Miracle-dz/UniFAR-FTUs huggingface dataset:
Ambulante Antibiotika-Verordnungsrate und Mastoiditis-Fälle bei Kindern und Jugendlichen im Saarland, 2014–2019

Zusammenfassung Hintergrund Infektionen der Atemwege sind die Hauptindikation für eine ambulante Antibiotikatherapie im Kindes- und Jugendalter. In den letzten Jahren ist die Antibiotika-Verordnungsrate (AVR) in der pädiatrischen Bevölkerung im Saarland deutlich zurückgegangen. Ziele und Fragestellung Ziel des retrospektiven Mastoiditis-Audits im PaedineSaar Netzwerk ist es, die Inzidenz der stationären akuten Mastoiditis (AM) im Saarland (2014–2019) im Hinblick auf die sinkende AVR bei Kindern zu untersuchen sowie Daten zum klinischen Verlauf der AM zu erheben. Methoden Es wurden alle stationären AM-Fälle (2014–2019) aus 6 Kliniken für Kinderheilkunde und/oder HNO-Heilkunde im Saarland und Trier retrospektiv ausgewertet. Patienten mit AM im Alter von 0–17 Jahren mit Wohnsitz im Saarland wurden in die Studie eingeschlossen. Ergebnisse Zwischen 2014 und 2019 wurden 53 stationär behandelte AM-Fälle erfasst. Während des Studienzeitraums gab es keinen signifikanten Anstieg der AM-Inzidenz (mittlere Inzidenz 2014–2019 6,1/100 000). 34% (18/53) der Patienten erhielten prästationär eine orale antibiotische Therapie. Mindestens eine Komplikation trat bei 30% der Patienten (16/53) auf. Es zeigte sich ein leichter Trend zu mehr Komplikationen bei Kindern ohne orale Antibiotikabehandlung vor der stationären Aufnahme (14/35 (40%) vs. 2/18 (11%) P=0,056). Diskussion Die Inzidenz der stationär behandelten AM im Saarland ist 2014–2019 trotz eines signifikanten Rückgangs der ambulanten AVR nicht gestiegen. Die Ergebnisse dieses Audits sollen zur Entwicklung eines standardisierten Vorgehens bei der Diagnostik und Behandlung von Kindern mit AM dienen.
Kinematics Research on PRP Industrial Manipulator with Cylindrical Coordinate

Abstract: This paper mainly studies the problem of the movement of PRP cylindrical coordinate industrial manipulator. Based on the description of the manipulator position and gesture, the determination of coordinate of the rectilinear joint and rotary joint and the homogeneous coordinate transformation, the kinematical equation and its inverse solution can be deduced subsequently. It provides the theoretical basis for the following study.
APPLICATION OF ACOUSTIC EMISSION IN OPTIMIZING THE DESIGN OF NEW GENERATION CASTINGS OF HIGH-VOLTAGE ELECTRIC DEVICES

The technological progress in power systems requires the application of new solutions that will meet higher specifications than the currently used, in all parts of equipment design. This also refers to the external castings of power devices, especially those operating over a long time under high-pressure SF6 dielectric gas. Increased requirements include a 20% increase of the SF6 operating pressure and the same increase of the destructive test pressure, and simultaneous reduction of the casting weight. In this paper we present test results of casting materials and strength calculations for the implemented changes in design. The optimization and design verification activities included acoustic emission (AE) measurements performed during a hydraulic pressure test. The results obtained allowed to determine the locations and pressure values, at which the plastic strain began to occur, and the position of the final failure.
Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.
Neurofeedback Training With an Electroencephalogram-Based Brain-Computer Interface Enhances Emotion Regulation

Emotion regulation plays a vital role in human beings daily lives by helping them deal with social problems and protects mental and physical health. However, objective evaluation of the efficacy of emotion regulation and assessment of the improvement in emotion regulation ability at the individual level remain challenging. In this study, we leveraged neurofeedback training to design a real-time EEG-based brain-computer interface (BCI) system for users to effectively regulate their emotions. Twenty healthy subjects performed 10 BCI-based neurofeedback training sessions to regulate their emotion towards a specific emotional state (positive, negative, or neutral), while their EEG signals were analyzed in real time via machine learning to predict their emotional states. The prediction results were presented as feedback on the screen to inform the subjects of their immediate emotional state, based on which the subjects could update their strategies for emotion regulation. The experimental results indicated that the subjects improved their ability to regulate these emotions through our BCI neurofeedback training. Further EEG-based spectrum analysis revealed how each emotional state was related to specific EEG patterns, which were progressively enhanced through long-term training. These results together suggested that long-term EEG-based neurofeedback training could be a promising tool for helping people with emotional or mental disorders.
Collaborative Planning in an Uncollaborative World

