Local Average Research Articles

Community detection using Jaya optimization algorithm based on deep learning methods and KNN graph-based clustering

Purpose In this paper, community identification has been considered as the most critical task of social network analysis. The purpose of this paper is to organize the nodes of a given network graph into distinct clusters or known communities. These clusters will therefore form the different communities available within the social network graph. Design/methodology/approach To date, numerous methods have been developed to detect communities in social networks through graph clustering techniques. The k-means algorithm stands out as one of the most well-known graph clustering algorithms, celebrated for its straightforward implementation and rapid processing. However, it has a serious drawback because it is insensitive to initial conditions and always settles on local optima rather than finding the global optimum. More recently, clustering algorithms that use a reciprocal KNN (k-nearest neighbors) graph have been used for data clustering. It skillfully overcomes many major shortcomings of k-means algorithms, especially about the selection of the initial centers of clusters. However, it does face its own challenge: sensitivity to the choice of the neighborhood size parameter k, which is crucial for selecting the nearest neighbors during the clustering process. In this design, the Jaya optimization method is used to select the K parameter in the KNN method. Findings The experiment on real-world network data results show that the proposed approach significantly improves the accuracy of methods in community detection in social networks. On the other hand, it seems to offer some potential for discovering a more refined hierarchy in social networks and thus becomes a useful tool in the analysis of social networks. Originality/value This paper introduces an enhancement to the KNN graph-based clustering method by proposing a local average vector method for selecting the optimal neighborhood size parameter k. Furthermore, it presents an improved Jaya algorithm with KNN graph-based clustering for more effective community detection in social network graphs.

Appropriateness of antibiotic prescribing in paediatrics: retrospective controlled study assessing a multifaceted intervention in Northern Italy in a 7-year period

BackgroundAcademic detailing, audit and feedback, and peer comparison have been advocated as effective ways to promote appropriateness of prescribing and antimicrobial stewardship (AMS). This study explored the effectiveness of a...

Associations between local 30-day averages of weather variables and notifications of Shiga toxin-producing E. coli in Aotearoa New Zealand 2010-2019

Associations between local 30-day averages of weather variables and notifications of Shiga toxin-producing E. coli in Aotearoa New Zealand 2010-2019

Robust tests for equality of regression curves based on characteristic functions

This paper focuses on the problem of testing the null hypothesis that the regression functions of several populations are equal under a general nonparametric homoscedastic regression model. It is well known that linear kernel regression estimators are sensitive to atypical responses. These distorted estimates will influence the test statistic constructed from them so the conclusions obtained when testing equality of several regression functions may also be affected. In recent years, the use of testing procedures based on empirical characteristic functions has shown good practical properties. For that reason, to provide more reliable inferences, we construct a test statistic that combines characteristic functions and residuals obtained from a robust smoother under the null hypothesis. The asymptotic distribution of the test statistic is studied under the null hypothesis and under root − n contiguous alternatives. A Monte Carlo study is performed to compare the finite sample behaviour of the proposed test with the classical one obtained using local averages. The reported numerical experiments show the advantage of the proposed methodology over the one based on Nadaraya–Watson estimators for finite samples. An illustration to a real data set is also provided and enables to investigate the sensitivity of the p − value to the bandwidth selection.

Glaucomatous Focal Perfusion Loss in the Macula Measured by Optical Coherence Tomographic Angiography

