Adjustment Method Research Articles

Understanding the private-public school performance gap in PISA: evidence from Portugal

We analyse the PISA-reported convergence in the performance of private and public schools in Portugal. When PISA sampling weights are used, the number of students enrolled in those types of schools and specific grades/tracks of study differs significantly from official population figures. To account for those differences, we apply a post-stratification adjustment; however, sample sizes are small, resulting in estimates with low precision for several subgroups. We propose recommendations for improving the handling of these issues in future PISA cycles. In an additional analysis, we also account for changes in the composition of the student population. When all factors are considered, the convergence in scores is far less impressive than reported. For instance, in Science, after adjusting the sampling weights and removing population composition effects, the reported convergence of 46 points between private and public schools from 2015 to 2018 amounts to only 9 points. The decomposition and sample adjustment methods used in this paper can be easily adapted to other contexts.

Quantification of racial and ethnic disparities in alcohol-related problems in light of different methodological approaches.

We applied the Institute of Medicine (IOM) definition of racial and ethnic disparities in healthcare to estimate disparities in alcohol-related problems. This estimation involved adjusting for drinking patterns, gender and age, with observed disparities further explained by socioeconomic status (SES). We compared results of five statistical approaches which use different methods for adjusting covariates. We conducted analysis of the repeated cross-sectional data from the US National Alcohol Surveys (NAS) from 2000 to 2020, comparing traditional regression, rank-and-replacement, propensity score weighting, G-computation and the double-robust methods. 39 239 respondents aged 18 + across five NAS surveys oversampling Black and Hispanic/Latino/a populations. Our primary analysis examined the dichotomous outcomes of the three alcohol problem measures: occurrence of negative consequences, alcohol dependence (using DSM-IV criteria) and alcohol use disorder (AUD, using DSM-5 criteria). The drinking pattern variables encompassed past year total alcohol volume and measures of heavy drinking, including the number of days consuming 12+, 8-11 and 5-7 drinks. After adjusting for age, alcohol volume and heavy drinking days, statistically significantly higher prevalence of DSM-IV dependence and DSM-5 AUD were observed for Black and Hispanic men who drank in the past year compared with White men who drank. For instance, the Black-White difference in AUD prevalence ranged from 3.7% (95% confidence interval = 1.1%, 6.2%) to 4.9% (2.1%, 7.8%)-, while the HispanicWhite difference ranged from 2.3% (0.1%, 4.4%) to 3.4% (1.1%, 5.6%), using different adjustment methods. Further adjusting for SES factors only moderately explained the observed disparities. We found consistent results in the estimation of disparities across all five methods. There appear to be racial and ethnic disparities in alcohol-related problems between Black and Hispanic men in the United States relative to White men after alcohol drinking patterns and age are adjusted. The findings also exhibit overall consistency across the five different methods or measurement applied.

Human vs machine learning in face recognition: a case study from the travel industry

This research was conducted to help answer whether a machine learning simulation can replace the human ability to recognize human faces, especially under challenges under travel industry requirements. The human ability to recognize faces was evaluated using a series of questions in a survey. The questions challenged the human respondents to recognize faces under similar looks, with hair and makeup disguises, only part of the facial area, and under dark lighting conditions. At the same time, a histogram of oriented gradient (HoG) combined with a support vector machine (SVM) was built for machine learning simulations. The machine learning was evaluated using two datasets, i.e., the Extended Yale B (EYB) Face dataset for challenge under dark lighting conditions and The Extended Makeup Face Dataset (EMFD) for challenge using face with makeup disguise. The results showed that machine learning simulation of the face recognition system yielded accuracy as high as 95.4% under dark lighting conditions and 70.8% under facial makeup disguise. On the contrary, only 48% of respondents accurately recognized human faces in dark lighting. The number was increased to 94-96% when the face images were adjusted first with the contrast adjustment method. However, only 36-37% of respondents accurately recognized human faces under face makeup disguise.

ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging.

Numerical simulation serves as a cornerstone in scientific modeling, yet the process of fine-tuning simulation parameters poses significant challenges. Conventionally, parameter adjustment relies on extensive numerical simulations, data analysis, and expert insights, resulting in substantial computational costs and low efficiency. The emergence of deep learning in recent years has provided promising avenues for more efficient exploration of parameter spaces. However, existing approaches often lack intuitive methods for precise parameter adjustment and optimization. To tackle these challenges, we introduce ParamsDrag, a model that facilitates parameter space exploration through direct interaction with visualizations. Inspired by DragGAN, our ParamsDrag model operates in three steps. First, the generative component of ParamsDrag generates visualizations based on the input simulation parameters. Second, by directly dragging structure-related features in the visualizations, users can intuitively understand the controlling effect of different parameters. Third, with the understanding from the earlier step, users can steer ParamsDrag to produce dynamic visual outcomes. Through experiments conducted on real-world simulations and comparisons with state-of-the-art deep learning-based approaches, we demonstrate the efficacy of our solution.

Research on real-time adaptive adjustment method for HSER stable manufacture and consistent quality

Research on real-time adaptive adjustment method for HSER stable manufacture and consistent quality

The Controller Improves Voltage Quality In Microgrids

The inverters in the microgrid are connected in parallel to improve efficiency. When the Microgrid is operating in standalone mode, the inverters must be controlled to share their power to stabilize the frequency and voltage. The droop control method is one of the most popular power-sharing methods today, some studies have presented traditional and improved droop control methods. However, the purpose of the studies is power-sharing for the inverters that no purpose of reducing the voltage and frequency deviation to improve power quality. This paper presents a voltage and frequency adjustment method based on fuzzy logic to minimize voltage and frequency deviation to improve power quality in microgrids. This controller includes a Droop controller combined with fuzzy logic, the fuzzy logic block will control to change in the slope of the Droop characteristic curve when the load changes. The purpose of the proposed method is to improve the accuracy of power-sharing for inverters and at the same time minimize voltage and frequency deviations in microgrids. Simulation results will prove the effectiveness of the proposed method.

Identification of confounders and estimating the causal effect of place of birth on age-specific childhood vaccination

BackgroundIn causal analyses, some third factor may distort the relationship between the exposure and the outcome variables under study, which gives spurious results. In this case, treatment groups and control groups that receive and do not receive the exposure are different from one another in some other essential variables, called confounders.MethodPlace of birth was used as exposure variable and age-specific childhood vaccination status was used as outcome variables. Three approaches of confounder selection techniques such as all pre-treatment covariates, outcome cause covariates, and common cause covariates were proposed. Multiple logistic regression was used to estimate the propensity score for inverse probability treatment weighting (IPTW) confounder adjustment techniques. The proportional odds model was used to estimate the causal effect of place of birth on age-specific childhood vaccination. To validate the result obtained from observed data, we used a plasmode simulation of resampling 1000 samples from actual data 500 times.ResultOutcome cause and common cause confounder identification techniques gave comparable results in terms of treatment effect in the plasmode data. However, outcome causes that contain common causes and predictors of the outcome confounder identification gave relatively better treatment effect results. The treatment effect result in the IPTW confounder adjustment method was better than that of the regression adjustment method. The effect of place of birth on log odds of cumulative probability of age-specific childhood vaccination was 0.36 with odds ratio of 1.43 for higher level vaccination status. ConclusionIt is essential to use plasmode simulation data to validate the reproducibility of the proposed methods on the observed data. It is important to use outcome-cause covariates to adjust their confounding effect on the outcome. Using inverse probability treatment weighting gives unbiased treatment effect results as compared to the regression method of confounder adjustment. Institutional delivery increases the likelihood of childhood vaccination at the recommended schedule.

