Regularization Procedure Research Articles

Access to Healthcare in Rural Communities: A Bibliometric Analysis.

Access to healthcare is defined by being able to receive regular checkups, specialist appointments, immunizations, procedures, and surgeries within a reasonable timeframe and without restrictive barriers. The Web of Science database was used to collect 4,745 articles published relating to the keywords "rural", "access", and "healthcare". These articles then underwent bibliometric cataloging by country, journal, and keyword using VOSviewer 1.6.19. Between the years 2015 and 2022, the United States emerged as the predominant publisher in the field. From 2015 to 2019, the United States accounted for 47 percent of the total articles published. However, in the subsequent years of 2020 to 2022, there was a slight decline of 3 percent in the volume of U.S. publications. Australia secured the second position in terms of publications within the aforementioned timeframe of 2015 to 2022. England had the third most publications during the time between 2015 to 2022. The prevalence of articles containing the keyword "disparity" ranged between 7 percent and 12 percent throughout this period. Plus One exhibited prominence by contributing 5.025381 percent of all publications pertaining to access to rural medicine during the years 2015 to 2019 thereby establishing itself as the leading publisher in this domain. However, in 2020, the International Journal of Environment suppressed Plus One by publishing the highest number of articles related to rural healthcare. Subsequently, the BMC Health Services Research claimed the leading position in the years 2021 to 2022. An exploration of who is contributing to the discussion of access to healthcare in rural communities shows that the field and number of publications is growing, and that a focus on disparities is emerging, creating an environment surrounding rural health focused on researching equitable care.

Reconstruction of incidence reporting rate for SARS-CoV-2 Delta variant of COVID-19 pandemic in the US

In recent years, advanced regularization techniques have emerged as a powerful tool aimed at stable estimation of infectious disease parameters that are crucial for future projections, prevention, and control. Unlike other system parameters, i.e., incubation and recovery rates, the case reporting rate, Ψ, and the time-dependent effective reproduction number, Re(t), are directly influenced by a large number of factors making it impossible to pre-estimate these parameters in any meaningful way. In this study, we propose a novel iteratively-regularized trust-region optimization algorithm, combined with SuSvIuIvRD compartmental model, for stable reconstruction of Ψ and Re(t) from reported epidemic data on vaccination percentages, incidence cases, and daily deaths. The innovative regularization procedure exploits (and takes full advantage of) a unique structure of the Jacobian and Hessian approximation for the nonlinear observation operator. The proposed inversion method is thoroughly tested with synthetic and real SARS-CoV-2 Delta variant data for different regions in the United States of America from July 9, 2021, to November 25, 2021. Our study shows that case reporting rate during the Delta wave of COVID-19 pandemic in the US is between 12% and 37%, with most states being in the range from 15% to 25%. This confirms earlier accounts on considerable under-reporting of COVID-19 cases due to the impact of ”silent spreaders” and the limitations of testing.

An Updated Review on the Chemistry, Biological Characteristics and Analytical Techniques of Dapagliflozin

Background: Globally, type 2 diabetes mellitus (T2DM) prevalence is increasing. A patient must have lifetime therapy for diabetes to manage it and prevent any complications. There are many different medications that can be used to treat Type 2 diabetes. Still, almost all of them concentrate on the declining insulin sensitivity and secretion that are associated with the onset of the illness. Methods: There is growing interest in the development of innovative anti-diabetic medications that are not insulin-reliant because treatments with such insulin-dependent mechanisms of action usually lose their effectiveness over time. One such technique is the inhibition of renal glucose reuptake. Results: Dapagliflozin, the first line of selective sodium-glucose cotransporter 2 inhibitors that re-duce renal glucose reabsorption, is currently being developed as a therapy for Type 2 diabetes. Numerous analytical techniques have been developed for its detection, measurement, and regular quality control procedures. Conclusion: This review deliberates a thorough discussion on the chemistry of Dapagliflozin, all of its pharmacological actions with analytical and bioanalytical analyses, and more information on the clinical trials.

