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Context: In the framework of studying cosmic microwave background polarization and characterizing its Galactic foregrounds, the angular power spectrum analysis of the thermal dust polarization map has led to intriguing evidence of an E/B asymmetry and a positive TE correlation. The interpretation of these observations is the subject of theoretical and simulation-driven studies in which the correlation between the density structure of the interstellar medium (ISM) and the magnetic field appears to be a key aspect. In this context, and when the magnetized ISM structures are modeled in three dimensions, dust clouds are generally considered to be filamentary structures only, but both filamentary and sheet-like shapes are supported by observational and theoretical evidence.
Aims: We aim to study the influence of the cloud shape and its connection to the local magnetic field, as well as the influence from the viewing angle, on the angular power spectra measured on thermal dust polarization maps; we specifically focus on the dependence of the E/B power asymmetry and TE correlation.
Methods: To this end, we simulated realistic interstellar clouds with both filament-like and sheet-like shapes using the software ASTERION, which also allowed us to generate synthetic maps of thermal dust polarized emission with an area of 400 square degrees. Then, we computed their polarization power spectra in the multipole range ℓ ϵ [100, 500] and focused on the E/B power asymmetry, quantified through the ℛEB ratio, and the correlation coefficient rTE between Τ and Ε modes. We quantified the dependence of ℛEB and rTE values on the offset angle (between the longest cloud axis and local magnetic field lines) and inclination angle (between the line of sight and the magnetic field) for both types of cloud shapes, either embedded in a regular magnetic field or coupled to a nonregular field to mimic turbulence.
Results: We find that both types of cloud shapes cover the same regions of the (ℛEB, rTE) parameter space. The dependence on the inclination and offset angles is similar for both shapes, although sheet-like structures generally show larger scatter than filamentary structures. In addition to the known dependence on the offset angle, we find a strong dependence of ℛEB and rTE on the inclination angle.
Conclusions: The very fact that filament-like and sheet-like structures may lead to polarization power spectra with similar (ℛEB,rTE) values complicates their interpretation. We argue that interpreting them solely in terms of filament characteristics is risky, and in future analyses, this degeneracy should be accounted for, as should the connection to the magnetic field geometry. Our results based on maps of 400 square degrees clarify that the overall geometrical arrangement of the magnetized ISM surrounding the observer leaves its marks on polarization power spectra.
The purpose of this article is to evaluate optimal expected utility risk measures (OEU) in a risk-constrained portfolio optimization context where the expected portfolio return is maximized. We compare the portfolio optimization with OEU constraint to a portfolio selection model using value at risk as constraint. The former is a coherent risk measure for utility functions with constant relative risk aversion and allows individual specifications to the investor’s risk attitude and time preference. In a case study with three indices, we investigate how these theoretical differences influence the performance of the portfolio selection strategies. A copula approach with univariate ARMA-GARCH models is used in a rolling forecast to simulate monthly future returns and calculate the derived measures for the optimization. The results of this study illustrate that both optimization strategies perform considerably better than an equally weighted portfolio and a buy and hold portfolio. Moreover, our results illustrate that portfolio optimization with OEU constraint experiences individualized effects, e.g., less risk-averse investors lose more portfolio value in the financial crises but outperform their more risk-averse counterparts in bull markets.
Water is crucial for socio-economic development and healthy ecosystems. With the actual population growth and in view of future water scarcity, development calls for improved sectorial allocation of groundwater and surface water for domestic, agricultural and industrial use. Instead of intensifying the pressure on water resources, leading to conflicts among users and excessive pressure on the environment, sewage effluents, after pre-treatment, provide an alternative nutrient-rich water source for agriculture in the vicinity of cities. Water scarcity often occurs in arid and semiarid regions affected by droughts and large climate variability and where the choice of crop to be grown is limited by the environmental factors. Jatropha has been introduced as a potential renewable energy resource since it is claimed to be drought resistant and can be grown on marginal sites. Sewage effluents provide a source for water and nutrients for cultivating jatropha, a combined plant production/effluent treatment system. Nevertheless, use of sewage effluents for irrigation in arid climates carries the risk of salinization. Thus, potential irrigation with sewage effluents needs to consider both the water requirement of the crop and those needed for controlling salinity build-up in the top soil. Using data from a case study in Southern Morocco, irrigation requirements were calculated using CROPWAT 8.0. We present here crop evapotranspiration during the growing period, required irrigation, the resulting nutrient input and the related risk of salinization from the irrigation of jatropha with sewage effluent.
