Статті в журналах з теми "Filtre H1"

The optical filter under development employing frustrated total internal reflection is a micromechanical device containing two silicon rectangular prisms. There is flat silicon plane among hypotenuse edges of the prisms. The silicon plane is a Fabry-Perot resonator, with clearances h1 among the plate and prism edges being its mirrors. Theoretical resolution of the optical filter would be R ≈ 1,5∙103 if h1 = 50 µm and R ≈ 3∙105 if h1 = 100 µm with the thickness of the silicon plane being h = 65 µm; wavelength λ = 100 µm; free spectral region Δλ/λ ≈ 0,3…0,4, transmission in the maximum of spectral characteristics 0,6 (provided input and output silicon prism legs bloom).

This paper proposed three methods to compensate the temperature energy influence drift of the MEMS vibration gyroscope, including radial basis function neural network (RBF NN), RBF NN based on genetic algorithm (GA), and RBF NN based on GA with Kalman filter (KF). Three-axis MEMS vibration gyroscope (Gyro X, Gyro Y, and Gyro Z) output data are compensated and analyzed in this paper. The experimental results proved the correctness of these three methods, and MEMS vibration gyroscope temperature energy influence drift is compensated effectively. The results indicate that, after RBF NN-GA-KF method compensation, the bias instability of Gyros X, Y, and Z improves from 139°/h, 154°/h, and 178°/h to 2.9°/h, 3.9°/h, and 1.6°/h, respectively. And the angle random walk of Gyros X, Y, and Z was improved from 3.03°/h1/2, 4.55°/h1/2, and 5.89°/h1/2to 1.58°/h1/2, 2.58°/h1/2, and 0.71°/h1/2, respectively, and the drift trend and noise characteristic are optimized obviously.

This paper develops the unbiased finite impulse response (UFIR) filter for wireless sensor network (WSN) systems whose measurements are affected by random delays and packet dropout due to inescapable failures in the transmission and sensors. The Bernoulli distribution is used to model delays in arrived measurement data with known transmission probability. The effectiveness of the UFIR filter is compared experimentally to the KF and game theory recursive H1 filter in terms of accuracy and robustness employing the GPS-measured vehicle coordinates transmitted with latency over WSN.

AbstractIn this paper, we introduce the notion of state maps from a semihoop H1 to another semihoop H2, which is a generalization of internal states (or state operators) on a semihoop H. Also we give a type of special state maps from a semihoop H1 to H1, which is called internal state maps (or IS-maps). Then we give some examples and basic properties of (internal) state maps on semihoops. Moreover, we discuss the relations between state maps and internal states on other algebras. Then we introduce several kinds of filters by state maps on semihoops, called SM-filters, state filters and dual state filters, respectively, and discuss the relations among them. Furthermore we introduce and study the notion of prime SM-filters on semihoops. Finally, using SM-filter, we characterize two kinds of state semihoops.

Este proyecto evalúa el rendimiento en la localización de filtros emparejados para la detección del espectro en Radio Cognitiva. Para resolver el problema del modelo de detección espectral se aplican dos hipótesis Ho y H1, las cuales generaron la probabilidad de detección (PD) y la probabilidad de falsa alarma (PFA) sobre una señal para un radio de ruido determinado (SNR). Se muestra como los filtros adaptados mantienen el rendimiento y el cumplimiento de los requisitos establecidos por el IEEE 802.22 en los cambios en la PFA y la potencia del ruido.

Several methods have been developed in biomedical signal processing to extract the envelope and features of electromyography (EMG) signals and predict human motion. Also, efforts were made to use this information to improve the interaction of a human body and artificial protheses. The main operations here are envelope acquiring, artifacts filtering, estimate smoothing, EMG value standardizing, feature classifying, and motion recognizing. In this paper, we employ EMG data to extract the envelope with a highest Gaussianity using the rectified signal, where we deal with the absolute EMG signals so that all values become positive. First, we remove artifacts from EMG data by using filters such as the Kalman filter (KF), H1 filter, unbiased finite impulse response (UFIR) filter, and the cKF, cH1 filter, and cUFIR filter modified for colored measurement noise. Next, we standardize the EMG envelope and improve the Gaussianity. Finally, we extract the EMG signal features to provide an accurate prediction.

The human cone visual system maintains sensitivity over a broad range of illumination, from below 1 troland to 1,000,000 trolands. While the cone photoreceptors themselves are an important locus for sensitivity regulation—or light adaptation—the degree to which they contribute in primates remains unclear. To determine the range of sensitivity regulation in the outer retina, the temporal dynamics, neural gain control, and response range compression were measured in second-order neurons, the H1 horizontal cells, of the macaque retina. Situated at the first synapse in the retina, H1 cells receive input from a large population of cones. Lee et al. have previously shown that sensitivity regulation in H1 cells is both cone type-specific and spatially restricted. The sensitivity regulation seen in H1 cells at moderate illuminances thus takes place before the summation of cone signals in these cells, and the data establish the H1 cell as a convenient locus for analyzing cone signals. In the present study, cone-driven responses of primate H1 cells to temporally modulated sine-wave stimuli and to increment pulses were measured at steady levels of 1–1,000 trolands. The H1 cell gave a modulated response to sine-wave stimuli and hyperpolarized to increment pulses with overshoots at stimulus onset and offset. The temporal amplitude sensitivity function was primarily low-pass in shape, with a small degree of low-frequency roll off and a resonance shoulder near 40 Hz. A model incorporating a cascade of first-order filters together with an underdamped second-order filter could describe both temporal sinusoidal and pulse hyperpolarizations. Amplitude sensitivity was estimated from both pulse and sine-wave data as a function of the steady adaptation level. Sensitivity at low light levels (1 troland) showed a slowing in temporal dynamics, indicating time-dependent sensitivity regulation. Sensitivity was reduced at light levels above approximately 10 trolands, reflecting both response range compression and neural gain control. Thus the outer retina is a major locus for sensitivity regulation in primates.