Flexible Robust Control Strategy for Synchronization of Uncertain Non-Linear Systems with Control Input Non-Linearity

A robust global fuzzy sliding mode controller is designed in this paper for the synchronization of non-linear systems with control input non-linearities (CIN) and uncertainties. A consistent global fuzzy sliding mode control (GFSMC) law is developed, which guarantees the suppression of the reaching phase and the presence of the sliding phase from the initial time. Chattering phenomenon, which is characteristic of customary sliding mode control (SMC), avoided by the on-line fuzzy regulation of the sliding surface in the controller, when the system is subject to disturbances and CIN. Finite-time boundedness (FTB) properties are designed with adequate conditions, which are entrenched in terms of linear matrix inequalities (LMIs) with the help of cost and Lyapunov functions. Numerical simulations for the synchronization problem of the chaotic modified Colpitt’s system and Duffing system clearly show the good performance of the proposed control scheme. The present work provides a regular procedure to design GFSMC for a class of non-linear systems with CIN.

Product contamination during mechanochemical synthesis of praziquantel co-crystal, polymeric dispersion and cyclodextrin complex

This paper aims to evaluate the product contamination by elemental impurities during the mechanochemical synthesis of praziquantel (PZQ) co-crystal, polymeric dispersion and cyclodextrin complex by grinding. To assess that, PZQ was co-ground with malic acid (MA), Poloxamer F-127 (F-127) and hydroxypropyl-β-cyclodextrin (HPβCD) in high-energy vibrational mills using stainless steel and agate grinding tools, applying different processing time (30 and 90 min). Differential scanning calorimetry and X-ray powder diffraction confirmed the formation of the targeted products, regardless of applied processing time and grinding tool type. After digestion of the solid powder products, the levels of selected elemental impurities were analysed by inductively coupled plasma mass spectrometry (ICP-MS). The analysis revealed that the content of Mg, Ca, and V are below the limit of quantification in all samples analysed. The contents of P and Na are not related to the type of ball mill and reaction time, but to the starting materials themselves, considering that Na is found in HPβCD and MA, while P was found in F-127. The detected Si impurities in the co-ground products can be related to the use of the agate balls and jars, while the presence of Cr and Fe can be related to the use of the stainless steel grinding tools. The risk assessment showed that the oral administration of the prepared co-ground products in quantities corresponding to regular PZQ oral doses resulted in only insignificant exposure to Cr. Finally, the use of agate grinding tools should be preferred, as administration of such products results in lower Cr exposure. The presented elemental impurities did not lead to any significant drug degradation as PZQ content at the end of the six-month testing period was still in the range of 95–105 % of the initial content. Regardless, ICP-MS analysis of the elemental impurities should be considered in regular quality control procedures in the development and production of novel pharmaceutical products prepared by grinding.

A Regularized Tseng Method for Solving Various Variational Inclusion Problems and Its Application to a Statistical Learning Model

We study three classes of variational inclusion problems in the framework of a real Hilbert space and propose a simple modification of Tseng’s forward-backward-forward splitting method for solving such problems. Our algorithm is obtained via a certain regularization procedure and uses self-adaptive step sizes. We show that the approximating sequences generated by our algorithm converge strongly to a solution of the problems under suitable assumptions on the regularization parameters. Furthermore, we apply our results to an elastic net penalty problem in statistical learning theory and to split feasibility problems. Moreover, we illustrate the usefulness and effectiveness of our algorithm by using numerical examples in comparison with some existing relevant algorithms that can be found in the literature.

