Adjustment Method Research Articles

Covariate adjustment and estimation of difference in proportions in randomized clinical trials.

Difference in proportions is frequently used to measure treatment effect for binary outcomes in randomized clinical trials. The estimation of difference in proportions can be assisted by adjusting for prognostic baseline covariates to enhance precision and bolster statistical power. Standardization or g-computation is a widely used method for covariate adjustment in estimating unconditional difference in proportions, because of its robustness to model misspecification. Various inference methods have been proposed to quantify the uncertainty and confidence intervals based on large-sample theories. However, their performances under small sample sizes and model misspecification have not been comprehensively evaluated. We propose an alternative approach to estimate the unconditional variance of the standardization estimator based on the robust sandwich estimator to further enhance the finite sample performance. Extensive simulations are provided to demonstrate the performances of the proposed method, spanning a wide range of sample sizes, randomization ratios, and model specification. We apply the proposed method in a real data example to illustrate the practical utility.

Comparison of outcomes between off-pump and on-pump coronary artery bypass graft surgery using collaborative targeted maximum likelihood estimation.

There are some discrepancies about the superiority of the off-pump coronary artery bypass grafting (CABG) surgery over the conventional cardiopulmonary bypass (on-pump). The aim of this study was estimating risk ratio of mortality in the off-pump coronary bypass compared with the on-pump using a causal model known as collaborative targeted maximum likelihood estimation (C-TMLE). The data of the Tehran Heart Cohort study from 2007 to 2020 was used. A collaborative targeted maximum likelihood estimation and targeted maximum likelihood estimation, and propensity score (PS) adjustment methods were used to estimate causal risk ratio adjusting for the minimum sufficient set of confounders, and the results were compared. Among 24,883 participants (73.6% male), 5566 patients died during an average of 8.2years of follow-up. The risk ratio estimates (95% confidence intervals) by unadjusted log-binomial regression model, PS adjustment, TMLE, and C-TMLE methods were 0.86 (0.78-0.95), 0.88 (0.80-0.97), 0.88 (0.80-0.97), and 0.87(0.85-0.89), respectively. This study provides evidence for a protective effect of off-pump surgery on mortality risk for up to 8 years in diabetic and non-diabetic patients.

In vivo demonstration of a real-time temporal SNR acoustic output adjustment method.

This work proposes a novel method of temporal signal-to-noise ratio (SNR) guided adaptive acoustic output adjustment and demonstrates this approach during in vivo fetal imaging. Acoustic output adjustment is currently the responsibility of sonographers, but ultrasound safety studies show recommended ALARA (As Low As Reasonably Achievable) practices are inconsistently followed. This study explores an automated ALARA method that adjusts the Mechanical Index (MI) output, targeting imaging conditions matching the temporal noise perception threshold. A 28 dB threshold SNR is used as the target SNR, following prior work showing relevant noise quantities are imperceptible once this image data quality level is reached. After implementing adaptive output adjustment on a clinical system, the average MI required to achieve 28 dB SNR in an eleven-volunteer fetal abdomen imaging test ranged from 0.17 to 0.26. The higher MI levels were required when imaging at higher frequencies. During tests with 20-second MI adjustment imaging periods, the degree of motion impacted the adaptive performance. For stationary imaging views, target SNR levels were maintained in 90% of SNR evaluations. When scanning between targets the imaging conditions were more variable, but the target SNR was still maintained in 71% of the evaluations. Given the relatively low MI recommended when performing MI adjustment and the successful adjustment of MI in response to changing imaging conditions, these results encourage adoption of adaptive acoustic output approaches guided by temporal SNR.

IMPACT OF COVID-19 ON THE SELECTED PRIVATE EDUCATIONAL INSTITUTES IN DELHI-NCR REGION

IMPACT OF COVID-19 ON THE SELECTED PRIVATE EDUCATIONAL INSTITUTES IN DELHI-NCR REGION

An improved wide-lane ambiguity resolution method for kinematic smartphone positioning

Abstract The release of GNSS raw data on Android smartphones provides the potential for high-precision smartphone positioning using multi-constellation and multi-frequency signals. However, severe multipath and low observation quality in kinematic environments make double-differenced uncombined ambiguities difficult to resolve reliably. To address this, the paper proposes an improved wide-lane (WL) integer ambiguity resolution (IAR) method that combines integer rounding and the Least-Square AMBiguity Decorrelation Adjustment (LAMBDA) methods. The proposed method achieved fix rates of 57% to 70% in challenging environments, with an average improvement of 7 · 7% in horizontal positioning accuracy compared to the float solution. The traditional partial integer rounding method only improved accuracy by 1 · 1%.