Abstract Objective To evaluate the potential for machine learning to predict medication alerts that might be ignored by a user, and intelligently filter out those alerts from the user’s view. Materials and Methods We identified features (eg, patient and provider characteristics) proposed to modulate user responses to medication alerts through the literature; these features were then refined through expert review. Models were developed using rule-based and machine learning techniques (logistic regression, random forest, support vector machine, neural network, and LightGBM). We collected log data on alerts shown to users throughout 2019 at University of Utah Health. We sought to maximize precision while maintaining a false-negative rate <0.01, a threshold predefined through discussion with physicians and pharmacists. We developed models while maintaining a sensitivity of 0.99. Two null hypotheses were developed: H1—there is no difference in precision among prediction models; and H2—the removal of any feature category does not change precision. Results A total of 3,481,634 medication alerts with 751 features were evaluated. With sensitivity fixed at 0.99, LightGBM achieved the highest precision of 0.192 and less than 0.01 for the pre-defined maximal false-negative rate by subject-matter experts (H1) (P < 0.001). This model could reduce alert volume by 54.1%. We removed different combinations of features (H2) and found that not all features significantly contributed to precision. Removing medication order features (eg, dosage) most significantly decreased precision (−0.147, P = 0.001). Conclusions Machine learning potentially enables the intelligent filtering of medication alerts.

Abstract This paper deals with the problem of robust fault detection (FD) for an unmanned aerial vehicle (UAV) flight control system (FCS). A nonlinear model to describe the UAV longitudinal motions is introduced, in which multiple sources of disturbances include wind effects, modeling errors and sensor noises are classified into groups. Then the FD problem is formulated as fault detection filter (FDF) design for a kind of nonlinear discrete time varying systems subject to multiple disturbances. In order to achieve robust FD performance against multiple disturbances, simultaneous disturbance compensation and H1/H∞ optimization are carried out in designing the FDF. The optimality of the proposed FDF is shown in detail. Finally, both simulations and real flight data are applied to validate the proposed method. An improvement of FD performance is achieved compared with the conventional H1/H∞-FDF.

The purpose of this article is to provide an understanding of possible techniques through which a H1 31.25 kbps Foundation Fieldbus Manchester coded signal can be modulated or demodulated. This understanding will be rendered through MATLAB and SIMULINK models, simulating both ideal and practical as well as noiseless and noisy conditions during the modulation/demodulation process. The simulation models will differentiate between the modulation methods adopted to generate ideal rectangular as well as practical trapezoidal waveforms for the Manchester coded signal. The analysis of the simulation results will describe the mechanisms through which distortion induced by additive white Gaussian noise can be eliminated during the demodulation process either by using recursive filters, such as a Kalman filter, or by calculating wave energy levels in specific time intervals.

In this paper, a two-dimensional (2-D) circular-support wavelet transform (2-D CSWT) is presented. 2-D CSWT is a new geometrical image transform, which can efficiently represent images using 2-D circular spectral split schemes (circularlydecomposed frequency subspaces). 2-D all-pass functions and lattice structure are used to produce 1-level circular symmetric 2-D discrete wavelet transform with approximate linear phase 2-D filters. The classical one-dimensional (1-D) analysis Haar filter bank branches H0(z) and H1(z) which work as low-pass and high-pass filters, respectively are transformed into their 2-D counterparts H0(z1,z2) and H1(z1,z2) by applying a circular-support version of the digital spectral transformation (DST). The designed 2-D wavelet filter bank is realized in a separable architecture. The proposed architecture is simulated using Matlab program to measure the deflection ratio (DR) of the high frequency coefficient to evaluate its performance and compare it with the performance of the classical 2-D wavelet architecture. The correlation factor between the input and reconstructed images is also calculated for both architectures. The FPGA (Spartan-3E) Kit is used to implement the resulting architecture in a multiplier-less manner and to calculate the die area and the critical path or maximum frequency of operation. The achieved multiplier-less implementation takes a very small area from FPGA Kit (the die area in 3-level wavelet decomposition takes 300 slices with 7% occupation ratio only at a maximum frequency of 198.447 MHz).

Activation of p34 cdc2 requires a complex series of protein/protein interactions and specific phosphorylation and de-phosphorylation events to occur. The cellular role of p34 cdc2 kinase lies in the regulation of eukaryotic cell entry into mitosis, in particular into the G2/M transition. This study examines the kinetic characterization of p34 cdc2/cyclin B kinase utilizing both the phosphocellulose filter binding assay (FBA) and a modified version of the scintillation proximity assay (SPA). Several factors were identified that elicited an effect on the kinetic constants determined for the phosphorylation reaction, with emphasis being placed on the Km apparent (Kmapp) values. Factors identified included the concentration of adenosine triphosphate (ATP) used in the reaction, the addition of a biotin label to the peptide substrate as required for capture of the phospholabeled peptide by SPA, and the source from which the p34 cdc2/cyclin B kinase was isolated. The Kmapp is the kinetic constant most frequently reported in the examination of protein phosphorylation reactions, generally being derived from simple Lineweaver-Burk analysis. In a two-substrate reaction, however, the Kmapp may not be the most informative constant, as it will be influenced by the concentration of the second substrate. In this study, true Km values were determined for ATP and a bi-otinylated peptide substrate used in the p34 cdc2 kinase-mediated phosphorylation reaction. Alberty and Dalziel constants were derived from secondary Lineweaver-Burk analysis of the phosphocellulose filter binding and SPA data. The kinetic constants determined by filter binding and scintillation proximity displayed good correlation, thus confirming the utility of scintillation proximity for the purpose of enzyme kinetic studies.

