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The design of a low-pass-frequency filter with the electronic change of the approximation characteristics of resulting responses is presented. The filter also offers the reconnection-less reconfiguration of the order (1st-, 2nd-, 3rd- and 4th-order functions are available). Furthermore, the filter offers the electronic control of the cut-off frequency of the output response. The feature of the electronic change in the approximation characteristics is investigated for the Butterworth, Bessel, Elliptic, Chebyshev and Inverse Chebyshev approximations. The design is verified by PSpice simulations and experimental measurements. The results are also supported by the transient domain response (response to the square waveform), comparison of the group delay, sensitivity analysis and implementation feasibility based on given approximation. The benefit of the proposed electronic change in the approximation characteristics feature (in general signal processing or for sensors in particular) is presented and discussed for an exemplary scenario.

This work presents a novel methodology to adjust the inductance of real coils (electronically) and to cancel out serial losses (up to tens or even hundreds of Ohms in practice) electronically. This is important in various fields of electromagnetic sensors (inductive sensors), energy harvesting, measurement and especially in the instrumentation of various devices. State-of-the-art methods do not solve the problem of cancellation of real serial resistance, which is the most important parasitic feature in low- and middle-frequency bands. In this case, the employment of serial negative resistance is not possible due to stability issues. To solve this issue, two solutions allowing the cancellation of serial resistance by the value of the passive element and an electronically adjustable parameter are introduced. The operational ranges are between 0.1 and 1 mH and 0.1 and 10 mH, valid in bandwidths from hundreds of Hz up to hundreds of kHz. The proposed concepts are experimentally tested in two applications: an electronically tunable oscillator of LC type and an electronically tunable band-pass RLC filter. The presented methodology offers significant improvements in the process of circuit design employing inductors and can be beneficially used for on-chip design, where serial resistance issues can be very significant.

The pursuit of low-cost and high-efficiency catalyst is imperative for the development and utilization of hydrogen energy. Heteroatomic doping which is conducive to the redistribution of electric density is one of the promising strategies to improve catalytic activity. Herein, the Se-doped CoS2 porous nanowires array with a superaerophobic surface was constructed on carbon fiber. Due to the electronic modulation and the unique superaerophobic structure, it showed improved hydrogen evolution activity and stability in urea-containing electrolyte. At a current density of 10 mA cm−2, the overpotentials are 188 mV for hydrogen evolution reaction (HER) and 1.46 V for urea oxidation reaction (UOR). When it was set as a cell, the voltage is low as 1.44 V. Meanwhile, the current densities of HER and UOR, as well as of cell remained basically unchanged after a continuous operation for 48 h. This work opens up a new idea for designing of cost-saving hydrogen evolution electrocatalysts.

BACKGROUND:The most used score to measure comorbidity is the Charlson index. Its application to a health care administrative database including International Classification of Diseases, 10th edition (ICD-10) codes, medical procedures, and medication required studying its properties on survival. Our objectives were to adapt the Charlson comorbidity index to the French National Health Insurance database to predict 1-year mortality of discharged patients and to compare discrimination and calibration of different versions of the Charlson index. METHODS:Our cohort included all adults discharged from a hospital stay in France in 2010 registered in the French National Health Insurance general scheme. The pathologies of the Charlson index were identified through ICD-10 codes of discharge diagnoses and long-term disease, specific medical procedures, and reimbursement of specific medications in the past 12 months before inclusion. RESULTS:We included 6,602,641 subjects at the date of their first discharge from medical, surgical, or obstetrical department in 2010. One-year survival was 94.88%, decreasing from 98.41% for Charlson index of 0–71.64% for Charlson index of ≥5. With a discrimination of 0.91 and an appropriate calibration curve, we retained the crude Cox model including the age-adjusted Charlson index as a 4-level score. CONCLUSIONS:Our study is the first to adapt the Charlson index to a large health care database including >6 million of inpatients. When mortality is the outcome, we recommended using the age-adjusted Charlson index as 4-level score to take into account comorbidities.