The purpose of this article is to expose the concept of collaborative planning to the reality of planning, thereby assessing its efficacy for informing and explaining what planners `really' do and can do. In this systematic appraisal, we begin by disaggregating collaborative planning into four elements that can enlighten such conceptual frameworks: ontology, epistemology, ideology and methodology. These four lenses help delimit and clarify the object of our examination and provide transparent criteria that guide our examination of collaborative planning's strengths and weaknesses. The second part of this article comprises an empirical investigation of planning processes in Northern Ireland, ranging from region-wide to local and from statutory to visionary. Planning efforts in this province make suitable test cases because special care has been invested in participatory deliberation processes to compensate for the democratic deficits in its mainstream political system. Such efforts have sought to ensure a maximally inclusive planning process. And indeed, the consultation process leading to the Regional Development Strategy, for example, has earned plaudits from leading exponents of collaborative planning. The final analysis provides a systematic gauge of collaborative planning in light of our empirical evidence, deploying the four conceptual dimensions introduced in the first part. This exposes a range of problems not only with the concept itself but also regarding its affinity with the uncollaborative world within which it has to operate. The former shed light on those aspects where collaborative planning as a conceptual tool for practitioners needs to be renovated, while the latter highlight inconsistencies in a political framework that struggles to accommodate both global competitiveness and local democratic collaboration.
Source Localization of EM Waves in the Near-Field of Spherical Antenna Array in the Presence of Unknown Mutual Coupling

To obtain an antenna array with isotropic radiation, spherical antenna array (SAA) is the right array configuration. The challenges of locating signals transmitted within the proximity of antenna array have been investigated considerably in the literature. However, near-field (NF) source localization of signals has hitherto not been investigated effectively using SAA in the presence of mutual coupling (MC). MC is another critical problem in antenna arrays. This paper presents an NF range and direction-of-arrival (DoA) estimation technique via the direction-independent and signal invariant spherical harmonics (SH) characteristics in the presence of mutual coupling. The energy of electromagnetic (EM) signal on the surface of SAA is captured successfully using a proposed pressure interpolation approach. The DoA estimation within the NF region is then calculated via the distribution of pressure. The direction-independent and signal invariant characteristics, which are SH features, are obtained using the DoA estimates in the NF region. We equally proposed a learning scheme that uses the source activity detection and convolutional neural network (CNN) to estimate the range of the NF source via the direction-independent and signal invariant features. Considering the MC problem and using the DoA estimates, an accurate spectrum peak in the multipath situation in conjunction with MC and a sharper spectrum peak from a unique MC structure and smoothing algorithms are obtained. For ground truth performance evaluation of the SH features within the context of NF localization, a numerical experiment is conducted and measured data were used for analysis to incorporate the MC and consequently computed the root mean square error (RMSE) of the source range and NF DoA estimate. The results obtained from numerical experiments and measured data indicate the validity and effectiveness of the proposed approach. In addition, these results are motivating enough for the deployment of the proposed method in practical applications.
Routing differentiated reliable connections in single- and multifiber WDM optical networks

Wavelength division multiplexing (WDM) networks are matured to provide, scalable data centric infrastructure, capable of delivering flexible, value added, high speed and high bandwidth services directly from the optical (WDM) layer. Different applications/end users need different levels of fault tolerance and differ in how much they are willing to pay for the service they get. The current optical networks are capable of providing either full protection in presence of single failure or no protection at all. So, there is a need for a way of providing the requested level of fault tolerance (reliability) to different applications/end users. We choose the reliability of a connection as a parameter to denote the different levels of fault tolerance. In this paper, we consider the problem of dynamically establishing reliable connections (R-connections) in wavelength routed WDM optical networks. We develop an efficient algorithm to select routes and wavelengths to establish an R-connection with a specified reliability guarantee, in a resource efficient manner, using primary-backup approach. In our scheme, we provide partial backup lightpaths for varying lengths of the primary lightpath to enhance the reliability of the connection. The length of the primary lightpath for which the backup lightpath is provided depends on the reliability required by the application/end user but not on the actual length of the primary lightpath, network topology, and design constraints. We present the initial experimental results which suggest that our scheme is attractive enough in terms of resource utilization and average call acceptance ratio.
Language Education in Emergencies: A Systematic Review