Subglottal Impedance-Based Inverse Filtering (IBIF) allows for the continuous, non-invasive estimation of glottal airflow from a surface accelerometer placed over the anterior neck skin below the larynx. It has been shown to be advantageous for the ambulatory monitoring of vocal function, specifically in the use of high-order statistics to understand long-term vocal behavior. However, during long-term ambulatory recordings over several days, conditions may drift from the laboratory environment where the IBIF parameters were initially estimated due to sensor positioning, skin attachment, or temperature, among other factors. Observation uncertainties and model mismatch may result in significant deviations in the glottal airflow estimates; unfortunately, they are very difficult to quantify in ambulatory conditions due to a lack of a reference signal. To address this issue, we propose a Kalman filter implementation of the IBIF filter, which allows for both estimating the model uncertainty and adapting the airflow estimates to correct for signal deviations. One-way analysis of variance (ANOVA) results from laboratory experiments using the Rainbow Passage indicate an improvement using the modified Kalman filter on amplitude-based measures for phonotraumatic vocal hyperfunction (PVH) subjects compared to the standard IBIF; the latter showing a statistically difference (p-value =0.02, F=4.1) with respect to a reference glottal volume velocity signal estimated from a single notch filter used here as ground-truth in this work. In contrast, maximum flow declination rates from subjects with vocal phonotrauma exhibit a small but statistically difference between the ground-truth signal and the modified Kalman filter when using one-way ANOVA (p-value =0.04, F=3.3). Other measures did not have significant differences with either the modified Kalman filter or IBIF compared to ground-truth, with the exception of H1-H2, whose performance deteriorates for both methods. Overall, both methods (modified Kalman filter and IBIF) show similar glottal airflow measures, with the advantage of the modified Kalman filter to improve amplitude estimation. Moreover, Kalman filter deviations from the IBIF output airflow might suggest a better representation of some fine details in the ground-truth glottal airflow signal. Other applications may take more advantage from the adaptation offered by the modified Kalman filter implementation.

Recently, methods of statistical estimation theory have been applied by Bialek and collaborators (1991) to reconstruct time-varying velocity signals and to investigate the processing of visual information by a directionally selective motion detector in the fly's visual system, the H1 cell. We summarize here our theoretical results obtained by studying these reconstructions starting from a simple model of H1 based on experimental data. Under additional technical assumptions, we derive a closed expression for the Fourier transform of the optimal reconstruction filter in terms of the statistics of the stimulus and the characteristics of the model neuron, such as its firing rate. It is shown that linear reconstruction filters will change in a nontrivial way if the statistics of the signal or the mean firing rate of the cell changes. Analytical expressions are then derived for the mean square error in the reconstructions and the lower bound on the rate of information transmission that was estimated experimentally by Bialek et al. (1991). For plausible values of the parameters, the model is in qualitative agreement with experimental data. We show that the rate of information transmission and mean square error represent different measures of the reconstructions: in particular, satisfactory reconstructions in terms of the mean square error can be achieved only using stimuli that are matched to the properties of the recorded cell. Finally, it is shown that at least for the class of models presented here, reconstruction methods can be understood as a generalization of the more familiar reverse-correlation technique.

Perubahan iklim secara langsung menyebabkan terjadinya kenaikan muka air laut bagi wilayah kepesisiran. Hal ini menyebabkan garis pantai menjadi tidak stabil, sehingga perlu untuk dilakukan pemantauan secara spasial dan temporal. Pulau Karimunjawa dan Kemujan merupakan dua pulau kecil dengan garis pantai yang saling terhubung oleh hutan mangrove. Penelitian ini bertujuan untuk mengidentifikasi dinamika garis pantai Pulau Karimunjawa dan Kemujan tahun 2000 – 2030. Identifikasi garis pantai tahun 2000 – 2020 diperoleh melalui ekstraksi garis pantai metode single band (B5) dan band ratio (B4/B2 dan B5/B2) citra Landsat 7 ETM+, sedangkan untuk identifikasi garis pantai tahun 2030 diperoleh melalui prediksi garis pantai metode Kalman Filter Model. Hasil penelitian menunjukkan bahwa garis pantai cenderung mengalami akresi dengan rata-rata perubahan garis pantai sebesar +0,76 meter per tahun dan diprediksi akan mengalami kemajuan garis pantai rata-rata sebesar +1,02 meter per tahun. Garis pantai daerah penelitian tergolong stabil dengan sedikit akresi (akresi sedang).Climate change causes sea level rise for coastal region. This causes the shoreline become unstable, so spatial and temporal monitoring is necessary. Karimunjawa and Kemujan Islands are two small islands with a shoreline [H1] that are connected by mangrove forests. This research aims to identify shoreline dynamics of Karimunjawa and Kemujan Islands in 2000 – 2030. The identification of shoreline in 2000 – 2020 was obtained through the extraction of shoreline using single band (B5) and band ratio (B4/B2 and B5/B2) method from Landsat 7 ETM+, while for identification of shoreline in 2030 was obtained through shoreline forecasting using Kalman Filter Model method. The results show that the shoreline tends to be accreted with an average shoreline change of +0.76 meters per year and predicted to be accretion with an average shoreline change of +1.02 meters per year. The Shoreline of the study area is [H2] relatively stable with little accretion (medium accretion).

The process of news consumption has undergone great changes over the past decade: Information is now available in an ever-increasing amount from a plethora of sources. Recent work suggests that most people would favor algorithmic solutions over human editors. This stands in contrast to public and scholarly debate about the pitfalls of algorithmic news selection—i.e., the so-called “filter bubbles.” This study therefore investigates reasons and motivations which might lead people to prefer algorithmic gatekeepers over human ones. We expect that people have more algorithmic appreciation when consuming news to pass time, entertain oneself, or out of escapism than when using news to keep up-to-date with politics (H1). Secondly, we hypothesize the extent to which people are confident in their own cognitive abilities to moderate that relationship: When people are overconfident in their own capabilities to estimate the relevance of information, they are more likely to have higher levels of algorithmic appreciation, due to the third person effect (H2). For testing those two pre-registered hypotheses, we conducted an online survey with a sample of 268 US participants and replicated our study using a sample of 384 Dutch participants. The results show that the first hypothesis cannot be supported by our data. However, a positive interaction between overconfidence and algorithmic appreciation for the gratification of surveillance (i.e., gaining information about the world, society, and politics) was found in both samples. Thereby, our study contributes to our understanding of the underlying reasons people have for choosing different forms of gatekeeping when selecting news.