Tomato Plant Leaf Disease Detection Using Convolutional Neural Network

Our country is growing country, India is mostly depended on the agriculture field, and Agriculture is pillar of our country’s economy. Because of this India has to improve in agriculture filed, in many of situations’ farmer face difficulties to detect crop disease in a case large area farm, or late in detection of crop disease in such cases farmer lose their all crop and face the huge loss, to avoid this, in agricultural field is detection of plant diseases is very important. And it is very difficult tasks. In regular normal procedure it requires a more time period and trained person to detect accurate plant disease. In this paper, we proposed effective way for plant disease detection using computer science and machine learning model. Disease transmission from unhealthy plant to all other healthy plants in farm is one of the major damages to crop farm. And these diseases spread like forest fire and have the possible to impact the whole operation if not identified in early on. Now Plant disease detection methods helps to identify infected plants in a very early stage and also help us to identify plant disease in a wide range of area of crops in a cost-effective manner. The aim of this project model is to implement machine learning models, in our proposed system we take a plant leaf image on that leaf images we predict the plant disease using Convolution Neural Networks (CNN) model, in that we build a such a model to predict the plant disease with maximum accuracy and it is for plant disease detection for tomato plant. This machine learning model is analyzing different image metrics pixels data to determine the best performance of network. For that dataset were used around 7around 8016 images we going to use to train the model. We going to use 14 layers CNN models to get better accuracy results. Model consist various layers like convolution, pooling, flatten and dense. Early two layers that is preprocessing and augmentation of images Finally, we get the result of which disease that plant have.

Introduction of Digital Microscope and Near Eye Screens with Augmented Reality and Virtual Reality in Regular Dental Procedures

Usage of VR (Virtual Reality) in Dentistry is limited to patient wearing the viewer to see a video to calm oneself down while undergoing fearful dental procedures. But it was never worn by the dentist himself, to view and do the dental procedure. The main principle of the study is to increase the magnification of the restricted area of oral cavity using a digital microscope. Digital microscopes of 2 Megapixels and 5 Megapixels, were used by the authors to view dental procedures while doing through smartphone supported Augmented Reality (AR) and Virtual Reality (VR) glasses. It is important to note that using digital microscope in doing regular dental procedures viewing through AR/VR glasses were so far not reported. But we were careful in establishing the success of this new concept and are delighted to share for further advancement. At the same time, there are bottlenecks which are listed as limitations that can be further modified for success, which has to be developed by the manufacturers involved in research and manufacturing of dental equipments. Hence this article envisage the Dental Chair manufacturers should roll out every dental unit with built in maneuverable digital microscope, software and Near Eye Screen that will be a boon for the patient and the dentist.

A rotating modified JNW spacetime as a Kerr black hole mimicker

AbstractThe Event Horizon Telescope has recently observed the images and shadows of the compact objects M87* and Sgr A* at the centres of the galaxies Messier 87 and Milky Way. This has opened up a new window in observational astronomy to probe and test gravity and fundamental physics in the strong-field regime. In this paper, we construct a rotating version of a modified Janis-Newman-Winicour metric obtained through the Simpson-Visser regularisation procedure and constrain the metric parameters using the observed shadows of M87* and Sgr A*. Depending on parameter values, the spacetime metric represents either a naked singularity or a wormhole. We find that the naked singularity case is not consistent with observations, as it casts a shadow that is much smaller than the observed ones. On the other hand, the shadow formed by the wormhole branch, depending on the parameter values, is consistent with the observations. We put constraints on the wormhole throat radius by comparing the shadow with the observed ones of M87* and Sgr A*.

A new method that automatically regularizes scattering amplitudes

We present a new regularization procedure called autoregularization. The new procedure regularizes the divergences, encountered previously in a scattering process, using the intrinsic scale of the process. We use autoregularization to calculate the amplitudes of several scattering processes in QED and compare the calculations with experimental measurements over a broad range of center-of-momentum energies (≲MeV to ≳200 GeV). The calculated amplitudes are found to be in good agreement with experimental data. 1 1 Specifically, the O(α) correction to electron’s gyromagnetic ratio predicted by autoregularization agrees with experimental measurement to within 0.06% (Section ), which is to be compared to Schwinger’s O(α) correction which agrees with experimental measurement to within 0.15%; the O(α) estimate of the Lamb shift predicted by autoregularization agrees with the experimental measurements to within 0.33% (section ); the running fine structure constant predicted by autoregularization at O(α) agrees with the prediction of cutoff regularization to within 0.8% over one to four orders of magnitude above the electron’s mass scale (section ); the tree-level prediction of autoregularization for Compton scattering is in better agreement with experimental data than the prediction of the well-known Klein-Nishina formula by about 4.02% (section ); the tree-level predicition of autoregularization for pair annihilation at center-of-momentum energy of 206.671 GeV agrees with the experimental data about 0.67 % better than the prediction of the standard QED (section ). To test autoregularization in a non-Abelian gauge theory, we calculate the QCD coupling constant at 1-loop and show that, like the known regularization schemes, autoregularization also predicts asymptotic freedom in QCD. Finally, we show that the vacuum energy density of the free fields in the Standard Model, calculated using autoregularization, is smaller than the current estimate of the cosmic critical density.

