Deep Learning for Uplink CSI-based Downlink Precoding in FDD massive MIMO Evaluated on Indoor Measurements

When operating massive multiple-input multiple-output (MIMO) systems with uplink (UL) and downlink (DL) channels at different frequencies (frequency division duplex (FDD) operation), acquisition of channel state information (CSI) for downlink precoding is a major challenge. Since, barring transceiver impairments, both UL and DL CSI are determined by the physical environment surrounding transmitter and receiver, it stands to reason that, for a static environment, a mapping from UL CSI to DL CSI may exist. First, we propose to use various neural network (NN)-based approaches that learn this mapping and provide baselines using classical signal processing. Second, we introduce a scheme to evaluate the performance and quality of generalization of all approaches, distinguishing between known and previously unseen physical locations. Third, we evaluate all approaches on a real-world indoor dataset collected with a 32-antenna channel sounder

Deep Learning Based Adaptive Joint mmWave Beam Alignment

The challenging propagation environment, combined with the hardware limitations of mmWave systems, gives rise to the need for accurate initial access beam alignment strategies with low latency and high achievable beamforming gain. Much of the recent work in this area either focuses on one-sided beam alignment, or, joint beam alignment methods where both sides of the link perform a sequence of fixed channel probing steps. Codebook-based non-adaptive beam alignment schemes have the potential to allow multiple user equipment (UE) to perform initial access beam alignment in parallel whereas adaptive schemes are favourable in achievable beamforming gain. This work introduces a novel deep learning based joint beam alignment scheme that aims to combine the benefits of adaptive, codebook-free beam alignment at the UE side with the advantages of a codebook-sweep based scheme at the base station. The proposed end-to-end trainable scheme is compatible with current cellular standard signaling and can be readily integrated into the standard without requiring significant changes to it. Extensive simulations demonstrate superior performance of the proposed approach over purely codebook-based ones

Proof of concept: Predicting distress in cancer patients using back propagation neural network (BPNN)

Background: Research findings suggest that a significant proportion of individuals diagnosed with cancer, ranging from 25% to 60%, experience distress and require access to psycho-oncological services. Until now, only contemporary approaches, such as logistic regression, have been used to determine predictors of distress in oncological patients. To improve individual prediction accuracy, novel approaches are required. We aimed to establish a prediction model for distress in cancer patients based on a back propagation neural network (BPNN). Methods: Retrospective data was gathered from a cohort of 3063 oncological patients who received diagnoses and treatment spanning the years 2011-2019. The distress thermometer (DT) has been used as screening instrument. Potential predictors of distress were identified using logistic regression. Subsequently, a prediction model for distress was developed using BPNN. Results: Logistic regression identified 13 significant independent variables as predictors of distress, including emotional, physical and practical problems. Through repetitive data simulation processes, it was determined that a 3-layer BPNN with 8 neurons in the hidden layer demonstrates the highest level of accuracy as a prediction model. This model exhibits a sensitivity of 79.0%, specificity of 71.8%, positive predictive value of 78.9%, negative predictive value of 71.9%, and an overall coincidence rate of 75.9%. Conclusion: The final BPNN model serves as a compelling proof of concept for leveraging artificial intelligence in predicting distress and its associated risk factors in cancer patients. The final model exhibits a remarkable level of discrimination and feasibility, underscoring its potential for identifying patients vulnerable to distress

Land use and soil development in Southern chile: Effects on physical properties

Different physical properties of volcanic ash soils were investigated along a transect of 120 km from the western slope of the Central Cordilleras (40°20’S, 72°06’W) to the eastern slope of the Costal Cordillera (39°39’S, 73°11’W) in southern Chile with respect to the degree of soil development (Arenosol versus Andosol stage; Arenosol: young volcanic ash soil, free of clay, tephric properties, Andosol: older volcanic soil, clayey). The Andosols show a higher total pore volume and a higher field capacity, especially due to an increase in fine pores, than the Arenosols. Furthermore, the precompression stress (Pc) as a parameter for the mechanical soil strength is higher for Andosols despite of a lower bulk density. A land use (cropland, meadow, forest) dependent variation of the investigated parameters was less distinct for Andosols. A reduction of macropores and saturated hydraulic conductivity (ks) due to agriculture could be determined in the field, but in general the values are still on a high level with ks-values >100 cm d-1. However, at higher stresses using an oedometer test the ks-values of the Andosols are highly negatively affected with values <10 cm d-1. Aggregation is of major importance for soil stability of Andosols, whereas a homogenization of soil structure will lead to a distinct decrease of Pc of approx. 50%.DFG/HO 911/45-1DFG/BA 1359/12-

