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India has been implementing active case-finding (ACF) for TB among marginalised and vulnerable (high-risk) populations since 2017. The effectiveness of ACF cycle(s) is dependent on the use of appropriate screening and diagnostic tools and meeting quality indicators. To determine the number of ACF cycles implemented in 2021 at national, state (  = 36) and district (  = 768) level and quality indicators for the first ACF cycle. In this descriptive study, aggregate TB program data for each ACF activity that was extracted was further aggregated against each ACF cycle at the district level in 2021. One ACF cycle was the period identified to cover all the high-risk populations in the district. Three TB ACF quality indicators were calculated: percentage population screened (≥10%), percentage tested among screened (≥4.8%) and percentage diagnosed among tested (≥5%). We also calculated the number needed to screen (NNS) for diagnosing one person with TB (≤1538). Of 768 TB districts, ACF data for 111 were not available. Of the remaining 657 districts, 642 (98%) implemented one, and 15 implemented two to three ACF cycles. None of the districts or states met all three TB ACF quality indicators' cut-offs. At the national level, for the first ACF cycle, 9.3% of the population were screened, 1% of the screened were tested and 3.7% of the tested were diagnosed. The NNS was 2824: acceptable (≤1538) in institutional facilities and poor for population-based groups. Data were not consistently available to calculate the percentage of i) high-risk population covered, ii) presumptive TB among screened and iii) tested among presumptive. In 2021, India implemented one ACF cycle with sub-optimal ACF quality indicators. Reducing the losses between screening and testing, improving data quality and sensitising stakeholders regarding the importance of meeting all ACF quality indicators are recommended.

Within the Advocacy Coalition Framework (ACF), policy-oriented learning is understood as a change in policy beliefs. Additional work has noted that belief reinforcement, not just belief change, is also a potential policy learning outcome. Yet, little work has attempted to reconcile how learning could involve both belief change and belief reinforcement. In this article, I propose a policy-oriented learning model where policy beliefs – deep core, policy core, or secondary aspects – are understood as having a distribution with a central tendency (that is, the belief) as well as variance (that is, certainty associated with the belief). With policy beliefs considered as distributions, learning can be understood as changes in beliefs (that is, a change in the central tendency) as well as changes in certainty (that is, variance), and conversely, a decrease in belief uncertainty would constitute belief reinforcement. Using data from a deliberative forum that brought together various stakeholders including experts, natural resource managers, and the public to discuss environmental issues impacting coastal communities, I explore policy-oriented learning as changes in concern regarding several key issues before and after the forum. Additionally, I examine the association between concern following the forum and self-reported learning. I find support for the proposed policy-oriented learning model as shown by significant changes in average concern as well as average variance among participants across several of the issues discussed. In this way, the article makes a theoretical contribution to the ACF literature by testing the use of distributions to assess policy learning.

Spot detection has attracted continuous attention for laser sensors with applications in communication, measurement, etc. The existing methods often directly perform binarization processing on the original spot image. They suffer from the interference of the background light. To reduce this kind of interference, we propose a novel method called annular convolution filtering (ACF). In our method, the region of interest (ROI) in the spot image is first searched by using the statistical properties of pixels. Then, the annular convolution strip is constructed based on the energy attenuation property of the laser and the convolution operation is performed in the ROI of the spot image. Finally, a feature similarity index is designed to estimate the parameters of the laser spot. Experiments on three datasets with different kinds of background light show the advantages of our ACF method, with comparison to the theoretical method based on international standard, the practical method used in the market products, and the recent benchmark methods AAMED and ALS.

Quantification and prediction of drought events are important for planning and management of water resources in coping with climate change scenarios at global and local scales. In this study, heuristic approaches including Co-Active Neuro Fuzzy Inference System (CANFIS), Multi-Layer Perceptron Neural Network (MLPNN) and Multiple Linear Regression (MLR) were utilized to predict the hydrological drought based on multi-scalar Streamflow Drought Index (SDI) at Naula and Kedar stations located in upper Ramganga River basin, Uttarakhand State, India. The SDI was calculated on 1-, 3-, 6-, 9-, 12- and 24-month time scales (SDI-1, SDI-3, SDI-6, SDI-9, SDI-12, and SDI-24) using monthly streamflow data of 33 years (1975-2007). The significant input variables (lags) for CANFIS, MLPNN, and MLR models were derived using autocorrelation and partial autocorrelation functions (ACF &PACF) at 5% significance level on SDI-1, SDI-3, SDI-6, SDI-9, SDI-12 and SDI-24 data series. The predicted values of multi-scalar SDI using CANFIS, MLPNN and MLR models were compared with the calculated values, based on root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of correlation (COC) and Willmott index (WI). The visual interpretation was also made using line diagram, scatter diagram and Taylor diagram (TD). The results of analysis revealed that the performance of CANFIS models was the best for hydrological drought prediction at 3-, 6- and 12-month time scales for Naula station, and at 1-, 3-, 12- and 24-month time scales for Kedar station; while MLPNN was the best at 1- and 9-month time scales for Naula station, and at 6- and 9-month time scales for Kedar station. The MLR model was found to be the best at 24-month time scale for Naula station only. The results of this study could be helpful in prediction of hydrological drought on multiple time scales and decision making for remedial schemes to cope with hydrological drought at Naula and Kedar stations.

