Adjustment Method Research Articles

The utilization of guidewires for adjusting the intraoperative catheter malposition during the venous access port implantation: A retrospective study

This study presents an in-depth exploration of various adjustment methods for intraoperative catheter malposition by guidewires in the implantation of totally implantable venous access ports (TIVAP). It not only aims to summarize these methods but also endeavors to identify the most advantageous approach. The patient list was searched using the hospital information system from January 1, 2022, to October 31, 2023. All patients who had undergone chest port placement using the axillary vein (AxV) or subclavian vein (SCV) approach were reviewed, and further imaging was performed to confirm the guidewire applied to adjust the guidewire or catheter from the internal jugular vein into the superior vena cava (SVC) under fluoroscopy. Demographic data, diagnoses, technical outcomes, and perioperative complications were collected. About 32 patients with an average age of 62.8 years were included in the study. The operation time was 29.3 ± 13.3 minutes and SVC selecting time was 16.9 ± 11.5 seconds. The dose of X-ray exposure was only 7.2 ± 9.0 μGym2. Adjustments combined the guidewire with the puncture needle had the shortest SVC selection time and operation time with a minimal radiation dose. intraoperative catheter malposition can be timely and effectively adjusted using guidewires under fluoroscopy during any process of TIVAP implantation. Timely adjustment using a guidewire after inserting into the puncture needle is an optimal choice for a smooth and successful operation.

PDGrad: Guiding Diffusion Model for Reference-Based Blind Face Restoration with Pivot Direction Gradient Guidance

Reference-based blind face restoration (RefBFR) has gained considerable attention because it utilizes additional reference images to restore facial images in situations where the degradation is caused by unknown factors, making it particularly useful in real-world applications. Recently, guided diffusion models have demonstrated exceptional performance in this task without requiring training. They achieve this by integrating gradients of the losses where each loss reflects the different desired properties of the additional external images. However, these approaches fail to consider potential conflicts between gradients of multiple losses, which can lead to sub-optimal results. To address this issue, we introduce Pivot Direction Gradient guidance (PDGrad), a novel gradient adjustment method for RefBFR within a guided diffusion framework. To this end, we first define the loss function based on both low-level and high-level features. For loss at each feature level, both the coarsely restored image and the reference image are fully integrated. In cases of conflicting gradients, a pivot gradient is established for each level and other gradients are aligned to it, ensuring that the strengths of both images are maximized. Additionally, if the magnitude of the adjusted gradient exceeds that of the pivot gradient, it is adaptively scaled according to the ratio between the two, placing greater emphasis on the pivot. Extensive experimental results on the CelebRef-HQ dataset show that the proposed PDGrad significantly outperforms competitive approaches both quantitatively and qualitatively.

An integrated approach for decomposing time series data into trend, cycle and seasonal components

ABSTRACT This study presents a methodology for decomposing time series data into trend, seasonal, and cyclical components using the moving linear model approach by Kyo and Kitagawa (Journal of Business Cycle Research, 19(3): 373-397, 2023). Our approach integrates seasonal adjustment and decomposition into a single framework. We evaluated our approach with two case studies: examining daily COVID-19 case data and the Index of Industrial Production (IIP) in Japan, comparing the results to seasonally adjusted IIP data. Performance metrics included the discrimination power index and the variance of the adjusted cyclical component. Our findings show that our method effectively extracts business cycle information, achieving higher discrimination power and greater adjusted variance compared to seasonally adjusted IIP data, highlighting the superior performance of our integrated seasonal adjustment method. We compared the proposed approach with a state-space modelling method by introducing an overall stability as a new indicator. The results demonstrated the stability of the estimations obtained with our proposed method.