A Multi-Area Illumination Adjustment Method based on Metro Passenger Flow

Lighting energy consumption in subway stations is an important part of energy consumption, and improving lighting energy efficiency in stations can effectively reduce overall energy consumption. The article proposes a multi-area, intelligent lighting control model based on passenger flow density to realize the dynamic adjustment of lighting intensity in each area, effectively enhance the energy efficiency of the subway lighting system and save subway operating costs. At the same time, this control model also helps to reduce energy consumption and carbon emissions, and promote the sustainable development of urban transportation.

Metyrapone single administration, as a possible predictive tool of its dosage and timing in Cushing's syndrome.

Metyrapone is commonly used in the initial management of Cushing's syndrome to reduce hypercortisolemia, but its optimal dosage and timing can vary significantly between patients. Currently, there are limited guidelines on adjustment methods for its administration to individual needs. This study aimed to evaluate responsiveness of each patient to metyrapone and identify the patient characteristics associated with the indices of cortisol responsiveness following a low-dose metyrapone. This single-center retrospective observational study included 15 treatment-naïve patients, 7 of whom had Cushing's disease and 8 had adrenal Cushing's syndrome. Serum cortisol levels were measured hourly from the time of administration of 250 mg of metyrapone up to four hours afterward. Parameters analyzed included the nadir of serum cortisol levels (Fnadir), the difference between basal and nadir serum cortisol levels (ΔF), the time to nadir, and the characteristics of the patients. As a result, cortisol suppression curves showed significant variability among patients, particularly in the time to nadir. While the median time to nadir was 2 hours, 20% of patients required 4 hours or more, and these responses were not associated with patient characteristics. Fnadir was positively correlated with early-morning serum cortisol levels, serum cortisol levels after low-dose dexamethasone suppression test (LDDST), and urinary free cortisol (UFC) levels, whereas ΔF was positively correlated with late-night serum cortisol levels, serum cortisol levels after LDDST, and UFC levels. In conclusion, the duration of response to metyrapone appeared unpredictable in patients with Cushing's syndrome and did not correlate with patient characteristics at baseline. Tracking the effect of metyrapone following a single low-dose administration may explain this variability and provide insights for optimizing individual dosing regimens. Further studies are required to validate these findings and guide more personalized treatment adjustments.

Genetic analysis of psychosis Biotypes: shared Ancestry-adjusted polygenic risk and unique genomic associations.

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes, and isoforms showed significant genomic associations with specific Biotypes in a Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in the adult brain and fetal brain. TWAS inflation was addressed by the inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.

Research on a Novel Flux-Switching Permanent Magnet Motor with Adjustable Torque Ripple Using an Auxiliary Rotor

In the field of material manufacturing, torsional vibration has some benefits for cutting or forming processing. In principle, torsional vibration can be caused by the torque ripples of a motor. Flux-switching permanent magnet (FSPM) motors are doubly salient and generate torque ripples, making them an option for manufacturing. However, the amplitude of the torque ripples is related to the motor structure, and frequency has a linear relationship with running speed. The torque ripples cannot be controlled separately from the motor running speed at conventional current excitations. Thus, a novel FSPM machine with an auxiliary rotor (AR-FSPM) is proposed to adjust the torque ripples. The main rotor (m-rotor) outputs the power, and the auxiliary rotor (a-rotor) adjusts the ripple frequency. When the speed difference between two rotors is six times the m-rotor speed, the torque ripple waveforms overlap. In this case, by controlling the phase difference between the two rotors, the amplitudes of the torque ripples can be adjusted. In this study, the principle of this novel machine is introduced, and the relationship between the torque ripple performance and the geometric parameters of the auxiliary rotor is studied. Then, the torque ripple amplitude adjustment method for this machine is discussed. Finally, the prototype of this novel machine is presented to verify its feasibility.