The thickness of the liquefable layer can be the factor inducing liquefaction hazard, apart from seismicity. Several studies have been conducted to predict the possibility of the liquefable layer based on the filed sampling. However, a detailed investigation of the subsurface interpretation has not been defined, in particular the thickness estimation of the liquefable layer. This study is carried out in south Cilacap area where potential liquefaction is exists due to the earthquake history data and near surface condition. The aim of this study is to investigate the physical properties and thickness distribution using GGMplus gravity data and resistivity data. This research is conducted by spectrum analysis of gravity model and 2D resistivity model . This study’s main results is by performing the residual gravity anomaly with the associated SRTM/DEM data to define the subsurface physical distribution and structural orientation of the area. Residual gravity anomaly is also separated through the low pass filter in order to have robust interpretation. The residual anomaly indicates that the area has identical structural pattern with geological and SRTM map. The results show a pattern of high gravity index in the northeast area of ​​the study having range of 70 – 115 MGal gravity index, associated with the volcanic breccia, and a low gravity profile with less than 65 in the southwest, associated with the alluvial and water table dominated distribution. The thickness of Alluvial is determined by resistivity model with H1 at a range of 3 meters and H2 at a range of 4 m. This research is included in the potential liquefaction category with the potential for a large earthquake.

Purpose The purpose of this paper is to determine the cost effectiveness of palliative care on patients in a home health and hospice setting. Secondary data set was utilized to test the hypotheses of this study. Home health care and hospice care services have the potential to avert hospital admissions in patients requiring palliative care, which significantly affects medicare spending. With the aging population, it has become evident that demand of palliative care will increase four-fold. It was determined that current spending on end-of-life care is radically emptying medicare funds and fiscally weakening numerous families who have patients under palliative care during life-threatening illnesses. The study found that a majority of people registering for palliative and hospice care settings are above the age group of 55 years old. Design/methodology/approach Different variables like length of stay, mode of payment and disease diagnosis were used to filter the available data set. Secondary data were utilized to test the hypothesis of this study. There are very few studies on hospice and palliative care services and no study focuses on the cost associated with this care. Since a very large number of the USA, population is turning 65 and over, it is very important to analyze the cost of care for palliative and hospice care. For the purpose of this analysis, data were utilized from the National Home and Hospice Care Survey (NHHCS), which has been conducted periodically by the Centers for Disease Control and Prevention’s National Center for Health Statistics. Descriptive statistics, χ2 tests and t-tests were used to test for statistical significance at the p<0.05 level. Findings The Statistical Package for Social Sciences (SPSS) was utilized for this result. H1 predicted that patients in the age group of 65 years and up have the highest utilization of home and hospice care. This study examined various demographic variables in hospice and home health care which may help to evaluate the cost of care and the modes of payments. This section of the result presents the descriptive analysis of dependent, independent and covariate variables that provide the overall national estimates on differences in use of home and hospice care in various age groups and sex. Research limitations/implications The data set used was from the 2007 NHHCS survey, no data have been collected thereafter, and therefore, gap in data analysis may give inaccurate findings. To compensate for this gap in the data set, recent studies were reviewed which analyzed cost in palliative care in the USA. There has been a lack of evidence to prove the cost savings and improved quality of life in palliative/hospice care. There is a need for new research on the various cost factors affecting palliative care services as well as considering the quality of life. Although, it is evident that palliative care treatment is less expensive as compared to the regular care, since it eliminates the direct hospitalization cost, but there is inadequate research to prove that it improves the quality of life. A detailed research is required considering the additional cost incurred in palliative/hospice care services and a cost-benefit analysis of the same. Practical implications While various studies reporting information applicable to the expenses and effect of family caregiving toward the end-of-life were distinguished, none of the previous research discussed this issue as their central focus. Most studies addressed more extensive financial effect of palliative and end-of-life care, including expenses borne by the patients themselves, the medicinal services framework and safety net providers or beneficent/willful suppliers. This shows a significant hole in the current writing. Social implications With the aging population, it has become evident that demand of palliative/hospice care will increase four-fold. The NHHCS have stopped keeping track of the palliative care requirements after 2007, which has a negative impact on the growing needs. Cost analysis can only be performed by analyzing existing data. This review has recognized a huge niche in the evidence base with respect to the cost cares of giving care and supporting a relative inside a palliative/hospice care setting. Originality/value The study exhibited that cost diminishments in aggressive medications can take care of the expenses of palliative/hospice care services. The issue of evaluating result in such a physically measurable way is complicated by the impalpable nature of large portions of the individual components of outcome. Although physical and mental well-being can be evaluated to a certain degree, it is significantly more difficult to gauge in a quantifiable way, the social and profound measurements of care that help fundamentally to general quality of care.

Intramembrane proteases (IMPs) are a unique class of proteases that catalyze the proteolysis within the membrane and regulate diverse cellular processes in various organisms. RseP, an Escherichia coli site-2 protease (S2P) family IMP, is involved in the regulation of an extracytoplasmic stress response through the cleavage of membrane-spanning anti-stress-response transcription factor (anti-σE) protein RseA. Extracytoplasmic stresses trigger a sequential cleavage of RseA, in which first DegS cleaves off its periplasmic domain, and RseP catalyzes the second cleavage of RseA. The two tandem-arranged periplasmic PDZ (PDZ tandem) domains of RseP serve as a size-exclusion filter which prevents the access of an intact RseA into the active site of RseP IMP domain. However, RseP’s substrate recognition mechanism is not fully understood. Here, we found that a periplasmic region of RseP, located downstream of the PDZ tandem, contains a segment (named H1) predicted to form an amphiphilic helix. Bacterial S2P homologs with various numbers of PDZ domains have a similar amphiphilic helix in the corresponding region. We demonstrated that the H1 segment forms a partially membrane-embedded amphiphilic helix on the periplasmic surface of the membrane. Systematic and random mutagenesis analyses revealed that the H1 helix is important for the stability and proteolytic function of RseP and that mutations in the H1 segment can affect the PDZ-mediated substrate discrimination. Cross-linking experiments suggested that H1 directly interacts with the DegS-cleaved form of RseA. We propose that H1 acts as an adaptor required for proper arrangement of the PDZ tandem domain to perform its filter function and for substrate positioning for its efficient cleavage.