Purpose: To measure low perfusion area (LPA) and focal perfusion loss (FPL) in the macula using OCT angiography (OCTA) for glaucoma. DesignProspective, cross-sectional “case-control” comparison study. Methods: A total of 60 patients with primary open-angle glaucoma (POAG) and 37 normal participants were analyzed. AngioVue 6 × 6-mm high-definition (400 × 400 transverse pixels) macular OCTA scans were performed on one eye of each participant. Flow signal was calculated using the split-spectrum amplitude-decorrelation angiography algorithm. En face ganglion cell layer plexus (GCLP) and superficial vascular complex (SVC) images were generated. Using custom software, vessel density (VD) maps were obtained by computing the fraction of area occupied by flow pixels after low-pass filtering by local averaging 41 × 41 pixels. LPA was defined by local VD below 0.5 percentile over a contiguous area above 98.5 percentile of the normal reference population. The FPL was the percent VD loss (relative to normal mean) integrated over the LPA. Results: Among patients with POAG, 30 had perimetric and 30 had pre-perimetric glaucoma. The LPAGCLP-VD was 0.16±0.38 mm2 in normal and 5.78±6.30 mm2 in glaucoma subjects (P<0.001). The FPLGCLP-VD was 0.20%±0.47% in normal and 7.52%±8.84% in glaucoma subjects (P<0.001). The perimetric glaucoma diagnostic accuracy, measured by the area under the receiver operating curve, was 0.993 for LPAGCLP-VD and 0.990 for FPLGCLP-VD. The sensitivities were 96.7% and 93.3% at 95% specificity, respectively. The LPAGCLP-VD and FPLGCLP-VD had good repeatability (0.957 and 0.952 by intraclass correlation coefficient). Diagnostic accuracy was better than GCLP VD (AROC 0.950, sensitivity 83.3%) and OCT ganglion cell complex (GCC) thickness (AROC 0.927, sensitivity 80.0%), GCC focal loss volume (AROC 0.957, sensitivity 80.0%). The LPAGCLP-VD and FPLGCLP-VD correlated well with central VF mean deviations (Pearson's r=-0.716 and -0.705 respectively, both P<0.001). Conclusion: Assessment of macular focal perfusion loss using OCTA is useful in evaluating glaucomatous damage.

Overcoming local extrema in torque-actuated source seeking using the divergence theorem and delay

We study the problem of seeking the source of a scalar signal with a force- and torque-controlled unicycle. The vehicle does not have the capability to measure or track its position. The only information about the vehicle’s position relative to the source is given by measurements of the signal. We propose a feedback law that induces a uniform forward motion and an almost uniform rotational motion. This in turn leads to an almost circular motion of the unicycle; i.e., a motion along the boundary of a disk. An application of the divergence theorem reveals that the measurements of the signal along the boundary of the disk provide access to the gradient of an averaged signal, where the average is taken over the entire disk. Such a local average of the sensed signal can eliminate undesired local extrema, which in turn is beneficial for the goal of reaching a global extremum. We prove practical asymptotic stability for the closed-loop system with respect to extrema of the averaged signal. Our theoretical analysis takes L∞-measurement errors of the signal into account. We compare the performance of the proposed control law to another source seeking method in the presence of local extrema through numerical simulations.

Learning the Appropriate Local Averaging Length for Traffic Speed Deflectometer Deflection Velocity Measurements: Application to the Analysis of Rigid Pavement Joint Responses

Learning the Appropriate Local Averaging Length for Traffic Speed Deflectometer Deflection Velocity Measurements: Application to the Analysis of Rigid Pavement Joint Responses

Variable selection for semivarying coefficient models via local averaging

AbstractThis study aims to provide novel insights into variable selection in the semivarying coefficient model. We focus on the problem of variable selection and screening for the constant coefficient part. A common approach in the existing literature is to infer the constant coefficients by transforming the problem into a linear model scenario, utilizing a fine estimator of the varying coefficients. In this paper, we propose an approximation method for the varying coefficient functions using local averaging, which is characterized by its simplicity, rough and computational efficiency. Additionally, we introduce an adaptive lasso estimator and a forward regression algorithm specifically designed for semivarying coefficient models. Theoretical and experimental results highlight the effectiveness of the local averaging method in extending variable selection techniques from the linear model to the semivarying coefficient model. Our proposed approaches demonstrate a significant improvement in inference speed compared with baseline methods, with little loss of asymptotic efficiency.

A Phase-Space Discontinuous Galerkin Scheme for the Radiative Transfer Equation in Slab Geometry

Abstract We derive and analyze a symmetric interior penalty discontinuous Galerkin scheme for the approximation of the second-order form of the radiative transfer equation in slab geometry. Using appropriate trace lemmas, the analysis can be carried out as for more standard elliptic problems. Supporting examples show the accuracy and stability of the method also numerically, for different polynomial degrees. For discretization, we employ quad-tree grids, which allow for local refinement in phase-space, and we show exemplary that adaptive methods can efficiently approximate discontinuous solutions. We investigate the behavior of hierarchical error estimators and error estimators based on local averaging.