Assessment of the partial saddle point approximation in field-theoretic polymer simulations.

Field-theoretic simulations are numerical treatments of polymer field theory models that go beyond the mean-field self-consistent field theory level and have successfully captured a range of mesoscopic phenomena. Inherent in molecularly-based field theories is a "signproblem" associated with complex-valued Hamiltonian functionals. One route to field-theoretic simulations utilizes the complex Langevin (CL) method to importance sample complex-valued field configurations to bypass the signproblem. Although CL is exact in principle, it can be difficult to stabilize in strongly fluctuating systems. An alternate approach for blends or block copolymers with two segment species is to make a "partial saddle point approximation" (PSPA) in which the stiff pressure-like field is constrained to its mean-field value, eliminating the signproblem in the remaining field theory, allowing for traditional (real) sampling methods. The consequences of the PSPA are relatively unknown, and direct comparisons between the two methods are limited. Here, we quantitatively compare thermodynamic observables, order-disorder transitions, and periodic domain sizes predicted by the two approaches for a weakly compressible model of AB diblock copolymers. Using Gaussian fluctuation analysis, we validate our simulation observations, finding that the PSPA incorrectly captures trends in fluctuation corrections to certain thermodynamic observables, microdomain spacing, and location of order-disorder transitions. For incompressible models with contact interactions, we find similar discrepancies between the predictions of CL and PSPA, but these can be minimized by regularization procedures such as Morse calibration. These findings mandate caution in applying the PSPA to broader classes of soft-matter models and systems.

On constraining 3D seismic anisotropy in subduction, mid-ocean-ridge, and plume environments with teleseismic body wave data

Conventional seismic tomography studies consider the Earth’s interior as mechanically isotropic, despite seismic anisotropy being widely observed. This current standard approach to seismic imaging is likely to lead to significant artefacts in tomographic images with first-order effects on interpretations and hinders the quantitative integration of seismology with geodynamic flow models. Although a few methodologies have been proposed for carrying out anisotropic tomography, their ability in simultaneously recovering isotropic and anisotropic structures has not been rigorously tested. In this contribution we use geodynamic and seismological modeling to predict the elastic properties and synthetic teleseismic P- and S-wave travel-time datasets for three different tectonic settings: a plume rising in an intraplate setting, a divergent margin, and a subduction zone. Subsequently, we perform seismic anisotropy tomography testing a recently developed methodology that allows for the inversion of an arbitrarily oriented weakly anisotropic hexagonally symmetric medium using multiple body-wave datasets. The tomography experiments indicate that anisotropic inversions of separate and joint P- and S-wave travel-times are capable of recovering the first order isotropic velocity anomalies and anisotropic patterns. In particular, joint P- and S-wave anisotropic inversions show that by leveraging both phases it is possible to greatly mitigate issues related to imperfect data coverage common in seismology and reduce parameter trade-offs. In contrast, by neglecting seismic anisotropy, isotropic tomographic models provide no information on the mantle fabrics and in all cases are contaminated by strong velocity artifacts. In the inversions the magnitude of anisotropy (as well as that of seismic anomalies) is always underestimated owing to regularization procedures and smearing effects. It follows that the true seismic anisotropy of mantle rocks is likely higher than estimated from anisotropic tomographies, and more consistent with predictions from laboratory and numerical micro-mechanical experiments. Altogether, these results suggest that anisotropic body-wave tomography could provide unprecedented information about the Earth’s deep geological structure, and that the latter could be better recovered by complementing teleseismic body-wave travel-times with other geophysical datasets.