Neutral Hydrogen (HI) is a fundamental component in the composition of galaxies, providing crucial insights into their structure and dynamics. In this research, we propose a novel approach to improve the detection of H1 spectral lines in radio telescope data by employing a matched filter algorithm. The algorithm capitalizes on the convolution of a template function, optimized using the "Busy" function that defines the line profile properties, with a randomly generated H1 peak. This H1 peak is subsequently embedded within noisy data, accounting for the effects of telescope measurements, such as thermal broadening and radio-frequency interference. The incorporation of the Voigt profile in the noise generation ensures a realistic representation of these effects. Through comprehensive analysis, we demonstrate that a matched filter with a multi-layered convolution scheme yields the highest efficiency in recovering H1 peaks. The effectiveness of peak recovery is shown to be influenced by the injected noise profiles, allowing for a direct comparison of the matched filter’s performance concerning the Integrated and Peak Signal to Noise ratios. This research contributes to the advancement of observational techniques in astrophysics and deepens our understanding of the distribution and properties of Neutral Hydrogen in galaxies. It represents the first such exploration of this technique in the context of the Canadian Hydrogen Observatory and Radio Transient Detector (CHORD), which is under construction at the Dominion Radio Astronomy Observatory in Penticton, BC and which will survey the HI sky more broadly and deeply than has been previously possible.

Hydrocyclones are used as pre-filter to reduce suspended particles in irrigation water on the subsequent filters. The aim of the study was to determine separation efficiency (SE) of hydrocyclones, called H1, H2 and H3 according to inlet/outlet diameters, at water velocities of 1.0 (V10), 1.5 (V15) and 2.0 (V20) ms-1, and sands diameter of 0.5 (D05), 1.0 (D10), 1.5 (D15), 2.0 (D20) and 2.5 (D25) mm. Therefore, a hydrocyclone laboratory test system was constituted using a water tank, motor pump, inverter, flowmeter, valve, hydrocyclone, disk filter, and polythene pipe. Separation efficiencies were calculated by dividing amounts of sand collected in collection box by feeding amount of sand. The lowest average separation efficiency was determined as 37% at H1V10D05 treatment and the highest ones as 97% at both H2V20D10 and H3V20D20, and the other ones changed between two values. Average separation efficiencies resulted as 69%, 88% and 88% for H1, H2 and H3 hydrocyclones, and 71%, 84% and 90% for V10, V15 and V20 water velocities, and 78%, 82%, 82%, 83% and 84% for D05, D10, D15, D20 and D25 sand diameters, respectively. Besides these, average separation efficiency for three parameters was 82%. Since the inlet size of H2 is smaller than that of H3 and its SE was higher than that of H1 and equal to that of H3, the most suitable hydrocyclone was determined as H2 to be used in the micro irrigation. The highest average separation efficiency was 90% at a water velocity of 2.0 ms-1. According to separation efficiency of the hydrocyclone, the optimum water velocity in the inlet of the hydrocyclone was determined as 2.0 ms-1. The separation efficiency of hydrocyclone showed that the efficiencies increased with increasing water velocity from 0.5 ms-1 to 2.0 ms-1 and sand diameters from 0.5 to 2.5 mm. In separation efficiency for micro irrigation, water velocities and suspended materials play crucial roles as well as hydrocyclone mechanical properties.

Abstract We investigate the assimilation of nowcasted information into a classical data assimilation cycle. As a reference setup we employ the assimilation of standard observations such as direct observations of particular variables into a forecasting system. The pure advective movement extrapolation of observations as a simple nowcasting (NWC) is usually much better for the first minutes to hours, until outperformed by numerical weather prediction (NWP) based on data assimilation. Can nowcasted information be used in the data assimilation cycle? We study both an oscillator model and the Lorenz 63 model with assimilation based on the Localized Ensemble Transform Kalman Filter (LETKF). We investigate and provide a mathematical framework for the assimilation of nowcasted information, approximated as a local tendency, into the LETKF in each assimilation step. In particular, we derive and discuss adequate observation error and background uncertainty covariance matrices and interprete the assimilation of nowcasted information as assimilation with an H1-type metric in observation space. Further, we show numerical results which prove that nowcasted information in data assimilation has the potential to significantly improve model based forecasting.

Glycolytic overload in diabetes causes large accumulation of the highly reactive dicarbonyl compound methylglyoxal (MGO) and overproduction of advanced glycation end products (AGEs), which interact with its receptors (RAGE), leading to diabetes-associated macrovascular complications. Bladder is an organ that stays most in contact with dicarbonyl species, but little is known about the importance of MGO-AGEs-RAGE pathway to diabetes-associated bladder dysfunction. Here, we aimed to investigate the role of MGO-AGEs-RAGE pathway in bladder dysfunction of diabetic male and female ob/ob mice in comparison with wild type (WT) lean mice. Diabetic ob/ob mice were treated with the AGE-breaker alagebrium (ALT-711; 1 mg/kg) for eight weeks in drinking water. Compared with WT animals, male and female ob/ob mice showed marked hyperglycemia and insulin resistance, whereas fluid intake remained unaltered. Levels of total AGEs and MGO-derived hydroimidazolone 1 (MG-H1) and RAGE in bladder tissues, as well as fluorescent AGEs in serum were significantly elevated in ob/ob mice of either sex. Collagen content was also markedly elevated in bladders of ob/ob mice. Void spot assays in filter paper in conscious mice revealed significant increases of total void volume and volume per void in ob/ob mice with no alterations of spot number. Treatment with ALT-711 significantly reduced the levels of MGO, AGEs, RAGE, and collagen content in ob/ob mice. In addition, ALT-711 treatment normalized the volume per volume and increased the number of spots in ob/ob mice. Activation of AGEs-RAGE pathways by MGO in bladder wall may contribute to pathogenesis of diabetes-associated bladder dysfunction.