Community intervention of a single-dose or 2-dose regimen of bivalent human papillomavirus vaccine in schoolgirls in Thailand: vaccine effectiveness 2 years and 4 years after vaccination

Abstract Background With accumulating evidence of single-dose human papillomavirus (HPV) vaccine efficacy in young women, we conducted a community vaccine effectiveness study comparing HPV single-dose and 2-dose regimens (0 and 6 months) of a bivalent HPV vaccine among grade 8 schoolgirls (aged 13-14 years) in Thailand. Methods In 2018, eligible grade 8 schoolgirls in Udon Thani (single dose) and Buri Ram (2 doses) provinces were offered HPV vaccine per assigned dose regimen. Concurrently, a cross-sectional survey for measuring baseline HPV prevalence was conducted in grade 10 (n = 2600) and grade 12 unvaccinated schoolgirls (n = 2000) in each province. HPV infection was assessed in first-void urine samples, tested by DNA polymerase chain reaction on the cobas 4800 system (Roche Molecular Diagnostics, Pleasanton, CA). All samples positive on the cobas system and an equal number of negative samples were also tested by Anyplex II HPV28 Detection (Seegene, Seoul, South Korea). The surveys were repeated in 2020 and 2022, when vaccinated grade 8 schoolgirls reached grade 10, and then subsequently grade 12, respectively. Vaccine effectiveness was estimated by comparing the weighted prevalence of HPV-16 or HPV-18 between grade-matched unvaccinated schoolgirls on the baseline survey (2018) and vaccinated schoolgirls in the year-2 (2020) and year-4 (2022) surveys. Adjustment methods were used in the analysis to account for potential differences in sexual behavior due to the noncontemporaneous comparison. Results The prevalence of HPV-16 and HPV-18 on the baseline survey among unvaccinated grade 10/grade 12 schoolgirls was 2.90% (95% confidence interval [CI] = 2.54% to 3.31%)/3.98% (95% CI = 3.52% to 4.49%) for Udon Thani and 3.87% (95% CI = 3.46% to 4.34%)/6.13% (95% CI = 5.56% to 6.75%) for Buri Ram. On the year-2 survey, the prevalence among vaccinated grade 10 schoolgirls was 0.57% (95% CI = 0.42% to 0.77%) for Udon Thani and 0.31% (95% CI = 0.21% to 0.47%) for Buri Ram. The 2-year postvaccination crude vaccine effectiveness for the single-dose regimen was estimated at 80.4% (95% CI = 73.9% to 86.9%), and for the 2-dose regimen at 91.9% (95% CI = 88.5% to 95.4%). On the year-4 survey, the prevalence among vaccinated grade 12 schoolgirls was 0.37% (95% CI = 0.25% to 0.56%) for Udon Thani and 0.28% (95% CI = 0.18% to 0.45%) for Buri Ram. Four-year postvaccination crude vaccine effectiveness for the single-dose regimen was estimated at 90.6% (95% CI = 86.6% to 94.6%) and for the 2-dose regimen was estimated at 95.4% (95% CI = 93.2% to 97.6%). All adjustment methods minimally affected vaccine effectiveness for the single-dose and 2-dose regimens. At 4 years after vaccination, the difference in crude vaccine effectiveness between the single-dose and 2-dose regimens was ‒4.79% (95% CI = ‒9.32% to ‒0.25%), meeting the study’s noninferiority criteria. Conclusions Our study demonstrated that both single-dose and 2-dose HPV vaccination significantly decreased HPV-16/18 point prevalence 2 years and 4 years after vaccination. Crude vaccine effectiveness at 4 years after vaccination was greater than 90% for both the single-dose and 2-dose regimens; the single-dose regimen was not inferior to the 2-dose regimen. These data show that a single dose of HPV vaccine provides high levels of protection when administered to schoolgirls younger than 15 years of age.

Automated self-adjustment of array probe with a robotic ultrasonic test system

Ultrasonic testing of objects with complex geometries often requires the use of a robotic arm to position the probe perpendicular to the local surface. Using immersion makes it possible to test these objects with standard ultrasonic linear array probes. Here, the probe positions and orientations provided by the robot are used for merging the locally acquired image data into a 3D-reconstruction. The quality of this reconstruction is highly dependent on the alignment of the position of the physical probe with the position used in the digital model. For common industrial tools, the tool center point (TCP) is usually acquired using geometric features of the tools. However, for ultrasonic arrays in immersion, there is a water standoff between the probe and the test object, therefore the TCP is in free space in front of the array and cannot be acquired with the common method. To overcome this challenge, we propose a method that allows the robotic ultrasonic system to automatically self-adjust the position and orientation of the ultrasonic probe using a test block made of steel with defined geometric features as a target for referencing. For each of the six degrees of freedom, a scan and adjustment routine are established using the data based on the actual ultrasound characteristics of the probe. Given a coarse pre-definition of the tool position and the known target test block, minimal human interaction is required to supervise the adjustment method, leading to higher quality reconstructions than with manual adjustment.