Transfer matrix model of beam propagation and optimization method for bulk multi-pass cell

Abstract Bulk multi-pass cell (MPC) is an effective technique used for spectral broadening and temporal compression in the fields of ultrafast optics. In an actual experiment, due to mode-mismatching, the beam profile changes at each pass transmitting through the medium, which will damage the optical elements and has a negative impact on the nonlinear effects. In this paper, based on the symmetry configuration of MPC and ABCD transfer matrix, we propose the ABCD transfer matrix model for beam propagation and adjusted optimization method for input beam. To verify the model, the result is compared with the theoretical value of the resonator. The beam propagation and B-integral before and after mode-matching are calculated. The results demonstrate that the mode-matching adjustment method significantly improves beam quality and nonlinear effects during transmission. This technique provides a potential tool for the design, experiment and evaluation in the generation of ultrashort pulse.

A multiple imputation approach in enhancing causal inference for overall survival in randomized controlled trials with crossover

ABSTRACT Crossover or treatment-switching in randomized controlled trials presents notable challenges not only in the development and approval of new drugs but also poses a complex issue in their reimbursement, especially in oncology. When the investigational treatment is superior to control, crossover from control to investigational treatment upon disease progression or for other reasons will likely cause the underestimation of treatment benefit. Rank Preserving Structural Failure Time (RPSFT) and Two-Stage Estimation (TSE) methods are commonly employed to adjust for treatment switching by estimating counterfactual survival times. However, these methods may induce informative censoring by adjusting censoring times for switchers while leaving those for non-switchers unchanged. Existing approaches such as re-censoring or inverse probability of censoring weighting (IPCW) are often used alongside RPSFT or TSE to handle informative censoring, but may result in long-term information loss or suffer from model misspecification. In this paper, Kaplan–Meier multiple imputation with bootstrap procedure (KMIB) is proposed to address the informative censoring issues in adjustment methods for treatment switching. This approach can avoid information loss and is robust to model misspecification. In the scenarios that we investigate, simulation studies show that this approach performs better than other adjustment methods when the treatment effect is small, and behave similarly under other scenarios despite different switching probability. A case study in non-small cell lung cancer (NSCLC) is also provided to demonstrate the use of this method.

Genetic Analysis of Psychosis Biotypes: Shared Ancestry-Adjusted Polygenic Risk and Unique Genomic Associations.

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes and isoforms showed significant genomic associations with specific Biotypes in Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in adult brain and fetal brain. TWAS inflation was addressed by inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.

Estimating Causal Effect of Two-Dose COVID-19 Vaccination on Hospitalization: A Propensity Score Matching Approach

Introduction: To estimate the effectiveness of two-dose COVID-19 vaccination in reducing hospitalization, accounting for complex confounding factors in observational studies. Methods: Researchers applied propensity score methods to adjust for confounding variables, comparing their performance to traditional covariate adjustment methods. Multiple Logistic Regression and Propensity Score Matching were employed to analyze the data, ensuring a balanced comparison between vaccinated and unvaccinated groups. Results: Both analytical methods demonstrated a significant reduction in the likelihood of hospitalization among vaccinated individuals. The adjusted odds ratios (OR) were 0.29 (95% CI: 0.26, 0.31) via logistic regression and 0.32 (95% CI: 0.30, 0.34) using propensity score matching. Conclusion: The study confirms the effectiveness of two-dose COVID-19 vaccination in decreasing hospitalization. It highlights the importance of using meticulous approaches like propensity score methods to assess real-world impacts in complex observational data settings

Potentially mineralizable nitrogen: Estimation of the labile and stabilized pools under woodland and cultivated soils in Mexico