Synopsis The research problem We measured the sensitivity of bank market values to capital and regulatory adjustments (RAs) applied to bank capital using a novel approach of measuring value relevance. Motivation or theoretical reasoning Regulators require banks to apply adjustments to book equity to calculate regulatory capital, where book equity is the starting point of the calculation of capital ratios. The regulators emphasize the benefits of these adjustments. Making banks hold more capital, the adjustments should contribute to the reduction of procyclical amplification of financial shocks throughout the banking system, financial markets, and the broader economy. However, RAs are underresearched: empirical research focuses on a few of them, such as the prudential filter on AFS securities, and their effects in specific circumstances. This prompts questions about the overall relevance of RAs. This paper then examines the sensitivity, expressed in elasticities, of market values to capital and RAs. Are adjustments, on their own or in aggregate, value relevant? Are market values more or less sensitive to different measures of bank capital, such as book equity, Tier 1, or total capital? Does the sensitivity evolve over time? And if so, why? The test hypotheses H1: The elasticity of bank book equity with respect to market value is 1. H2: The elasticities of RAs with respect to market values are 0. Target population US bank holding companies over the years 2001Q1–2022Q3. Adopted methodology We used panel data analyses to estimate log–log models. These models transform all variables such as market values and accounting values into logs. Analyses We used log–log models as an alternative to traditional additive-linear models because the coefficients of the latter are sensitive to sample choice, choice of time period, and outlier treatment. The response coefficients in log–log models are scale-free elasticities that measure, with some precision, the proportional change in the dependent variable associated with a proportional change in the independent variables. Findings Our results show that the sensitivity of bank book equity converges to 1 when market uncertainty is low and when banks’ Tier 1 ratios reach 12% of risk-weighted assets (RWAs). Market values are more sensitive to changes in capital of highly geared banks when market uncertainty is high, with shareholders responding positively in particular to increases in Tier 1 and total capital. This is consistent with risk-shifting by shareholders. Market values are generally less sensitive to prudential filters, such as those on unrealized gains and losses on AFS securities.

