrape (automatically extract) apps and app information from Apple App Store and Google Play. Unique functionalities were derived from app descriptions and user reviews. App descriptions were screened for evidence-based content and expert involvement, and apps were subsequently analyzed in detail. Apps were opened and searched for used commercialization techniques, such as advertisements or affiliate marketing. Automated text analysis (natural language processing) was used on app reviews to assess users’ perception of evidence-based content/expert involvement and commercialization techniques. Results In total, 495 apps were scraped. 226 remained after applying exclusion criteria. Out of these, 36 represented 97%/88% of the total market share (Apple App Store/Google Play), and were thus considered for review. Overall, 49 distinct functionalities were identified, out of which 6 were previously unreported. Functionalities for fetal kick movement counting were found. All apps are commercial. Only 15 apps mention the involvement of medical experts. 10.3% of two-stars user reviews include commercial topics, and 0.6% of one-/two-/three-/five stars user reviews include references to scientific content accuracy. Conclusion Problematic features and inadequate advice continue to be present in pregnancy apps. App developers should adopt an evidence-based development approach and avoid implementing as many features as possible, potentially at the expense of their quality or over-complication (“feature creep”). Financial incentives, such as grant programs, could support adequate content quality. Caregivers play a key role in pregnant individuals’ decision-making, should be aware of potential dangers, and could guide them to trustworthy apps.Bundesministerium für Gesundheithttp://dx.doi.org/10.13039/501100003107Open Access Publication Funding, Friedrich-Alexander-Universität Erlangen-NürnbergDeutsche Forschungsgemeinschafthttp://dx.doi.org/10.13039/50110000165