Crop index insurance as a tool for climate resilience: lessons from smallholder farmers in Nigeria

Crop farmers in arid and semi-arid regions face a covariate risk that is of paramount importance, namely, the threat of catastrophic crop loss triggered by natural disasters and climate hazards. Such an occurrence not only jeopardizes the livelihoods of these farmers but may also lead to persistent poverty. Promoting sustainable development requires control over climate-related shocks, which negatively affect the most vulnerable population in the developing world. This study investigates the option of choosing crop area-yield index insurance to mitigate the adverse effects associated with climate stress. In addition, we assessed the amount farmers are willing to pay for an insurance product that insures yields below 70% of the local average in the study area. Our results show that access to extension services and economic association membership offers farmers social capital and encourages their decision to purchase index insurance. We also find that the long-run welfare impact of index insurance policy on vulnerable households could be significant as an alternative insurance mechanism to traditional insurance. This study contributes to filling the gaps on the uptake of index insurance and provide guidance to policymakers in their approach to mitigating the effects of climate change on crop production in Nigeria.

Rapid inverse radiative transfer solver for multiparameter spectrophotometry without integrating sphere.

Multiparameter spectrophotometry (MPS) provides a powerful tool for accurate characterization of turbid materials in applications such as analysis of material compositions, assay of biological tissues for clinical diagnosis and food safety monitoring. This work is aimed at development and validation of a rapid inverse solver based on a particle swarm optimization (PSO) algorithm to retrieve the radiative transfer (RT) parameters of absorption coefficient, scattering coefficient and anisotropy factor of a turbid sample. Monte Carlo (MC) simulations were performed to obtain calculated signals for comparison to the measured ones of diffuse reflectance, diffuse transmittance and forward transmittance. An objective function has been derived and combined with the PSO algorithm to iterate MC simulations for MPS. We have shown that the objective function can significantly reduce the variance in calculated signals by local averaging of an inverse squared error sum function between measured and calculated signals in RT parameter space. For validation of the new objective function for PSO based inverse solver, the RT parameters of 20% Intralipid solutions have been determined from 520 to 1000nm which took about 2.7 minutes on average to complete signal measurement and inverse calculation per wavelength. The rapid solver enables MPS to be translated into easy-to-use and cost-effective instruments without integrating sphere for material characterization by separating and revealing compositional profiles at the molecular and particulate scales.

Point Cloud Registration Based on Local Variation of Surface Keypoints

Keypoint detection plays a pivotal role in three-dimensional computer vision, with widespread applications in improving registration precision and efficiency. However, current keypoint detection methods often suffer from poor robustness and low discriminability. In this study, a novel keypoint detection approach based on the local variation of surface (LVS) is proposed. The LVS keypoint detection method comprises three main steps. Firstly, the surface variation index for each point is calculated using the local coordinate system. Subsequently, points with a surface variation index lower than the local average are identified as initial keypoints. Lastly, the final keypoints are determined by selecting the minimum value within the neighborhood from the initial keypoints. Additionally, a sampling consensus correspondence estimation algorithm based on geometric constraints (SAC-GC) for efficient and robust estimation of optimal transformations in correspondences is proposed. By combining LVS and SAC-GC, we propose a coarse-to-fine point cloud registration algorithm. Experimental results on four public datasets demonstrate that the LVS keypoint detection algorithm offers improved repeatability and robustness, particularly when dealing with noisy, occluded, or cluttered point clouds. The proposed coarse-to-fine point cloud registration algorithm also exhibits enhanced robustness and computational efficiency.

Street tree diversity and urban heat

Higher diversity within a city's street trees may offer greater cooling benefits than less diverse urban forests. California’s urban forests are among the most diverse in the world and offer an opportunity to test the relationship between diversity and cooling at a large scale. For 136 urban ZIP codes, we connect the most comprehensive data to date on California’s urban forests to both local station and satellite weather data for the period 2010–2018. We test whether biodiversity, measured by the Shannon-Wiener index and the new Top Diversity 50 index, is correlated with extreme heat in summer. After controlling for local averages in weather and tree canopy cover, we find that urban forest biodiversity is associated with lower maximum and higher minimum temperatures for June to September. Our specification makes it unlikely that reverse causality drives our result. Instead, we suggest that greater tree species diversity may boost daytime cooling through several pathways, including mutualism and greater aboveground biomass, a mechanical relationship where greater biodiversity implies a greater likelihood of having species with excellent shade, and cooling benefits from structural diversity in urban settings.