Objective: The aim of this work was to evaluate nitrogen pool and its variation due to land uses in different soil orders. Design/methodology/approach: Alfisols, Entisols and Inceptisols under different uses (woodland and cultivated soils) were incubated for 20 weeks under controlled temperature and humidity. The mineralizable N (NO3--N and NH4+-N) was obtained and the potentially mineralizable nitrogen (N0) was estimated by the method of iterative adjustment. Nitrogen from 0 to 5 weeks was considered as labile nitrogen, and from 5 to 20 weeks was the stabilized nitrogen. The labile nitrogen was described by a potential model, while the stabilized nitrogen was described by a logistic model. The labile nitrogen and stabilized nitrogen pools were estimated after obtaining the first derivate and solving the integral of the potentially mineralizable nitrogen equation. Results: The greatest estimated amounts of labile and stabilized nitrogen were detected in an Alfisol under woodland use, and the minimum values were obtained in an Entisol under agricultural use. Similar results were obtained for the estimated amount of labile and stabilized nitrogen pool. Limitations on study/implications: It is important to measure the labile and stabilized fraction of nitrogen, which requires long-term incubations to obtain models of soil N pools, so other methods realiable and faster need to be considered. Findings/conclusions: The potentially mineralizable nitrogen was positively related to the different fractions of labile and stabilized nitrogen under the different land uses.

Transient Viscosity Adjustment Using a Coaxial Nozzle for Electrospinning Nanofibers from Non-Spinnable Pure m-Poly(hydroxyamide).

In this study, a transient viscosity adjustment method using a coaxial nozzle was explored to fabricate nanofibers from non-spinnable m-poly(hydroxyamide) (m-PHA). Unlike conventional electrospinning methods that often require additives to induce fiber formation, this approach relies on a sheath-core configuration, introducing tetrahydrofuran (THF) to the sheath to temporarily adjust solution viscosity. The diffusion of THF into the core m-PHA solution resulted in momentary solidification at the interface, promoting nanofiber formation without compromising polymer solubility. SEM and rheological analyses confirmed that optimized sheath-to-core flow ratios yielded nanofibers with significantly reduced particle formation. Notably, increasing the THF flow rate facilitated a faster solidification rate, enhancing jet elongation and resulting in uniform nanofibers with diameters of approximately 180-190 nm. Although complete nanofibers without beads were not achieved in this study, this coaxial electrospinning approach presents a possible pathway for fabricating nanofibers from polymers with limited spinnability, potentially expanding the application scope of electro-spun materials in high-performance fields.

Predictive Characterization of Fracture and Absorption Tests in Halite Formations: an integrated approach of numerical modeling and field data

This study introduces a predictive model based on field data from oil wells in the pre-salt region, focusing on the analysis of fractures in saline rocks and the simulation of leak-off tests through integration with the finite element method. The analysis utilized a variety of techniques, including the construction of a correlation matrix, statistical testing, linear adjustment methods, and numerical modeling. Utilizing specific field data from halite formations, the study identified overburden stress and sediment depth as the most critical variables for minimum stress. Three linear adjustment methods—least squares, genetic algorithms, and Bayesian methods—were employed. All methods demonstrated a strong correlation among the variables, with Bayesian methods exhibiting the lowest percentage error relative to experimental data. Residual analysis showed that the regression models provided accurate predictions, with most points closely matching the actual values. Numerical modeling further allowed for the assessment of rock behavior under different fluid conditions, indicating that the compositional fluid provides a more realistic simulation and adheres more closely to theoretical expectations and field data. The results affirm the efficacy of the proposed models, particularly highlighting the use of Bayesian methods and mathematical modeling integrated with the finite element method, in predicting the dynamics of fractures and fluid absorption in saline formations. This model significantly enhances the characterization of fractures and absorption pressures in oil wells, improving its utility in predicting behaviors in formations associated with pre-salt fields.

A gear tooth side clearance adjustment device and measurement method

Abstract The paper describes a gear tooth side clearance adjustment device structure principle, tooth side clearance adjustment and measurement calculation method, with simple operation, measurement accuracy is easy to ensure, adjustable range, can be adjusted continuously, and comparative test verification, proved the feasibility of the technology.

Geologically conditioned method of adjustment of a gravimetric network

Geologically conditioned method of adjustment of a gravimetric network