Atrial fibrillation (AF) is associated with substantial morbidity, in particular stroke. Despite good evidence for the reduction of stroke risk with anticoagulant therapy, there remains significant undertreatment. The main aim of the current study was to investigate whether a clinical decision support tool (CDS) for stroke prevention integrated in the electronic health record could improve adherence to guidelines for stroke prevention in patients with AF. We conducted a cluster-randomized trial where all 43 primary care clinics in the county of Östergötland, Sweden (population 444,347), were randomized to be part of the CDS intervention or to serve as controls. The CDS produced an alert for physicians responsible for patients with AF and at increased risk for thromboembolism (according to the CHA2DS2-VASc algorithm) without anticoagulant therapy. The primary endpoint was adherence to guidelines after 1 year. After randomization, there were 22 and 21 primary care clinics in the CDS and control groups, respectively. There were no significant differences in baseline adherence to guidelines regarding anticoagulant therapy between the 2 groups (CDS group 70.3% [5,186/7,370; 95% CI 62.9%-77.7%], control group 70.0% [4,187/6,009; 95% CI 60.4%-79.6%], p = 0.83). After 12 months, analysis with linear regression with adjustment for primary care clinic size and adherence to guidelines at baseline revealed a significant increase in guideline adherence in the CDS (73.0%, 95% CI 64.6%-81.4%) versus the control group (71.2%, 95% CI 60.8%-81.6%, p = 0.013, with a treatment effect estimate of 0.016 [95% CI 0.003-0.028]; number of patients with AF included in the final analysis 8,292 and 6,508 in the CDS and control group, respectively). Over the study period, there was no difference in the incidence of stroke, transient ischemic attack, or systemic thromboembolism in the CDS group versus the control group (49 [95% CI 43-55] per 1,000 patients with AF in the CDS group compared to 47 [95% CI 39-55] per 1,000 patients with AF in the control group, p = 0.64). Regarding safety, the CDS group had a lower incidence of significant bleeding, with events in 12 (95% CI 9-15) per 1,000 patients with AF compared to 16 (95% CI 12-20) per 1,000 patients with AF in the control group (p = 0.04). Limitations of the study design include that the analysis was carried out in a catchment area with a high baseline adherence rate, and issues regarding reproducibility to other regions. The present study demonstrates that a CDS can increase guideline adherence for anticoagulant therapy in patients with AF. Even though the observed difference was small, this is the first randomized study to our knowledge indicating beneficial effects with a CDS in patients with AF. ClinicalTrials.gov NCT02635685.

Qualitative Data is meant for the novice researcher who needs guidance on what specifically to do when faced with a sea of information. It takes readers through the qualitative research process, beginning with an examination of the basic philosophy of qualitative research, and ending with planning and carrying out a qualitative research study. It provides an explicit, step-by-step procedure that will take the researcher from the raw text of interview data through data analysis and theory construction to the creation of a publishable work. The volume provides actual examples based on the authors' own work, including two published pieces in the appendix, so that readers can follow examples for each step of the process, from the project's inception to its finished product. The volume also includes an appendix explaining how to implement these data analysis procedures using NVIVO, a qualitative data analysis program.

With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segmentation, can develop rapidly. Most state-of-the-art network frameworks, for instance, segmentation, are based on Mask R-CNN (mask region-convolutional neural network). However, the experimental results confirm that Mask R-CNN does not always successfully predict instance details. The scale-invariant fully convolutional network structure of Mask R-CNN ignores the difference in spatial information between receptive fields of different sizes. A large-scale receptive field focuses more on detailed information, whereas a small-scale receptive field focuses more on semantic information. So the network cannot consider the relationship between the pixels at the object edge, and these pixels will be misclassified. To overcome this problem, Mask-Refined R-CNN (MR R-CNN) is proposed, in which the stride of ROIAlign (region of interest align) is adjusted. In addition, the original fully convolutional layer is replaced with a new semantic segmentation layer that realizes feature fusion by constructing a feature pyramid network and summing the forward and backward transmissions of feature maps of the same resolution. The segmentation accuracy is substantially improved by combining the feature layers that focus on the global and detailed information. The experimental results on the COCO (Common Objects in Context) and Cityscapes datasets demonstrate that the segmentation accuracy of MR R-CNN is about 2% higher than that of Mask R-CNN using the same backbone. The average precision of large instances reaches 56.6%, which is higher than those of all state-of-the-art methods. In addition, the proposed method requires low time cost and is easily implemented. The experiments on the Cityscapes dataset also prove that the proposed method has great generalization ability.