Reducing the Memory Footprint of 3D Gaussian Splatting

3D Gaussian splatting provides excellent visual quality for novel view synthesis, with fast training and realtime rendering; unfortunately, the memory requirements of this method for storing and transmission are unreasonably high. We first analyze the reasons for this, identifying three main areas where storage can be reduced: the number of 3D Gaussian primitives used to represent a scene, the number of coefficients for the spherical harmonics used to represent directional radiance, and the precision required to store Gaussian primitive attributes. We present a solution to each of these issues. First, we propose an efficient, resolution-aware primitive pruning approach, reducing the primitive count by half. Second, we introduce an adaptive adjustment method to choose the number of coefficients used to represent directional radiance for each Gaussian primitive, and finally a codebook-based quantization method, together with a half-float representation for further memory reduction. Taken together, these three components result in a x27 reduction in overall size on disk on the standard datasets we tested, along with a x1.7 speedup in rendering speed. We demonstrate our method on standard datasets and show how our solution results in significantly reduced download times when using the method on a mobile device (see Fig. 1).

Qinghai-Tibet Plateau Glacier-DEM product derived from the Chinese stereo mapping satellites

Abstract. The Digital Elevation Model (DEM) is an important basic data for glacier research on the Qinghai-Tibet Plateau. With the rapid development of China 's stereo mapping satellite, it is possible to obtain high-precision DEM in the glacier area of the Qinghai-Tibet Plateau. In this study, the stereo images and laser altimetry data of ZY-3 and GF-7 satellites were comprehensively used, and the matching and joint adjustment methods of laser elevation control points and stereo images were proposed. A high-precision DEM product in glacier area was established, and the elevation accuracy of DEM was verified by using UAV data and ICESat-2 data. The results show that the accuracy of GF-7 DEM is better than 1.5 m when the slope is less than 6 degrees, and the overall accuracy is better than 2.8 m. The accuracy of ZY-3 DEM is better than 3.5 m when the slope is less than 6 degrees, and the overall accuracy is better than 5.0 m. Compared with the open source DEM with medium spatial resolution, the DEM elevation accuracy based on domestic stereo mapping satellite images is better, and the grid is finer and more detailed to describe the texture characteristics of the end of the glaciers. Compared with the high spatial resolution data set HMA DEM, the ZY-3 DEM is slightly worse and the GF-7 DEM is better, and both the ZY-3 DEM and GF-7 DEM are better than the HMA DEM in terms of coverage integrity in the Qinghai- Tibet Plateau. Chinese stereo mapping satellites can generate high-precision DEM in the glacier area, and can provide more accurate and reliable terrain reference data than the existing open free data for the study of glaciers in the Qinghai-Tibet Plateau.

Efficient Structure from Motion for Large-Size Videos from an Open Outdoor UAV Dataset

Modern UAVs (unmanned aerial vehicles) equipped with video cameras can provide large-scale high-resolution video data. This poses significant challenges for structure from motion (SfM) and simultaneous localization and mapping (SLAM) algorithms, as most of them are developed for relatively small-scale and low-resolution scenes. In this paper, we present a video-based SfM method specifically designed for high-resolution large-size UAV videos. Despite the wide range of applications for SfM, performing mainstream SfM methods on such videos poses challenges due to their high computational cost. Our method consists of three main steps. Firstly, we employ a visual SLAM (VSLAM) system to efficiently extract keyframes, keypoints, initial camera poses, and sparse structures from downsampled videos. Next, we propose a novel two-step keypoint adjustment method. Instead of matching new points in the original videos, our method effectively and efficiently adjusts the existing keypoints at the original scale. Finally, we refine the poses and structures using a rotation-averaging constrained global bundle adjustment (BA) technique, incorporating the adjusted keypoints. To enrich the resources available for SLAM or SfM studies, we provide a large-size (3840 × 2160) outdoor video dataset with millimeter-level-accuracy ground control points, which supplements the current relatively low-resolution video datasets. Experiments demonstrate that, compared with other SLAM or SfM methods, our method achieves an average efficiency improvement of 100% on our collected dataset and 45% on the EuRoc dataset. Our method also demonstrates superior localization accuracy when compared with state-of-the-art SLAM or SfM methods.