Reliability-based factor of safety for bearing capacity of square foundations on 3D cohesive-frictionless soils considering samples

Reliability-based design concepts have been widely adopted in structural engineering but remain less prevalent in geotechnical practice due to the challenges of characterising ground uncertainties. To facilitate the unification of structural and geotechnical designs in complex engineering systems, we focus on square shallow foundations as crucial elements. Before a complete transition to load and resistance factor design, understanding the reliability of foundations designed using traditional factors of safety is essential. This paper presents a simplified analytical method for evaluating the probability of failure of square shallow foundations on 3D cohesive soils, utilising local average theory. The approach efficiently computes local averages and covariances of soil properties without the need for time-consuming 3D finite element computations. Verified against random finite element analysis, the method accurately determines required factors of safety for bearing capacity designs. Site-specific parameters, including sample location, shear strength coefficient of variation, and spatial variability fluctuation scale, influence the derived factors of safety. We advocate a pragmatic approach that calibrates traditional factor of safety methods with rigorous reliability theories, easing the transition to reliability-based design for practicing engineers. Moreover, the determined factors of safety can aid in establishing appropriate resistance factors by considering suitable load factors.

Rayleigh wave group velocities in North-West Iran: SOLA Backus-Gilbert vs. Fast Marching tomographic methods

In this study, we focus on Northwest Iran and exploit a dataset of Rayleigh-wave group-velocity measurements obtained from ambient noise cross-correlations and earthquakes.We build group-velocity maps using the recently developed SOLA Backus-Gilbert linear tomographic scheme as well as the more traditional Fast-marching Surface-wave Tomography method.The SOLA approach produces robust, unbiased local averages of group velocities with detailed information on their local resolution and uncertainty; however, it does not as yet allow ray-path updates in the inversion process. The Fast-marching method, on the other hand, does allow ray-path updates, although it does not provide information on the resolution and uncertainties of the resulting models (at least not without great computational cost) and may suffer from bias due to model regularisation.The core of this work consists in comparing these two tomographic methods, in particular how they perform in the case of strong vs. weak seismic-velocity contrasts and good vs. poor data coverage. We demonstrate that the only case in which the Fast-marching inversion outperforms the SOLA inversion is for strong anomaly contrasts in regions with good path coverage; in all other configurations, the SOLA inversion produces more coherent anomalies with fewer artefacts.

An audit of a general practices compliance with national recommendations for the avoidance of complications for type 2 diabetic patients

Abstract Introduction Each week, diabetes leads to more than 700 strokes, almost 600 heart attacks and more than 2000 instances of heart failure, in addition to being the single most common cause of chronic kidney disease in the UK.1 The NHS spends 10% of its annual budget on diabetes with 80% of that treating the complications of diabetes.2 Pharmacists have a crucial role to optimise the medication of diabetic patients and prevent future problems. Aim To assess a general practices (GP) compliance with national recommendations and the Quality and Outcomes Framework for the avoidance of complications for Type 2 Diabetes Mellitus (T2DM) patients. Methods This single-centre, retrospective audit was conducted at a GP in the London Borough of Southwark. Six standards were measured focusing on the prevention of the complications of diabetes. Each standard incorporated an exclusion and inclusion criteria. Standard 1 and 2 reviewed the appropriate management of nephropathy and hypertension respectively. Standard 3 and 4 relate to statin use for primary and secondary prevention. The final standards assess how many patients have documented QRISK2 scores and initiated on a Sodium-Glucose Co-Transporter 2 (SGLT2) inhibitor as per the new NICE guidelines.3 Ethical approval was not deemed necessary for this clinical audit in agreement with the Hurley Group and King’s College Hospital NHS Foundation Trust guidelines. Results Half (50%, n=153/304) of all included T2DM patients at the practice have a blood pressure reading defined as hypertensive. Out of all diabetic patients, 25 of them had clinical proteinuria, micro-albuminuria, or a diagnosis of nephropathy. Of these 25 patients, 19 (76%) were prescribed an angiotensin-converting enzyme inhibitor or angiotensin-receptor blockers. A small portion (7%, n=28/374) of the diagnosed T2DM patients have a documented QRISK2 score. Subsequently, only 32 diabetic patients (9%, n=32/374) are prescribed an SGLT2 inhibitor. For standards 3 and 4, there were 122 patients (73%) who were prescribed a statin for primary prevention and 89 patients (87%) who were prescribed a statin for secondary prevention. Discussion/Conclusion The microvascular and macrovascular complications of diabetes are well documented. The incidence of these complications can be reduced by pharmacological management. Although none of the standards were met, the results are similar to other practices across the country. However, in comparison to the local average, the audited GP underperformed with no standards being better than the average of GPs in the area. Sub-analysis emphasises the disparities in health between various racial and ethnic groups. The results of the two largest demographic groups audited, Caucasian and Caribbean patients, show that the former is more likely to meet the standards. These findings indicate that the processes of the practice require change to meet the needs of our diabetic patients. In particular, work is required to understand the disparities in care for our patients categorised as ethnic minorities. The recommendations fall under 5 main categories – feedback, learn, templates, analyse, and review.