Background: In recent years, the volume of medical knowledge and health data has increased rapidly. For example, the increased availability of electronic health records (EHRs) provides accurate, up-to-date, and complete information about patients at the point of care and enables medical staff to have quick access to patient records for more coordinated and efficient care. With this increase in knowledge, the complexity of accurate, evidence-based medicine tends to grow all the time. Health care workers must deal with an increasing amount of data and documentation. Meanwhile, relevant patient data are frequently overshadowed by a layer of less relevant data, causing medical staff to often miss important values or abnormal trends and their importance to the progression of the patient’s case.
Objective: The goal of this work is to analyze the current laboratory results for patients in the intensive care unit (ICU) and classify which of these lab values could be abnormal the next time the test is done. Detecting near-future abnormalities can be useful to support clinicians in their decision-making process in the ICU by drawing their attention to the important values and focus on future lab testing, saving them both time and money. Additionally, it will give doctors more time to spend with patients, rather than skimming through a long list of lab values.
Methods: We used Structured Query Language to extract 25 lab values for mechanically ventilated patients in the ICU from the MIMIC-III and eICU data sets. Additionally, we applied time-windowed sampling and holding, and a support vector machine to fill in the missing values in the sparse time series, as well as the Tukey range to detect and delete anomalies. Then, we used the data to train 4 deep learning models for time series classification, as well as a gradient boosting–based algorithm and compared their performance on both data sets.
Results: The models tested in this work (deep neural networks and gradient boosting), combined with the preprocessing pipeline, achieved an accuracy of at least 80% on the multilabel classification task. Moreover, the model based on the multiple convolutional neural network outperformed the other algorithms on both data sets, with the accuracy exceeding 89%.
Conclusions: In this work, we show that using machine learning and deep neural networks to predict near-future abnormalities in lab values can achieve satisfactory results. Our system was trained, validated, and tested on 2 well-known data sets to ensure that our system bridged the reality gap as much as possible. Finally, the model can be used in combination with our preprocessing pipeline on real-life EHRs to improve patients’ diagnosis and treatment.
For a detailed discussion of process mining, the objective of this paper is the analysis of the successful implementation of process mining in the practical fields of supply chain management. The research comprises the investigation of use cases in companies that are already actively using process mining.
Purpose: This research aims to highlight the applicability of process mining in the supply chain management business field.
Research Methodology: In order to examine the applicability of process mining in supply chain management a research study was conducted among experts in this business field. Further, theoretical findings were compared to the results and evaluated.
Results: Process Mining can be applied very well in the SCM area. The advantages that arise primarily reflect significant potential benefits and improved process throughput times. The information that can be gained from the operational areas supported by process mining is suitable for reliable decisions, both in the tactical and strategic areas.
Limitations: The results on the application of process mining show a certain generalization and have to be adapted and adjusted to the respective application case.
Contribution: This study is useful, especially for the purchasing and logistics business area.
Background: Stratified care is an up-to-date treatment approach suggested for patients with back pain in several guidelines. A comprehensively studied stratification instrument is the STarT Back Tool (SBT). It was developed to stratify patients with back pain into three subgroups, according to their risk of persistent disabling symptoms. The primary aim was to analyse the disability differences in patients with back pain 12 months after inclusion according to the subgroups determined at baseline using the German version of the SBT (STarT-G). Moreover, the potential to improve prognosis for disability by adding further predictor variables, an analysis for differences in pain intensity according to the STarT-Classification, and discriminative ability were investigated.
Methods: Data from the control group of a randomized controlled trial were analysed. Trial participants were members of a private medical insurance with a minimum age of 18 and indicated as having persistent back pain. Measurements were made for the risk of back pain chronification using the STarT-G, disability (as primary outcome) and back pain intensity with the Chronic Pain Grade Scale (CPGS), health-related quality of life with the SF-12, psychological distress with the Patient Health Questionnaire-4 (PHQ-4) and physical activity. Analysis of variance (ANOVA), multiple linear regression, and area under the curve (AUC) analysis were conducted.