Zero-noise dynamics after collapse for three point vortices

The continuation of point vortex dynamics after a vortex collapse is investigated by means of a regularization procedure consisting in introducing a small stochastic diffusive term, that corresponds to a vanishing viscosity. In contrast with deterministic regularization, in which a cutoff interaction selects in the limit a single trajectory of the system after collapse, the zero-noise method produces a probability distribution supported by trajectories satisfying relevant conservation laws of the point vortex system.

'Ironing out the risk': Assessing the effect of plateletpheresis donation frequency on iron stores in South-Asian male donors.

Repeated blood donation is a well-known cause of iron deficiency among donors. However, present scientific literature lacks comprehensive evidence regarding the impact of regular plateletpheresis procedures on body iron reserves. In this study, we aimed to detect and correlate iron deficiency (using iron indices) with the frequency of platelet donations. Additionally, we also analysed the correlation between other iron and haematological indices with serum ferritin to determine cost-effective parameters that may serve as an initial screening approach to determine which donors should be subjected to serum ferritin testing. A total of 180 male participants from our platelet donor registry were enrolled in this observational cross-sectional study. Enrolment questionnaires were administered to eligible donors, and biological samples were collected during plateletpheresis donation. Biological tests such as complete blood count, reticulocyte indices, iron indices, vitamin B12 and folate were performed. Donors with ≥12 donations per year showed the highest prevalence of low ferritin (serum ferritin: 15-30 ng/mL) and absent iron stores (serum ferritin <15 ng/mL) (41.3% and 26.7%, respectively). Ferritin showed a significant negative correlation with recent (r = -0.346) and lifetime donations (r = -0.196). The efficacy of other indices for identifying iron depletion was much better using a serum ferritin value <15 ng/mL. Regular plateletpheresis donations can lead to varying severities of non-anaemic iron deficiency. Blood centres must regularly monitor frequent plateletpheresis donors (especially donors with more than 11 donations in a calendar year) and ideally maintain their serum ferritin above 30 ng/mL.

Robust adaptive variable selection in ultra-high dimensional linear regression models

We consider the problem of simultaneous variable selection and parameter estimation in an ultra-high dimensional linear regression model. The adaptive penalty functions are used in this regard to achieve the oracle variable selection property with simpler assumptions and lesser computational burden. Noting the non-robust nature of the usual adaptive procedures (e.g. adaptive LASSO) based on the squared error loss function against data contamination, quite frequent with modern large-scale data sets (e.g. noisy gene expression data, spectra and spectral data), in this paper, we present a new adaptive regularization procedure using a robust loss function based on the density power divergence (DPD) measure under a general class of error distributions. We theoretically prove that the proposed adaptive DPD-LASSO estimator of the regression coefficients is highly robust, consistent, asymptotically normal and leads to robust oracle-consistent variable selection under easily verifiable assumptions. Numerical illustrations are provided for the mostly used normal and heavy-tailed error densities. Finally, the proposal is applied to analyse an interesting spectral dataset, in the field of chemometrics, regarding the electron-probe X-ray microanalysis (EPXMA) of archaeological glass vessels from the 16th and 17th centuries.

Determination of the distribution of relaxation times through Loewner framework: A direct and versatile approach

In this work, a direct data-driven approach is employed to extract the distribution of relaxation times (DRT) from impedance data. The proposed procedure is based on the Loewner framework, that is a data driven methodology with theoretical basis in the linear system theory. One of the main advantages is that the DRT can be computed without need of regularization procedures or iterative optimization algorithms. We test the Loewner framework based algorithm on synthetic impedance data generated through equivalent circuit models widely used for modeling electrochemical systems. Impedance data set including noise are also processed. In addition, experimental electrochemical impedance spectroscopy (EIS) data are analyzed. The study shows a significant improvement in the accuracy and a high versatility of the methodology. However, the presence of significant amount of noise compromises the reliability of the obtained DRT due to the occurrence of artifact peaks. Some recommendations how to deal with noisy data are discussed.