Introduction In this article, we hypothesise how mainstream media coverage can promote the spread of disinformation about Covid-19. Mainstream media are often discussed as opposed to disinformation (Glasser; Benkler et al.). While the disinformation phenomenon is related to the intentional production and spread of misleading and false information to influence public opinion (Fallis; Benkler et al.), mainstream media news is expected to be based on facts and investigation and focussed on values such as authenticity, accountability, and autonomy (Hayes et al.). However, journalists might contribute to the spread of disinformation when they skip some stage of information processing and reproduce false or misleading information (Himma-Kadakas). Besides, even when the purpose of the news is to correct disinformation, media coverage might contribute to its dissemination by amplifying it (Tsfati et al.). This could be particularly problematic in the context of social media, as users often just read headlines while scrolling through their timelines (Newman et al.; Ofcom). Thus, some users might share news from the mainstream media to legitimate disinformation about Covid-19. The pandemic creates a delicate context, as journalists are often pressured to produce more information and, therefore, are more susceptible to errors. In this research, we focussed on the hypothesis that legitimate news can contribute to the spread of disinformation on social media through headlines that reinforce disinformation discourses, even though the actual piece may frame the story differently. The research questions that guide this research are: are URLs with headlines that reinforce disinformation discourses and other mainstream media links shared into the same Facebook groups? Are the headlines that support disinformation discourses shared by Facebook users to reinforce disinformation narratives? As a case study, we look at the Brazilian disinformation context on Covid-19. The discussion about the disease in the country has been highly polarised and politically framed, often with government agents and scientists disputing the truth about facts on the disease (Araújo and Oliveira; Recuero and Soares; Recuero et al.). Particularly, the social media ecosystem seems to play an important role in these disputes, as Brazilian President Jair Bolsonaro and his supporters use it as a key channel to spread disinformation about the virus (Lisboa et al.; Soares et al.). We use data from public groups on Facebook collected through CrowdTangle and a combination of social network analysis and content analysis to analyse the spread and the content of URLs and posts. Theoretical Background Disinformation has been central to the Covid-19 “infodemic”, created by the overabundance of information about the pandemic, which makes it hard for people to find reliable guidance and exacerbates the outbreak (Tangcharoensathien et al.). We consider disinformation as distorted, manipulated, or false information intentionally created to mislead someone (Fallis; Benkler et al.). Disinformation is often used to strengthen radical political ideologies (Benkler et al.). Around the world, political actors politically framed the discussion about the pandemic, which created a polarised public debate about Covid-19 (Allcott et al., Gruzd and Mai; Recuero and Soares). On social media, contexts of polarisation between two different political views often present opposed narratives about the same fact that dispute public attention (Soares et al.). This polarisation creates a suitable environment for disinformation to thrive (Benkler et al.) The polarised discussions are often associated with the idea of “bubbles”, as the different political groups tend to share and legitimate only discourses that are aligned with the group's ideological views. Consequently, these groups might turn into ideological bubbles (Pariser). In these cases, content shared within one group is not shared within the other and vice versa. Pariser argues that users within the bubbles are exposed exclusively to content with which they tend to agree. However, research has shown that Pariser’s concept of bubbles has limitations (Bruns), as most social media users are exposed to a variety of sources of information (Guess et al.). Nevertheless, polarisation might lead to different media diets and disinformation consumption (Benkler et al.). That is, users would have contact with different types of information, but they would choose to share certain content over others because of their political alignment (Bruns). Therefore, we understand that bubbles are created by the action of social media users who give preference to circulate (through retweets, likes, comments, or shares) content that supports their political views, including disinformation (Recuero et al.). Thus, bubbles are ephemeral structures (created by users’ actions in the context of a particular political discussion) with permeable boundaries (users are exposed to content from the outside) in discussions on social media. This type of ephemeral bubble might use disinformation as a tool to create a unique discourse that supports its views. However, it does not mean that actors within a “disinformation bubble” do not have access to other content, such as the news from the mainstream media. It means that the group acts to discredit and to overlap this content with an “alternative” story (Larsson). In addition, the mainstream media might disseminate false or inaccurate disinformation (Tsfati et al.). Particularly, we focus on inaccurate headlines that reinforce disinformation narratives, as social media users often only read news headlines (Newman et al.; Ofcom). This is especially problematic because a large number of social media users are exposed to mainstream media content, while exposure to disinformation websites is heavily concentrated on only a few users (Guess et al.; Tsfati et al.). Therefore, when the mainstream media disseminate disinformation, it is more likely that a larger number of social media users will be exposed to this content and share it into ideological bubbles. Based on this discussion, we aim to understand how the mainstream media contribute to the spread of disinformation discourses about Covid-19. Methods This study is about how mainstream media coverage might contribute to the spread of disinformation about Covid-19 on Facebook. We propose two hypotheses, as follows: H1: When mainstream media headlines frame the information in a way that reinforces the disinformation narrative, the links go into a “disinformation bubble”. H2: In these cases, Facebook users might use mainstream media coverage to legitimate disinformation narratives. We selected three case studies based on events that created both political debate and high media coverage in Brazil. We chose them based on the hypothesis that part of the mainstream media links could have produced headlines that support disinformation discourses, as the political debate was high. The events are: On 24 March 2020, Brazilian President Jair Bolsonaro made a public pronouncement on live television. In the week before the pronouncement, Brazilian governors decided to follow World Health Organisation (WHO) protocols and closed non-essential business. In his speech, Bolsonaro criticised social distancing measures. The mainstream media reproduced some of his claims and claims from other public personalities, such as entrepreneurs who also said the protocols would harm the economy. On 8 June 2020, a WHO official said that it “seems to be rare that an asymptomatic person transmits [Covid-19] onward to a secondary individual”. Part of the mainstream media reproduced the claim out of context, which could promote the misperception that both asymptomatic and pre-symptomatic persons (early stages of an illness, before the first symptoms) do not transmit Covid-19 at all. On 9 November 2020, Brazil’s national sanitary watchdog Anvisa reported that they had halted the clinical studies on the CoronaVac vaccine, developed by the Chinese company Sinovac. Bolsonaro often criticised CoronaVac because it was being produced in partnership with São Paulo’s Butantan Institute and became the subject of a political dispute between Bolsonaro and the Governor of São Paulo, João Dória. Bolsonaro said the halt of the CoronaVac trial was "another victory for Jair Bolsonaro". Anvisa halted the trail after a "severe adverse event". The mainstream media rapidly reverberated the decision. Later, it was revealed that the incident was a death that had nothing to do with the vaccine. Before we created our final dataset that includes links from the three events together, we explored the most shared URLs in each event. We used keywords to collect posts shared in the public groups monitored by CrowdTangle, a tool owned by Facebook that tracks publicly available posts on the platform. We collected posts in a timeframe of three days for each event to prevent the collection of links unrelated to the cases. We collected only posts containing URLs. Table 1 summarises the data collected. Table 1: Data collected Dates March 24-26 2020 June 8-10 2020 November 9-11 2020 Keywords “Covid-19” or “coronavirus” and “isolation” or “economy” “Covid-19” or “coronavirus” and “asymptomatic” “vaccine” and “Anvisa” or “CoronaVac” Number of posts 4780 2060 3273 From this original dataset, we selected the 60 most shared links from each period (n=180). We then filtered for those which sources were mainstream media outlets (n=74). We used content analysis (Krippendorff) to observe which of these URLs headlines could reinforce disinformation narratives (two independent coders, Krippendorff’s Alpha = 0.76). We focussed on headlines because when these links are shared on Facebook, often it is the headline that appears to other users. We considered