Symbol-level scheme for combating eavesdropping: Symbol conversion and constellation adjustment

In the non-orthogonal multiple access scenario, users may suffer inter-multiuser eavesdropping due to the feature of successive interference cancellation, and the conditions of eavesdropping suppression methods in the traditional schemes may not be satisfied. To combat this eavesdropping, we consider physical layer security and propose a novel scheme by specially designing symbol conversion and constellation adjustment methods. Based on these methods, the amplitudes and phases of symbols are properly changed. When each user intercepts information as an eavesdropper, he/she has to accept high error probability, or he/she has to undergo exorbitant overhead. Analytical and numerical results demonstrate that the proposed scheme can protect the privacy of information, and this protection does not destruct the execution of successive interference cancellation and symbol transmission.

Small sample adjustment for inference without assuming orthogonality in a mixed model for repeated measures analysis

ABSTRACT The mixed model for repeated measures (MMRM) analysis is sometimes used as a primary statistical analysis for a longitudinal randomized clinical trial. When the MMRM analysis is implemented in ordinary statistical software, the standard error of the treatment effect is estimated by assuming orthogonality between the fixed effects and covariance parameters, based on the characteristics of the normal distribution. However, orthogonality does not hold unless the normality assumption of the error distribution holds, and/or the missing data are derived from the missing completely at random structure. Therefore, assuming orthogonality in the MMRM analysis is not preferable. However, without the assumption of orthogonality, the small-sample bias in the standard error of the treatment effect is significant. Nonetheless, there is no method to improve small-sample performance. Furthermore, there is no software that can easily implement inferences on treatment effects without assuming orthogonality. Hence, we propose two small-sample adjustment methods inflating standard errors that are reasonable in ideal situations and achieve empirical conservatism even in general situations. We also provide an R package to implement these inference processes. The simulation results show that one of the proposed small-sample adjustment methods performs particularly well in terms of underestimation bias of standard errors; consequently, the proposed method is recommended. When using the MMRM analysis, our proposed method is recommended if the sample size is not large and between-group heteroscedasticity is expected.

Adaptive decomposition-based evolutionary algorithm for many-objective optimization with two-stage dual-density judgment

In order to better balance the convergence and diversity of MOEA/D for many objective optimization problems (MaOPs) with various Pareto fronts (PFs), an adaptive decomposition-based evolutionary algorithm for MaOPs with two-stage dual-density judgment is proposed. To solve the problem that weighted Tchebycheff decomposition may produce weakly Pareto optimal solutions when the solution is not unique or the uniqueness is difficult to guarantee, an augmented weighted Tchebycheff decomposition is adopted. To balance the convergence and diversity of non-dominated solutions in the external archive, different sparsity-level evaluations using vector angles or Euclidean distances are used to measure the distribution of solutions at different stages. To improve the diversity of solution sets obtained by MOEA/D for various PFs, an adaptive weight vector adjustment method based on two-stage dual-density judgment is presented. For weight vector addition, the potential search area is found according to the two-stage density judgment, and then a two-stage sparsity level judgment on the solutions of this area is performed for a second density judgment. For weight vector deletion, the degree of crowding is used to delete the weight vectors with a high crowding degree. Compared with nine advanced multi-objective optimization algorithms on DTLZ and WFG problems, the results demonstrate that the performance of the proposed algorithm is significantly better than other algorithms.