Electrostatic monitoring technology for aero-engines has demonstrated considerable capability in early fault warning. However, raw electrostatic signals often contain significant noise and exhibit low signal-to-noise ratios, making denoising essential to improve the accuracy of fault-related information extraction. To address the issue of coupled noise in electrostatic signals, this study introduces methodologies based on Improved Complete Ensemble EMD with Adaptive Noise (ICEEMDAN), Autocorrelation Function (ACF), and Wavelet Soft-thresholding (WTD). We investigate the criteria and principles for screening intrinsic mode functions (IMFs) and propose a joint denoising algorithm, along with its specific procedure, based on IMF-optimized reconstruction and wavelet thresholding. The proposed method is validated using both simulated signals and actual electrostatic signals collected from a micro-turbojet engine test. Comparisons with other denoising techniques are conducted. Simulation results indicate that the proposed method improves denoising performance in terms of signal-to-noise ratio (SNR), mean square error (MSE), and normalized cross-correlation (NCC). Test results further demonstrate that the method effectively suppresses random noise and power frequency interference while preserving useful abnormal particle signals more effectively.

This article critically examines the Reno Model responsible gambling undertaking: its evolution, core ideological beliefs and promotion in four internationally influential journal articles, published between 2004 and 2015. This discourse has framed the international RG policy landscape for over a decade; emphasising individualised responsibility for harms and providing governments with justifications for compromised RG regulation. Axioms of the Reno Model are individual responsibility, framed as personal control and autonomy for informed choice and a focus on problem gamblers who manifest clinical symptoms of impaired control. Drawing on corporate political activity (CPA) analysis, regulatory-avoidance framing strategies of the gambling industry include shaping the evidence base, policy substitution (voluntary industry operator codes of conduct and problem gambler treatment programs) and assertions of insufficient evidence for introducing reforms. Barriers to ethical RG standards include deception and exploitation, faulty regulation and grim working conditions in gambling environments, along with Reno Model adherents’ dismissal of contradictory evidence. The critique proposes a shift in the dominant regulatory Model from industry self-regulation under self-monitored codes of practice to RG-Consumer Protection that addresses structural issues of power and vested interests, featuring core principles of public health, consumer protection, operator duty of care, regulatory transparency and independent research.

Heart failure (HF) has been declared as global pandemic and current therapies are still ineffective, especially in patients that develop concurrent cardio-renal syndrome. Considerable attention has been focused on the nitric oxide (NO)/soluble guanylyl cyclase (sGC)/cyclic guanosine monophosphate (cGMP) pathway. In the current study, we aimed to investigate the effectiveness of sGC stimulator (BAY41-8543) with the same mode of action as vericiguat, for the treatment of heart failure (HF) with cardio-renal syndrome. As a model, we chose heterozygous Ren-2 transgenic rats (TGR), with high-output heart failure, induced by aorto-caval fistula (ACF). The rats were subjected into three experimental protocols to evaluate short-term effects of the treatment, impact on blood pressure, and finally the long-term survival lasting 210 days. As control groups, we used hypertensive sham TGR and normotensive sham HanSD rats. We have shown that the sGC stimulator effectively increased the survival of rats with HF in comparison to untreated animals. After 60 days of sGC stimulator treatment, the survival was still 50% compared to 8% in the untreated rats. One-week treatment with sGC stimulator increased the excretion of cGMP in ACF TGR (109 ± 28 nnmol/12 h), but the ACE inhibitor decreased it (-63 ± 21 nnmol/12 h). Moreover, sGC stimulator caused a decrease in SBP, but this effect was only temporary (day 0: 117 ± 3; day 2: 108 ± 1; day 14: 124 ± 2 mmHg). These results support the concept that sGC stimulators might represent a valuable class of drugs to battle heart failure especially with cardio-renal syndrome, but further studies are necessary.

Achieving both accurate and real-time monitoring heartbeat signals by non-invasive sensing techniques is challenging due to various noise interferences. In this paper, we propose an enhanced discrete wavelet transform (DWT) method that incorporates objective denoising quality assessment metrics to determine accurate thresholds and adaptive threshold functions. Our approach begins by denoising ECG signals from various databases, introducing several types of typical noise, including additive white Gaussian (AWG) noise, baseline wandering noise, electrode motion noise, and muscle artifacts. The results show that for Gaussian white noise denoising, the enhanced DWT can achieve 1–5 dB SNR improvement compared to the traditional DWT method, while for real noise denoising, our proposed method improves the SNR tens or even hundreds of times that of the state-of-the-art denoising techniques. Furthermore, we validate the effectiveness of the enhanced DWT method by visualizing and comparing the denoising results of heartbeat signals monitored by fiber-optic micro-vibration sensors against those obtained using other denoising methods. The improved DWT enhances the quality of heartbeat signals from non-invasive sensors, thereby increasing the accuracy of cardiovascular disease diagnosis.