Reading Wishes from the Lips: Cancer Patients’ Need for Psycho-Oncological Support during Inpatient and Outpatient Treatment

Background: Psycho-oncological support (PO) is an effective measure to reduce distress and improve the quality of life in patients with cancer. Currently, there are only a few studies investigating the (expressed) wish for PO. The aim of this study was to evaluate the number of patients who request PO and to identify predictors for the wish for PO. Methods: Data from 3063 cancer patients who had been diagnosed and treated at a Comprehensive Cancer Center between 2011 and 2019 were analyzed retrospectively. Potential predictors for the wish for PO were identified using logistic regression. As a novelty, a Back Propagation Neural Network (BPNN) was applied to establish a prediction model for the wish for PO. Results: In total, 1752 patients (57.19%) had a distress score above the cut-off and 14.59% expressed the wish for PO. Patients’ requests for pastoral care (OR = 13.1) and social services support (OR = 5.4) were the strongest predictors of the wish for PO. Patients of the female sex or who had a current psychiatric diagnosis, opioid treatment and malignant neoplasms of the skin and the hematopoietic system also predicted the wish for PO, while malignant neoplasms of digestive organs and older age negatively predicted the wish for PO. These nine significant predictors were used as input variables for the BPNN model. BPNN computations indicated that a three-layer network with eight neurons in the hidden layer is the most precise prediction model. Discussion: Our results suggest that the identification of predictors for the wish for PO might foster PO referrals and help cancer patients reduce barriers to expressing their wish for PO. Furthermore, the final BPNN prediction model demonstrates a high level of discrimination and might be easily implemented in the hospital information system

High-resolution nerve ultrasound abnormalities in POEMS syndrome: a comparative study

Background: High-resolution nerve ultrasound (HRUS) has been proven to be a valuable tool in the diagnosis of immune-mediated neuropathies, such as chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, M-protein, skin changes) is an important differential diagnosis of CIDP. Until now, there have been no studies that could identify specific HRUS abnormalities in POEMS syndrome patients. Thus, the aim of this study was to assess possible changes and compare findings with CIDP patients. Methods: We retrospectively analyzed HRUS findings in three POEMS syndrome and ten CIDP patients by evaluating cross-sectional nerve area (CSA), echogenicity and additionally calculating ultrasound pattern scores (UPSA, UPSB, UPSC and UPSS) and homogeneity scores (HS). Results: CIDP patients showed greater CSA enlargement and higher UPSS (median 14 vs. 11), UPSA (median 11.5 vs. 8) and HS (median 5 vs. 3) compared with POEMS syndrome patients. However, every POEMS syndrome patient illustrated enlarged nerves exceeding reference values, which were not restricted to entrapment sites. In CIDP and POEMS syndrome, heterogeneous enlargement patterns could be identified, such as inhomogeneous, homogeneous and regional nerve enlargement. HRUS in CIDP patients visualized both increased and decreased echointensity, while POEMS syndrome patients pictured hypoechoic nerves with hyperechoic intraneural connective tissue. Discussion: This is the first study to demonstrate HRUS abnormalities in POEMS syndrome outside of common entrapment sites. Although nerve enlargement was more prominent in CIDP, POEMS syndrome patients revealed distinct echogenicity patterns, which might aid in its differentiation from CIDP. Future studies should consider HRUS and its possible role in determining diagnosis, prognosis and treatment response in POEMS syndrome

Fear of COVID-19 Predicts Depression, Anxiety and Post-Traumatic Stress Disorders in Patients with Implantable Cardioverter Defibrillators and Is Mediated by Positive and Negative Affects—A Cross-Sectional Study