New task-new results? How the gaze cone is influenced by the method of measurement.

Perceiving direct gaze-the perception of being looked at-is important in everyday life. The gaze cone is a concept to define the area in which observers perceive gaze as direct. The most frequently used methods to measure direct gaze threshold fall into two broad groups: First, a variant of the method of constant stimuli, firstly introduced by Gibson and Pick (The American Journal of Psychology, 76, 386-394, 1963). Second, a variant of the method of adjustment, firstly introduced by Gamer and Hecht (Journal of Experimental Psychology: Human Perception and Performance, 33, 705-715, 2007). Previous studies found a considerable range of thresholds, and although some influences on thresholds are already known (uncertainty, clinical groups), thresholds often vary for no apparent reason. Another important method is a triadic gaze-perception task, which usually finds triadic gaze direction judgments to be overestimated. In two experiments, we compare the method of adjustment with the method of constant stimuli. Experiment 1additionally examines the influence of the overestimation effect found in the triadic task. Results indicate that thresholds are larger when measured by the method of adjustment than by constant stimuli. Furthermore, Experiment 1finds a nonlinear overestimation factor, indicating that gaze directions near 0° are less overestimated than larger eccentricities. Correcting the thresholds with individually obtained overestimation factors widens the gaze cone but does not eliminate the average difference between the methods of adjustments and constant stimuli.

InSAR Digital Surface Model Refinement by Block Adjustment with Horizontal Constraints

Abstract. Interferometric Synthetic Aperture Radar (InSAR) technology is an important method to generate digital surface model (DSM). The past studies on space-borne derived DSM most focused on the elevation correction, due to the relative low resolution of DSM product. As a large number of high-resolution satellite data emerge, the horizontal discrepancies are needed to be considered. This paper proposes a DSM block adjustment method with horizontal constraints, aimed at eliminating the horizontal errors that exist between multiple DSM scenes with overlaps, achieving high precision and consistency in both the horizontal and vertical dimensions. Using ICESat-2 ATL08 point clouds as absolute elevation control and a reference DSM for horizontal control, the adjustment equations are constructed based on the constraint of tie points and controls. The experiment selects 7 image pairs of China TH2-01 SAR satellite, corresponding ICESat-2 ATL08 point and AW3D30 as reference DEM. The block adjustment results show that the proposed method improves the absolute vertical accuracy from 3.78 m to 2.56 m and reduces the average horizontal standard deviation between the InSAR derived DSMs and the reference AW3D30 from 15.31 m to 9.08 m.

Near Real-Time Monitoring of Large Gradient Nonlinear Subsidence in Mining Areas: A Hybrid SBAS-InSAR Method Integrating Robust Sequential Adjustment and Deep Learning

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results.

Autonomous ultrasound scanning robotic system based on human posture recognition and image servo control: an application for cardiac imaging

In traditional cardiac ultrasound diagnostics, the process of planning scanning paths and adjusting the ultrasound window relies solely on the experience and intuition of the physician, a method that not only affects the efficiency and quality of cardiac imaging but also increases the workload for physicians. To overcome these challenges, this study introduces a robotic system designed for autonomous cardiac ultrasound scanning, with the goal of advancing both the degree of automation and the quality of imaging in cardiac ultrasound examinations. The system achieves autonomous functionality through two key stages: initially, in the autonomous path planning stage, it utilizes a camera posture adjustment method based on the human body’s central region and its planar normal vectors to achieve automatic adjustment of the camera’s positioning angle; precise segmentation of the human body point cloud is accomplished through efficient point cloud processing techniques, and precise localization of the region of interest (ROI) based on keypoints of the human body. Furthermore, by applying isometric path slicing and B-spline curve fitting techniques, it independently plans the scanning path and the initial position of the probe. Subsequently, in the autonomous scanning stage, an innovative servo control strategy based on cardiac image edge correction is introduced to optimize the quality of the cardiac ultrasound window, integrating position compensation through admittance control to enhance the stability of autonomous cardiac ultrasound imaging, thereby obtaining a detailed view of the heart’s structure and function. A series of experimental validations on human and cardiac models have assessed the system’s effectiveness and precision in the correction of camera pose, planning of scanning paths, and control of cardiac ultrasound imaging quality, demonstrating its significant potential for clinical ultrasound scanning applications.