Study of the strongest dust storm occurred in Uzbekistan in November 2021

We studied and reconstructed a severe Central Asian dust storm of November 4, 2021, through high-resolution TROPOMI UVAI spaceborne observations, ground-based aerosol measurements, and Lagrangian particle modeling. The dust storm was caused by the front part of a cold polar anticyclone front from the Ural-Volga regions, which struck the central and eastern parts of Uzbekistan under favorable atmospheric conditions. Two plumes spread out, causing a thick haze to blanket the region. The most severe dust storm effects hit the capital of Uzbekistan (Tashkent) and the Fergana Valley, where the thick atmospheric dust layer dropped the visibility to 200 m. PM10 concentrations reached 18,000 µg/m3 (260-fold exceedance of the local long-term average). The PM2.5 concentrations remained above 300 µg/m3 for nearly ten days, indicating an extremely long-lasting event. The dust storm was caused by an extremely strong summer heatwave of 2021 in Kazakhstan with unprecedentedly high temperatures reaching 46.5 °C. The long-lasting drought dried up the soil down to 50 cm depth, triggering the soil cover denudation due to drying out vegetation and losing its moisture. This event was the worst since 1871 and considering the increasing aridity of Central Asia, the onset of potentially recurring severe dust storms is alarming.

Random average sampling and reconstruction in a reproducing kernel space

Suppose that signals of interest reside in a reproducing kernel space defined on a metric measure space. We consider the scenario that the sampling positions are distributed on a bounded domain of a metric measure space, and the sampling data are local averages of the original signals in a reproducing kernel space. For signals concentrated on in that reproducing kernel space, we study the stability of this sampling procedure by establishing a weighted sampling inequality of bi‐Lipschitz type. This type of stability implies a weak version of conventional sampling inequality. We propose an iterative algorithm that reconstruct these concentrated signals from finite sampling data. The reconstruction error is characterized through the concentration ratio and the Hausdorff distance between the set of sampling positions and . We also consider the random sampling scheme where the sampling positions are i.i.d. randomly drawn on , and the sampling data are local averages of concentrated signals. We demonstrate that these concentrated signals can be approximated from the random sampling data with high probability when the sampling size is at least of the order with being the measure of .

The kernel-balanced equation for deep neural networks

Deep neural networks have shown many fruitful applications in this decade. A network can get the generalized function through training with a finite dataset. The degree of generalization is a realization of the proximity scale in the data space. Specifically, the scale is not clear if the dataset is complicated. Here we consider a network for the distribution estimation of the dataset. We show the estimation is unstable and the instability depends on the data density and training duration. We derive the kernel-balanced equation, which gives a short phenomenological description of the solution. The equation tells us the reason for the instability and the mechanism of the scale. The network outputs a local average of the dataset as a prediction and the scale of averaging is determined along the equation. The scale gradually decreases along training and finally results in instability in our case.

A length scale for non-local multi-scale gradient interactions in isotropic turbulence

Three-dimensional turbulent flows enhance velocity gradients via strong nonlinear interactions of the rate-of-strain tensor with the vorticity vector, and with itself. For statistically homogeneous flows, their total contributions to gradient production are related to each other by conservation of mass, and so are the total enstrophy and total dissipation. However, locally, they do not obey this relation and have different (often extreme) values, and for this reason both production mechanisms have been subject to numerous studies, often decomposed into multi-scale interactions. In general lines, their dynamics and contributions to the cascade processes and turbulent kinetic dissipation are different, which poses a difficulty for turbulence modelling. In this paper, we explore the consequence of the ‘Betchov’ relations locally, and show that they implicitly define a length scale. This length scale is found to be approximately three times the size of the turbulent structures and their interactions. It is also found that, while the non-locality of the dissipation and enstrophy at a given scale comes mostly from larger scales that do not cancel, the non-local production of strain and vorticity comes from multi-scale interactions. An important consequence of this work is that isotropic cascade models need not distinguish between vortex stretching and strain self-amplification, but can instead consider both entities as part of a more complex transfer mechanism, provided that their detailed point value is not required and a local average of reasonable size is sufficient.