The COVID-19 pandemic affected both the physical and mental health of the general population. People with cardiac diseases seem to be particularly vulnerable to the implications of the pandemic. However, studies on the mental health impact of the COVID-19 pandemic on people with implantable cardioverter defibrillator (ICDs) are lacking. Thus, we aimed to explore the level of fear of COVID-19 and the prevalence of anxiety, depression and post-traumatic stress disorder (PTSD) in ICD patients. Furthermore, we aimed to identify novel predictors for anxiety, depression and PTSD, including COVID-19-related variables, and to assess whether positive affects (PAs) and negative affects (NAs) mediate the relationship between the level of fear of COVID-19 and anxiety, depression and PTSD, respectively. The data of 363 patients with ICDS who had been prospectively included in this study between 2020 and 2023, were analyzed. Potential predictors for anxiety, depression, and PTSD were identified using logistic regression. To identify indirect mediating effects of PAs and NAs, we applied the PROCESS regression path analysis modeling tool. The prevalence of anxiety was 9.19%, of depression 10.85%, and of PTSD 12.99%. Being unemployed was the strongest predictor for anxiety (OR = 10.39) and depression (OR = 6.54). Younger age predicted anxiety (OR = 0.95) and PTSD (OR = 0.92). Receiving low social support was associated with anxiety (OR = 0.91), depression (OR = 0.88) and PTSD (OR = 0.91). Patients with a history of COVID-19 (OR = 3.58) and those who did not feel well-informed about COVID-19 (OR = 0.29) were more likely to be depressed. Higher levels of fear of COVID-19 predicted anxiety (OR = 1.10), depression (OR = 1.12) and PTSD (OR = 1.14). The relationship between fear of COVID-19 and anxiety or depression was fully mediated by PAs and NAs, while NAs partially mediated the relationship between fear of COVID-19 and PTSD. Vulnerable subgroups of ICD patients may need additional psychological and educational interventions due to fear of COVID-19, anxiety, depression and PTSD during the pandemic

Mapping the spatial distribution of NO2 with in situ and remote sensing instruments during the Munich NO2 imaging campaign

We present results from the Munich Nitrogen dioxide (NO2) Imaging Campaign (MuNIC), where NO2 near-surface concentrations (NSCs) and vertical column densities (VCDs) were measured with stationary, mobile, and airborne in situ and remote sensing instruments in Munich, Germany. The most intensive day of the campaign was 7 July 2016, when the NO2 VCD field was mapped with the Airborne Prism Experiment (APEX) imaging spectrometer. The spatial distribution of APEX VCDs was rather smooth, with a horizontal gradient between lower values upwind and higher values downwind of the city center. The NO2 map had no pronounced source signatures except for the plumes of two combined heat and power (CHP) plants. The APEX VCDs have a fair correlation with mobile multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations from two vehicles conducted on the same afternoon (r=0.55). In contrast to the VCDs, mobile NSC measurements revealed high spatial and temporal variability along the roads, with the highest values in congested areas and tunnels. The NOx emissions of the two CHP plants were estimated from the APEX observations using a mass-balance approach. The NOx emission estimates are consistent with CO2 emissions determined from two ground-based Fourier transform infrared (FTIR) instruments operated near one CHP plant. The estimates are higher than the reported emissions but are probably overestimated because the uncertainties are large, as conditions were unstable and convective with low and highly variable wind speeds. Under such conditions, the application of mass-balance approaches is problematic because they assume steady-state conditions. We conclude that airborne imaging spectrometers are well suited for mapping the spatial distribution of NO2 VCDs over large areas. The emission plumes of point sources can be detected in the APEX observations, but accurate flow fields are essential for estimating emissions with sufficient accuracy. The application of airborne imaging spectrometers for studying NSCs is less straightforward and requires us to account for the non-trivial relationship between VCDs and NSCs

High-Energy Molecular-Frame Photoelectron Angular Distributions: A Molecular Bond-Length Ruler

We present an experimental and theoretical study of core-level ionization of small hetero- and homo-nuclear molecules employing circularly polarized light and address molecular-frame photoelectron angular distributions in the light's polarization plane (CP-MFPADs). We find that the main forward-scattering peaks of CP-MFPADs are slightly tilted with respect to the molecular axis. We show that this tilt angle can be directly connected to the molecular bond length by a simple, universal formula. The extraction of the bond length becomes more accurate as the photoelectron energy is increased. We apply the derived formula to several examples of CP-MFPADs of C 1s and O 1s photoelectrons of CO, which have been measured experimentally or obtained by means of ab initio modeling. The photoelectron kinetic energies range from 70 to 1000~eV and the extracted bond lengths agree well with the known bond length of the CO molecule in its ground state. In addition, we discuss the influence of the back-scattering contribution that is superimposed over the analyzed forward-scattering peak in case of homo-nuclear diatomic molecules as N$_2$