Cost-effectiveness of emicizumab prophylaxis for haemophilia A with inhibitors: an adaptive health technology assessment for the Indian setting

ObjectiveTo assess the cost-effectiveness of emicizumab prophylaxis for patients having haemophilia A with inhibitors in the Indian context using an adaptive health technology assessment (aHTA) methodology.DesignEconomic evaluation using multiple approaches...

Abstract PO5-08-12: Ethnic Admixture Affects Breast Cancer Incidence in Native Hawaiians: The Multiethnic Cohort

Abstract Background Native Hawaiians (NH) have the highest breast cancer incidence and the poorest survival of any ethnic group in Hawaii [1]. Since it has been shown that different ethnic groups have disparate risk of breast cancer in Hawaii and NHs are commonly of mixed ancestry, it is possible that the specific ethnic admixture may present an altered risk profile. For example, NH with Asian admixture was associated with higher risk for type 2 diabetes independent of known risk factors [2] compared to those without. The objective of this study was to calculate crude and age-adjusted breast cancer incidences by ethnic admixture group among participants of the Multiethnic Cohort (MEC). Methods: Based on self-reports, 106,525 eligible women were categorized by broad ethnic categories: Native Hawaiian, White, Black, Latino, and Japanese. In total there were 9257 breast cancer cases in the MEC cohort (1996-2019) at time of analysis. The Native Hawaiian population was further categorized into the following subgroups: Native Hawaiian only, Native Hawaiian & White, Native Hawaiian & Chinese, Native Hawaiian & White & Chinese. Crude and age-adjusted incidence rates (per 1000 person-years) were calculated for the various groups. The direct method of age adjustment using the US 2000 Standard population was used. Results: The ethnicity with the highest crude incidence were White=88.46, followed by Black=85.20, Latino=84.27, Japanese=84.90, and Native Hawaiian=81.28. However, after age-adjustment the highest were Japanese=159.1, White=153.8, Latino=148.3, Black=147.4, and Native Hawaiian=139.1. Crude incidence rates for Native Hawaiian ethnic admixture was found to be highest for Native Hawaiian & White=83.5, Native Hawaiian only=82.5, Native Hawaiian & Chinese=80.8, and Native Hawaiian & White & Chinese=80.4. After age adjustment, the highest was Native Hawaiian & Chinese=173.9, followed by Native Hawaiian & White=154.3, Native Hawaiian only=148.7, and finally Native Hawaiian & White & Chinese=120.8. Conclusion: These findings indicate that ethnic admixture in Native Hawaiians may be at elevated age-adjusted breast cancer incidence when compared to Native Hawaiian alone. When a woman was Native Hawaiian and Chinese the incidence was notably higher than any other group at 173.9 when compared to all Native Hawaiian=139.1. Furthermore, the group that had the lowest age-adjusted incidence was the mixed group of Native Hawaiian & White & Chinese=120.8 which seems to account for why the overall Native Hawaiian group=139.1 had one of the lowest incidence rates of the 5 major ethnic groups. 1. Loo, L.W., M. Williams, and B.Y. Hernandez, The high and heterogeneous burden of breast cancer in Hawaii: A unique multiethnic US Population. Cancer epidemiology, 2019. 58: p. 71-76. 2. Maskarinec, G., et al., Ethnic admixture affects diabetes risk in native Hawaiians: the Multiethnic Cohort. Eur J Clin Nutr, 2016. 70(9): p. 1022-7. Citation Format: Dustin Valdez, Arianna Bunnell, David Bogumil, Gertraud Maskarinec, John Shepherd. Ethnic Admixture Affects Breast Cancer Incidence in Native Hawaiians: The Multiethnic Cohort [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-08-12.

Acoustic heterostructures designed by topology optimization for interface states

Interface states can be induced through the design of heterostructures comprised of structures or materials with different physical properties. Compared with the traditional method of the manual experience adjustment, the topology optimization method is more systematic and scientific in designing heterostructures to achieve the desired performance of interface states. We combine the multiple population genetic algorithm (MPGA) with the finite element method, and consider the transmission spectrum characteristic of interface states as a fitness function forming a topology optimization method to find optimal designs for acoustic heterostructures. The frequency of the interface state induced by acoustic heterostructures after topology optimization is highly consistent with the objective frequency. The characteristic analyses of the sound field and transmission spectrum show that interface states possess high transmission and localization. Such a topology optimization method opens up design solutions in various engineering fields, which include wavefront manipulation, sound detection, and acoustic topological materials.