Line-tension limits for line singularities and application to the mixed-growth case

We study variational models for dislocations in three dimensions in the line-tension scaling. We present a unified approach which allows to treat energies with subquadratic growth at infinity and other regularizations of the singularity near the dislocation lines. We show that the asymptotics via Gamma convergence is independent of the specific choice of the energy and of the regularization procedure.

Integrated partially linear model for multi-centre studies with heterogeneity and batch effect in covariates

Multi-centre study is increasingly used for borrowing strength from multiple research groups to obtain reproducible study findings. Regression analysis is widely used for analysing multi-group studies, however, some of the regression predictors are nonlinear and/or often measured with batch effects. Also, the group compositions are potentially heterogeneous across different centres. The conventional pooled data analysis can cause biased regression estimates. This paper proposes an integrated partially linear regression model (IPLM) to account for predictor's nonlinearity, general batch effect, group composition heterogeneity, and potential measurement-error in covariates simultaneously. A local linear regression-based approach is employed to estimate the nonlinear component and a regularization procedure is introduced to identify the predictors' effects. The IPLM-based method has estimation consistency and variable-selection consistency. Moreover, it has a fast computing algorithm and its effectiveness is supported by simulation studies. A multi-centre Alzheimer's disease research project is provided to illustrate the proposed IPLM-based analysis.

Phakic intraocular Lens implantation in keratoconus patients.

Both the anterior chamber and posterior chamber phakic intraocular lenses (pIOLs) implantation are acceptable refractive surgical approaches in keratoconus patients with high anisometropia, contact lens intolerance, or who prefer spectacle and contact lens independent. They are beneficial for correcting anisometropia in stable keratoconus cases or following corneal procedures such as intrastromal corneal ring segments (ICRS), collagen cross-linking (CXL), and keratoplasty. They are suitable for eyes without advanced keratoconus with acceptable best-corrected distance visual acuity (BCDVA) or without highly irregular astigmatism, high comma, and higher-order aberrations (HOAs). Combined procedures for irregular astigmatism reduction and corneal regularization with either ICRS or topography/wavefront-guided transepithelial PRK (with or without CXL) can be associated in advance with pIOLs implantation to improve BCDVA in these cases. To study and report the evidence regarding the safety and efficacy of pIOLs for KC patients' visual and refractive rehabilitation, we have analyzed the scientific evidence published within the last 10 years (from 2012 onwards). No randomized controlled trials but only eleven retrospective case series and two prospective case series were identified. Satisfactory visual rehabilitation was achieved regarding uncorrected and corrected distance visual acuity (CDVA) and predictability of the refractive correction. Both types of pIOL (iris claw and posterior chamber pIOLs) offer very good results in terms of safety and efficacy with indexes close to or even exceeding 1. pIOLs implantation is a valid refractive therapeutic approach for correcting stable keratoconus with moderate-to-high refractive errors, especially anisometropia associated with regular or mildly irregular astigmatism, and good CDVA.

3D Scan of Hardness Imprints for the Non-destructive In-Situ Structural Assessment of Operated Metal Components

The structural integrity of operated components can be assessed by non-destructive mechanical tests performed in-situ with portable instruments. Particularly promising in this context are small scale hardness tests supplemented by the mapping of the residual imprints left on metal surfaces. The data thus collected represent the input of inverse analysis procedures, which determine the material characteristics and their evolution over time. The reliability of these estimates depends on the accuracy of the geometry scans and on the robustness of the data filtering and interpretation methodologies. The objective of the present work is to evaluate the accuracy of the 3D reconstruction of the residual deformation produced on metals by hardness tests performed at a few hundred N load. The geometry data are acquired by portable optical microscopes with variable focal distance. The imperfections introduced by the imaging system, which may not be optimized for all ambient conditions when used in automatic mode, are analysed. Representative examples of the output produced by the scanning tool are examined, focusing attention on the experimental disturbances typical of onsite applications. Proper orthogonal decomposition and data reduction techniques are applied to the information returned by the instrumentation. The essential features of the collected datasets are extracted and the main noise is removed. The results of this investigation show that the accuracy achievable with the considered equipment and regularization procedures can support the development of reliable diagnostic analyses of metal components in existing structures and infrastructures.