Results: The mean age of the 294 participants was 53.5 (SD 8.7) years, and 38% were female. The ANOVA for disability and pain showed significant differences (p < 0.01) among the risk groups at 12 months. Post hoc Tukey tests revealed significant differences among all three risk groups for every comparison for both outcomes. AUC for STarT-G’s ability to discriminate reference standard ‘cases’ for chronic pain status at 12 months was 0.79. A prognostic model including the STarT-Classification, the variables global health, and disability at baseline explained 45% of the variance in disability at 12 months.
Conclusions: Disability differences in patients with back pain after a period of 12 months are in accordance with the subgroups determined using the STarT-G at baseline. Results should be confirmed in a study developed with the primary aim to investigate those differences.
For the assessment of human reaction time, a test environment was developed. This system consists of an embedded device with organic light-emitting diode (OLED) displays with push buttons for the combined presentation of visual stimulation and registration of the haptic human reaction. The test leader can define the test sequence with the aid of a graphical user interface (GUI) on a personal computer (PC). The validation of the system was proved by measuring the latency times of the whole system, which are conditioned by the specific hard- and software constellation. Through the investigation of the display’s light radiation by a photodiode and the recorded current consumption, latency times and their variance were specified. In the fastest mode the system can reach an error limit of 60 μs.
Radar systems for contactless vital sign monitoring are well known and an actual object of research. These radar-based sensors could be used for monitoring of elderly people in their homes but also for detecting the activity of prisoners and to control electrical devices (light, audio, etc.) in smart living environments. Mostly these sensors are foreseen to be mounted on the ceiling in the middle of a room. In retirement homes the rooms are mostly rectangular and of standardized size. Furniture like beds and seating are found at the borders or the corners of the room. As the propagation path from the center of the room ceiling to the borders and corners of a room is 1.4 and 1.7 time longer the power reflected by people located there is 6 or even 10 dB lower than if located in the center of the room. Furthermore classical antennas in microstrip technology are strengthening radiation in broadside direction. Radar systems with only one single planar antenna must be mounted horizontally aligned when measuring in all directions. Thus an antenna pattern which is increasing radiation in the room corners and borders for compensation of free space loss is needed. In this contribution a specification of classical room sizes in retirement homes are given. A method for shaping the antenna gain in the E-plane by an one-dimensional series-fed traveling wave patch array and in the H-plane by an antenna feeding network for improvement of people detection in the room borders and corners is presented for a 24 GHz digital beamforming (DBF) radar system. The feeding network is a parallel-fed power divider for microstrip patch antennas at 24 GHz. Both approaches are explained in theory. The design parameters and the layout of the antennas are given. The simulation of the antenna arrays are executed with CST MWS. Simulations and measurements of the proposed antennas are compared to each other. Both antennas are used for the transmit and the receive channel either. The sensor topology of the radar system is explained. Furthermore the measurement results of the protoype are presented and discussed.
Radar target simulator with complex-valued delay line modeling based on standard radar components
(2018)
With increasing radar activities in the automotive, industrial and private sector, there is a need to test radar sensors in their environment. A radar target simulator can help testing radar systems repeatably. In this paper, the authors present a concept of low-cost hardware for radar target simulation. The theoretical foundations are derived and analyzed. An implementation of a demonstrator operating in the 24 GHz ISM band is shown for which the dynamical range simulation was implemented in a FPGA with fast sampling ADCs and DACs. By using a FIR filtering approach a fine discretization of the range could be reached which will furthermore allow an inherent and automatic Doppler simulation by moving the target.
Reasons and potential solution approaches for the shortage of nursing staff in German hospitals
(2021)
The aim of this scientific paper was to find out the reasons for the shortage of nursing staff in German hospitals and to provide potential solution approaches for this shortage. Over the last years, the shortage of nursing staff has become a more and more important topic in the news: Not only due to the increasing amount of missing nurses, but also due to the ageing population in Germany, which leads to an increasing amount of patients in German hospitals. To reach this aim two surveys were done, of which one was for nursing staff only and the other one was for people from all occupational groups with the intention of creating comparative values. The surveys were done from March to April 2019 and were analysed afterwards. After a detailed analysis of the survey results, it can be summarized that the reasons for the shortage of nursing staff in German hospitals are very diverse: Starting with a weak salary, improvable working conditions – for example the shift work and the high amount of physical and psychological stress -, a difficult compatibility of family and job as well as the unattractive image of the job as a nurse in the society. It can be concluded that the solution for the shortage of nursing staff is very difficult. The future will show whether the governmental support will help to make the job as a nurse more attractive – not only for the current nurses, but also for potential future nurses.