Adjustment of p values for multiple hypotheses: why, when and how

It is quite common to investigate multiple hypotheses in a single study. For example, a researcher may want to investigate the effect on several outcome variables or at different time...

Trajectory optimization of B-splines interpolation based on dynamic error adjustment

In this paper, we propose a motion planning algorithm based on the five-fold B-spline curve for multi-axis motion control systems with non-smooth trajectories, acceleration, plus acceleration over the limit, etc. We construct a smooth transition model for the micro-line segment G3 of the continuous five-fold B-spline curve. A derivation of the corresponding theoretical equation is given. Using the transition model, we propose an optimization method for dynamic adjustment of motion trajectory errors based on the velocity forward planning constraints by smoothing the transition after the trajectory of the micro-line segment for the forward planning velocity. Finally, the design flow for the forward planning of the velocity is given. Finally, the above algorithm is used to perform simulation experiments for the optimization algorithm. Experimental results show that the proposed optimization improves the accuracy of fitting motion trajectories under the constraints of acceleration and deceleration addition, which validates the proposed algorithm.

Office building energy consumption forecast: adaptive long short term memory networks driven by improved beluga whale optimization algorithm

With the development of urbanization, buildings have become a major source of energy consumption. This research uses a data-driven approach to achieve accurate building energy consumption prediction by analyzing and modeling building energy consumption data. The model proposed in this paper uses improved beluga whale optimization algorithm (IBWO) to optimize long short-term memory networks (LSTM) for accurate energy consumption prediction. In order to enhance the ability of BWO in global search and local exploitation, a new method of dynamic adjustment of step factor as well as strategies such as nonlinear decreasing are introduced to improve BWO. For the first time, it is proposed to explore the accuracy of the number of hyper-parameters of LSTM on the prediction of energy consumption, and the improved beluga whale optimization algorithm is used to optimize the two, three, and four hyper-parameters of LSTM respectively. Then short-term prediction of historical energy consumption data of an office building in Xi`an is performed. Experiments show that the optimization of the four hyperparameters of LSTM using the IBWO of this paper can reduce the mean absolute error (MAE) of the pre-improvement model from 830.71 KW to 128.28 KW, the mean absolute percentage error (MAPE) from 12.32% to 1.38%, and the coefficient of variation (CV) from 7.5% to 1.2%.

Federated Learning With Dynamic Epoch Adjustment and Collaborative Training in Mobile Edge Computing

As a distributed learning paradigm, federated learning (FL) can be applied in mobile edge computing (MEC) to support real-time artificial intelligence by leveraging edge computation resources while preserving data privacy in the end devices. However, the unpredictable wireless connections between end devices and edge servers in MEC (e.g., frequent handovers and unstable wireless channels) may result in the loss of important model parameters, which slows down the FL training process and degrades the quality of the global model. In this paper, we propose an adaptive collaborative federated learning (ACFL) scheme to accelerate the convergence and improve model reliability by mitigating communication-based parameter loss under a three-layer MEC architecture. Firstly, a dynamic epoch adjustment method is proposed to reduce communication rounds by dynamically adjusting the training epochs in end devices. In addition, to accelerate the FL convergence, we present an edge server collaborative training scheme by leveraging a multi-layer computing architecture, where edge servers utilize their maintained data to collaboratively train models with end devices. Finally, extensive simulations are conducted and show that ACFL can efficiently improve model reliability and accelerate the convergence of the FL process in MEC.

Digitalized Voting System using Blockchain Technology

This paper introduces a novel Digitalized Voting System designed to address the shortcomings of current voting methods employed in India. With a focus on enhancing transparency and trust in the electoral process, the system aims to overcome challenges present in both traditional and digital voting systems, including instances of mishaps and injustice. Leveraging blockchain technology, the proposed system seeks to ensure fair elections and minimise occurrences of injustice. While electronic voting has been introduced as a solution to paper-based voting, it has encountered obstacles primarily related to security and privacy concerns. To address these issues, our framework emphasises the effectiveness of various components such as the polling process, hashing algorithms, contract and block creation, data accumulation, and result declaration.Utilising an adjustable blockchain method, the system aims to provide a robust solution to the security and data management challenges inherent in blockchain technology. By incorporating elements such as blockchain, hashing algorithms, block creation, OTP verification, and Ethereum, our approach endeavours to digitalize the voting process comprehensively. This paper contributes to the advancement of electoral integrity by presenting an improved manifestation of electronic voting, paving the way for more transparent and secure elections