In response to the Covid-19 pandemic, education systems around the globe suspended on-site classes and transitioned instruction to various remote environments, creating a distinctive context for teaching and learning. This systematic review brings together the available research in language education within the current emergency setting to examine the state of affairs, as well as the situation’s inherent challenges and opportunities for language learners and teachers. A total of 38 studies were collected to reflect the current trend, with 16 of these in-depth reviewed. Research focusing on learners was dominant. Most research was conducted at the tertiary level. The studies highlighted digital tools that are capable of engaging language learners in an interactive learning experience, though they are unable to replace face-to-face instruction. Outside-of-class support such as extra channels of communication, self-access language learning (SALL) materials, and advising in language learning (ALL) were all found to complement remote learning. It is recommended that teachers try to retain their teaching principles and put them into practice regardless of the abrupt transition. Teachers’ wellbeing can be promoted when teachers accept the changes and see them as opportunities.
Mitigation of Leakage and Stationary Clutters in Short-Range FMCW Radar With Hybrid Analog and Digital Compensation Technique

In short-range sensing, the frequency-modulated continuous-wave (FMCW) radar is subject to the leakage between the transmitter (Tx) and the receiver (Rx) and the stationary clutter reflections, which are difficult to deal with due to the proximity to the target. The proximity ghost image may not only interfere with the target but also pose limitations on the radar performance, such as the degradation of the signal-to-noise ratio (SNR). This article proposes a novel hybrid analog and digital compensation techniques, which mitigates the leakage and the stationary clutters by compensating with the prestored anti-interference signals in both analog and digital domains at the intermediate-frequency (IF) stage. A novel modulation signal-based synchronization (MBS) technique is developed to align the real-time IF signal with the anti-interference signal during the compensation process. By significantly suppressing both the Tx–Rx leakage and the stationary clutters, the proposed technique greatly improves the signal-to-interference ratio (SIR) of the IF beat signals and makes possible the detection of targets with weak reflections. Moreover, the analog compensation enables the full use of the dynamic range of the analog-to-digital converter (ADC), which will reduce the impact of quantization noise and, thus, improve the SNR. Experiments have been carried out to validate the performance of the proposed technique in leakage compensation, weak target detection, stationary clutter compensation, and gesture sensing. The experimental results show that the SIR and the SNR have been improved by around 38 and 10 dB, respectively.
Network security data visualization

In the past year of 2020, life in the post-epidemic era continues to move towards cyberspace. The surge in magnitude and value of data has also brought more data abuse, attacks and thefts, and data security issues have become particularly prominent. Moreover, in recent years, data attacks have become more subtle and complex, making traditional security protection methods stretched. Therefore, in view of the data security threats under the new situation, it has become urgent to study new security protection methods. Due to the intuitive sensory advantages of visual analysis, security visual analysis is gradually becoming an effective means to guarantee data security and gradually arouses people's attention. As a cross-cutting research field, network security data visualization can effectively improve the perception and understanding of network security experts in the process of analyzing network security issues by providing information and interactive visualization methods. First, we review the methods of network security data visualization and analyze the basic methods and processes of visualization after investigating the current research status of network security data visualization. Then we introduce data visualization techniques suitable for network security and analyze the existing problems. Finally, we make a conclusion for future trends.
Harnessing the Power of Sensors and Machine Learning to Design Smart Fence to Protect Farmlands