Background Methods to accurately predict the risk of in-hospital mortality are important for applications including quality assessment of healthcare institutions and research. Objective To update and validate the Kaiser Permanente inpatient risk adjustment methodology (KP method) to predict in-hospital mortality, using open-source tools to measure comorbidity and diagnosis groups, and removing troponin which is difficult to standardize across modern clinical assays. Design Retrospective cohort study using electronic health record data from GEMINI. GEMINI is a research collaborative that collects administrative and clinical data from hospital information systems. Participants Adult general medicine inpatients at 28 hospitals in Ontario, Canada, between April 2010 and December 2022. Main Measures The outcome was in-hospital mortality, modeled by diagnosis group using 56 logistic regressions. We compared models with and without troponin as an input to the laboratory-based acute physiology score. We fit and validated the updated method using internal-external cross-validation at 28 hospitals from April 2015 to December 2022. Key Results In 938,103 hospitalizations with 7.2% in-hospital mortality, the updated KP method accurately predicted the risk of mortality. The c -statistic at the median hospital was 0.866 (see Fig. 3) (25th–75th 0.848–0.876, range 0.816–0.927) and calibration was strong for nearly all patients at all hospitals. The 95th percentile absolute difference between predicted and observed probabilities was 0.038 at the median hospital (25th–75th 0.024–0.057, range 0.006–0.118). Model performance was very similar with and without troponin in a subset of 7 hospitals, and performance was similar with and without troponin for patients hospitalized for heart failure and acute myocardial infarction. Conclusions An update to the KP method accurately predicted in-hospital mortality for general medicine inpatients in 28 hospitals in Ontario, Canada. This updated method can be implemented in a wider range of settings using common open-source tools.

This book addresses a significant gap in the research literature on transitions across the school years: the continuities and discontinuities in school literacy education and their implications for practice. Across different curriculum domains, and using social semiotic, ethnographic, and conversation-analytic approaches, the contributors investigate key transition points for individual students' literacy development, elements of literacy knowledge that are at stake at each of these points, and variability in students' experiences. Grounding its discussion in classroom voices, experiences and texts, this book reveals literacy-specific curriculum demands and considers how teachers and students experience and account for these evolving demands. The contributors include a number of established names (such as Freebody, Derewianka, Myhill, Rowsell, Moje and Lefstein), as well as emerging scholars gaining increasing recognition in the field. They draw out implications for how literacy development is theorized in school curriculum and practice, teacher education, further research and policy formation. In addition, each section of the book features a summary from an international scholar who draws together key ideas from the section and relates these to their current thinking. They deploy a range of different theoretical and methodological approaches in order to bring rich yet complementary perspectives to bear on the issue of literacy transition.

In 2020 Medicare reintroduced Alzheimer's disease and related dementias (ADRD) Hierarchical Condition Categories (HCCs) to riskadjust Medicare Advantage and accountable care organization (ACO) payments. The potential for Medicare spending increases from this policy change are not well understood because the baseline accuracy of ADRD HCCs is uncertain. Using linked 2016-18 claims and electronic health record data from a large ACO, we evaluated the accuracy of claims-based ADRD HCCs against a reference standard of clinician-adjudicated disease. An estimated 7.5 percent of beneficiaries had clinician-adjudicated ADRD. Among those with ADRD HCCs, 34 percent did not have clinicianadjudicated disease. The false-negative and false-positive rates were 22.7 percent and 3.2 percent, respectively. Medicare spending for those with false-negative ADRD HCCs exceeded that of true positives by $14,619 per beneficiary. If, after the reintroduction of risk adjustment for ADRD, all false negatives were coded as having ADRD, expenditure benchmarks for beneficiaries with ADRD would increase by 9 percent. Monitoring ADRD coding could become challenging in the setting of concurrent incentives to decrease false-negative rates and increase false-positive rates.