that a headlined reinforced disinformation discourses only when it was flagged by both coders (n=21 – some examples are provided in Table 3 in the Results section). Table 2 provides a breakdown of this analysis. Table 2: Content analysis Event Mainstream media links Headlines that support disinformation discourses Number of links Number of posts Economy and quarantine 24 7 112 Asymptomatic 22 7 163 Vaccine trial 28 7 120 Total 74 21 395 As the number of posts that shared URLs with headlines that supported disinformation was low (n=395), we conducted another CrowdTangle search to create our final dataset. We used a sample of the links we classified to create a “balanced” dataset. Out of the 21 links with headlines that reinforced disinformation, we collected the 10 most shared in public groups monitored by CrowdTangle (this time, without any particular timeframe) (n=1346 posts). In addition, we created a “control group” with the 10 most shared links that neither of the coders considered could reinforce disinformation (n=1416 posts). The purpose of the “control group” was to identify which Facebook groups tend to share mainstream media links without headlines that reinforce disinformation narratives. Therefore, our final dataset comprises 20 links and 2762 posts. We then used social network analysis (Wasserman and Faust) to map the spread of the 20 links. We created a bipartite network, in which nodes are (1) Facebook groups and (2) URLs; and edges represent when a post within a group includes a URL from our dataset. We applied a modularity metric (Blondel et al.) to identify clusters. The modularity metric allows us to identify “communities” that share the same or similar links in the network map. Thus, it helped us to identify if there was a “bubble” that only shares the links with headlines that support disinformation (H1). To understand if the disinformation was supporting a larger narrative shared by the groups, we explored the political alignments of each cluster (H2). We used Textometrica (Lindgreen and Palm) to create word clouds with the most frequent words in the names of the cluster groups (at least five mentions) and their connections. Finally, we also analysed the posts that shared each of the 10 links with headlines that reinforced disinformation. This also helped us to identify how the mainstream media links could legitimate disinformation narratives (H2). Out of the 1346 posts, only 373 included some message (the other 973 posts only shared the link). We used content analysis to see if these posts reinforced the disinformation (two independent coders – Krippendorff’s Alpha = 0.723). There were disagreements in the categorisation of 27 posts. The two coders reviewed and discussed the classification of these posts to reach an agreement. Results Bubbles of information In the graph (Figure 1), red nodes are links with headlines that support disinformation discourses, blue nodes are the other mainstream media links, and black nodes are Facebook groups. Our first finding is that groups that shared headlines that support disinformation rarely shared the other mainstream media links. Out of the 1623 groups in the network, only 174 (10.7%) shared both a headline that supports disinformation discourse, and another mainstream media link; 712 groups (43.8%) only shared headlines that support disinformation; and 739 groups (45.5%) only shared other links from the mainstream media. Therefore, users’ actions created two bubbles of information. Figure 1: Network graph The modularity metric confirmed this tendency of two “bubbles” in the network (Figure 2). The purple cluster includes seven URLs with headlines that support disinformation discourse. The green cluster includes three headlines that support disinformation discourse and the other 10 links from the mainstream media. This result partially supports H1: When mainstream media headlines frame the information in a way that reinforces the disinformation narrative, the links go into a “disinformation bubble”. As we identified, most of the headlines that support disinformation discourse went into a separate “bubble”, as users within the groups of this bubble did not share the other links from the mainstream media. Figure 2: Network graph with modularity This result shows that users’ actions boost the creation of bubbles (Bakshy et al.), as they choose to share one type of content over the other. The mainstream media are the source of all the URLs we analysed. However, users from the purple cluster chose to share only links with headlines that supported disinformation discourses. This result is also related to the political framing of the discussions, as we explore below. Disinformation and Political Discourse We used word clouds (Lindgreen and Palm) to analyse the Facebook groups’ names to explore the ideological affiliation of the bubbles. The purple bubble is strongly related to Bolsonaro and his discourse (Figure 3). Bolsonaro is the most frequent word. Other prevalent words are Brazil, patriots (both related to his nationalist discourse), right-wing, conservative, military (three words related to his conservative discourse and his support of the military dictatorship that ruled Brazil from 1964 to 1985), President, support, and Alliance [for Brazil] (the name of his party). Some of the most active groups within the purple bubble are “Alliance for Brazil”, “Bolsonaro 2022 [next presidential election]”, “Bolsonaro’s nation 2022”, and “I am right-wing with pride”. Figure 3: Purple cluster word cloud Bolsonaro is also a central word in the green cluster word cloud (Figure 4). However, it is connected to other words such as “against” and “out”, as many groups are anti-Bolsonaro. Furthermore, words such as left-wing, Workers’ Party (centre-left party), Lula and Dilma Rousseff (two Workers’ Party ex-presidents) show another ideological alignment in general. In addition, there are many local groups (related to locations such as Rio de Janeiro, São Paulo, Rio Grande do Sul, Minas Gerais, and others), and groups to share news (news, newspaper, radio, portal). “We are 70 per cent [anti-Bolsonaro movement]”, “Union of the Left”, “Lula president”, and “Anti-Bolsonaro” are some of the most active groups within the green cluster. Figure 4: Green cluster word cloud Then, we analysed how users shared the mainstream media links with headlines that support disinformation discourses. In total, we found that 81.8% of the messages in the posts that shared these links also reproduced disinformation narratives. The frequency was higher (86.2%) when considering only posts that shared one of the seven links from the purple cluster (based on the modularity metric). Consequently, it was lower (64%) in the messages that shared one of the other three links. The messages often showed support for Bolsonaro; criticised other political and health authorities (the WHO, São Paulo Governor João Dória, and others), China, and the “leftists” (all opposition to Bolsonaro); claimed that quarantine and social distancing measures were unnecessary; and framed vaccines as dangerous. We provide some examples of headlines and posts in Table 3 (we selected the most-shared URL for each event to illustrate). This result supports H2 as we found that users shared mainstream media headlines that reinforce disinformation discourse to legitimate the disinformation narrative; and that it was more prevalent in the purple bubble. Table 3: Examples of headlines and posts Headline Post "Unemployment is a crisis much worse than coronavirus", says Bolsonaro Go to social media to support the President. Unemployment kills. More than any virus... hunger, depression, despair and everything UNEMPLOYMENT, THE DEPUTIES CHAMBER, THE SENATE AND THE SUPREME COURT KILL MORE THAN COVID19 Asymptomatic patients do not boost coronavirus, says WHO QUARANTINE IS FAKE #StayHome, the lie of the century! THIS GOES TO THE PUPPETS OF THE COMMUNIST PARTIES THE AND FUNERARY MEDIA Anvisa halts Coronavac vaccine trial after "serious adverse event" [The event] is adverse and serious, so the vaccine killed the person by covid And Doria [Governor of São Paulo and political adversary of Bolsonaro] wants to force you to take this shit This result shows that mainstream media headlines that support disinformation narratives may be used to reinforce disinformation discourses when shared on Facebook, making journalists potential agents of disinformation (Himma-Kadakas; Tsfati et al.). In particular, the credibility of mainstream news is used to support an opposing discourse, that is, a disinformation discourse. This is especially problematic in the context of Covid-19 because the mainstream media end up fuelling the infodemic (Tangcharoensathien et al.) by sharing inaccurate information or reverberating false claims from political actors. Conclusion In this article, we analysed how the mainstream media contribute to the spread of disinformation about Covid-19. In particular, we looked at how links from the mainstream media with headlines that support disinformation discourse spread on Facebook, compared to other links from the mainstream media. Two research questions guided this study: Are URLs with headlines that reinforce disinformation discourses and other mainstream media links shared into the same Facebook groups? Are the headlines that support disinformation discourses shared by Facebook users to reinforce disinformation narratives? We identified that (1) some Facebook groups only shared links with headlines that support disinformation narratives. This created a “disinformation bubble”. In this bubble, (2) Facebook users shared mainstream media links to reinforce disinformation – in particular, pro-Bolsonaro disinformation, as many of these groups had a pro-Bolsonaro alignment. 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