Agriculture and animals are two crucial factors for ecological balance. Human–wildlife conflict is increasing day-by-day due to crop damage and livestock depredation by wild animals, causing local farmer’s economic loss resulting in the deepening of poverty. Techniques are needed to stop the crop damage caused by animals. The most prominent technique used to protect crops from animals is fencing, but somehow, it is not a full-proof solution. Most fencing techniques are harmful to animals. Thousands of animals die due to the side effects of fencing techniques, such as electrocution. This paper introduces a virtual fence to solve these issues. The proposed virtual fence is invisible to everyone, because it is an optical fiber sensor cable, which is laid 12-inches-deep in soil. A laser light is used at the start of the fiber sensor cable, and a detector detects at the end of the cable. The technique is based on the reflection of light inside the fiber optic cable. The interferometric technique is used to predict the changes in the pattern of the laser light. The fiber cable sensors are connected to a microprocessor, which can predict the intrusion of any animal. The use of machine learning techniques to pattern detection makes this technique highly efficient. The machine learning algorithms developed for the identification of animals can also classify the animal. The paper proposes an economical and feasible machine-learning-based solution to save crops from animals and to save animals from dangerous fencing. The description of the complete setup of optical fiber sensors, methodology, and machine learning algorithms are covered in this paper. This concept was implemented and regressive tests were carried out. Tests were performed on the data, which were not used for training purposes. Sets of people (50 people in each set) were randomly moved into the fiber optic cable sensor in order to test the effectiveness of the detection. There have been very few instances where the algorithm has been unable to categorize the detections into different animal classes. Three datasets were tested for configuration effectiveness. The complete setup was also tested in a zoo to test the identification of elephants and tigers. The efficiency of identification is 94% for human, 80% for tiger, and 75% for elephant.
EFFECT OF PREHARVEST FOLIAR SPRAY WITH GIBBERELLIC ACID AND CALCIUM ON YIELD AND QUALITY OF ON -TREE STORED FRUITS OF NAVEL ORANGE.

This experiment was carried out during two successive seasons (2002-2003 and 2003-2004) on Washington navel orange trees 30 year old grafted on sour orange grown in a private orchard in Elkaliobia Governorate to explore the possible effects of foliar application of gibberellic acid at 10, 15, 20ppm and calcium chloride at 1% and2% on the quality of on-tree-stored fruits of Washington navel orange. Treatments were applied at fruit color break as whole tree sprays . Fruits were harvested at four dates January, February, March, and April. Yield and fruit quality parameters were recorded. Gibberellic acid at 20ppm significantly decreased the reduction in fruit weight, fruit drop and increased juice weight percentage, juice acidity, ascorbic acid. Nevertheless GA3 at all the tested concentrations minimized the peel thickness in the last harvesting date and decreased juice T.S. S Calcium chloride at 2%caused significant increases in fruit weight, juice weight percentage, juice acidity, ascorbic acid and insignificant increase in peel thickness while it significantly decreased number of fruit dropped and juice T.S. S
Lighting Quality Criteria Based on the Luminance Spatial-Angular Distribution

The article is devoted to assessing lighting quality based on lighting engineering design's spatial-angular brightness distribution (LSAD). The main problems of modern lighting design related to the modeling of scenes based on the emissivity equation and restrictions on the use, in this regard, as the main criterion for the lighting quality of Unified Glare Rating (UGR), are considered. The mathematical foundations of the use of LSAD in the practice of lighting engineering design are proposed. The integral equation LSAD is obtained, which allows modeling the brightness at an arbitrary point of the scene volume. A method for solving the formulated equation based on double local estimates of the Monte Carlo method is proposed. The formulated algorithm for calculating LSAD is view-independent: LSAD visualizes the lighting scene at all fixed points. Methods for storing the calculated LSAD are proposed. Based on the LSAD, a new criterion for lighting quality is formulated, which was experimentally tested in a full-scale experiment to evaluate the lighting of Moscow Metro stations. In the experiment, the proposed criterion and UGR were compared. One calculated the quality criteria by the station's photos, and their correlation with observers' assessment was found. Computer models of stations were created, in which quality criteria were also calculated from the experiment viewpoints – obtained good correspondence of the calculations with the estimates of observers.
Training Pedagogical Skills: Evaluation of Lecturers and Teacher Training Students at Educational Universities in Vietnam

The most critical requirement in Vietnam is improving the quality of training teachers by developing professional skills to meet the requirements of the national innovation in education. This study aims to investigate lecturers’ and pre-service teachers’ evaluation of the process of teaching pedagogical skills in educational institutions in Vietnam. This is a cross-sectional school-based study using the survey to collect data from 448 pre-service teachers and 85 lecturers. The questionnaire was designed based on the research objectives and the standard regulations of the Vietnam Ministry of Education and Training. Cronbach’s alpha of the questionnaire was 0.83. The results of this study are evidence of the inadequacy of learners’ needs in pedagogical training programs in teacher training institutions in Vietnam. Educational universities need to improve their training plans, contents, and curriculum to maximize professional skills practice, facilitate soft skills development, and support interaction between lecturers and pre-service teachers. Enhancing professional skills for lecturers and monitoring their teaching process is also recommended.