A non-stationary bias adjustment method for improving the inter-annual variability and persistence of projected precipitation

Hydrological studies depend heavily on environmental variables, such as precipitation and resulting runoff, which exhibit highly seasonal and intermittent behaviour in semiarid basins. In these basins, the use of traditional methods to adjust biases in time series projections can lead to inaccurate results regarding the impacts of climate change. This study introduces a non-stationary bias adjustment methodology (NS) specifically designed for environmental variables characterized by sporadic events and substantial intensity variability, such as precipitation. The methodology involves establishing a probability threshold to adapt the occurrence of precipitation events and utilizes a quantile mapping method based on a non-stationary theoretical and parametric distribution to adjust biases associated with precipitation intensity. The NS method is applied to daily precipitation projections from seven regional climatic models under the RCP 8.5 scenario spanning 2006–2100, alongside historical concurrent data from 1970 to 2005. The present method is compared to the widely used quantile delta mapping approach (QDM), revealing significant differences in performance related to the distribution of precipitation events throughout the year and the behaviour of mean and extreme intensity values. Both approaches show reliable performance, with root mean square errors between the empirical distribution functions of the corrected hindcast time series and the observations being lower than or close to 1 mm for the percentiles smaller than 50th. The error increases with the percentiles, particularly at stations located at higher altitudes. In these locations, QDM doubles or triples the error obtained with the non-stationary approach for percentiles higher than 75th, reaching up to 34 mm. The proposed methodology demonstrates promising potential in reducing uncertainties associated with systematic errors in inter-annual precipitation variability. It is a first step to jointly apply a similar approach to all involved driving variables. Such methods are key to assessing hydrological responses and associated impacts in semi-arid mountainous basins all around the Globe.

An improved approach to compensating for cure‐induced deformation to manufacture composite aircraft skin accurately

AbstractThe purpose of this paper is to compensate for the cure‐induced deformation to manufacture composite aircraft skin with variable curvature accurately. Firstly, a finite element analysis (FEA) method appropriate for curing simulation was described. Then, the discrete curvature‐based displacement adjustment (DC‐DA) method for mold surface compensation was proposed. The DC‐DA method utilizes the discrete curvature of the section points to obtain a smooth compensated curve. Finally, a skin part was selected to prove its effectiveness. The experimental results showed that after the mold's surface was modified by the DC‐DA method with the FEA‐predicting deformation as input, the forming shape could meet the requirement of the part accuracy in one cycle. The DC‐DA method was also compared with the displacement adjustment (DA) method and proved to be more efficient for the compensation of composite skin with variable curvature.Highlights The FEA is appropriate for the calculation of the skin's curing deformation. The DC‐DA method is efficient for compensating for the skin's deformation. The shape of the case part could meet the requirement of accuracy in one cycle.

When does adjusting covariate under randomization help? A comparative study on current practices

PurposeWe aim to thoroughly compare past and current methods that leverage baseline covariate information to estimate the average treatment effect (ATE) using data from of randomized clinical trials (RCTs). We especially focus on their performance, efficiency gain, and power.MethodsWe compared 6 different methods using extensive Monte-Carlo simulation studies: the unadjusted estimator, i.e., analysis of variance (ANOVA), the analysis of covariance (ANCOVA), the analysis of heterogeneous covariance (ANHECOVA), the inverse probability weighting (IPW), the augmented inverse probability weighting (AIPW), and the overlap weighting (OW) as well as the augmented overlap weighting (AOW) estimators. The performance of these methods is assessed using the relative bias (RB), the root mean square error (RMSE), the model-based standard error (SE) estimation, the coverage probability (CP), and the statistical power.ResultsEven with a well-executed randomization, adjusting for baseline covariates by an appropriate method can be a good practice. When the outcome model(s) used in a covariate-adjusted method is closer to the correctly specified model(s), the efficiency and power gained can be substantial. We also found that most covariate-adjusted methods can suffer from the high-dimensional curse, i.e., when the number of covariates is relatively high compared to the sample size, they can have poor performance (along with lower efficiency) in estimating ATE. Among the different methods we compared, the OW performs the best overall with smaller RMSEs and smaller model-based SEs, which also result in higher power when the true effect is non-zero. Furthermore, the OW is more robust when dealing with the high-dimensional issue.ConclusionTo effectively use covariate adjustment methods, understanding their nature is important for practical investigators. Our study shows that outcome model misspecification and high-dimension are two main burdens in a covariate adjustment method to gain higher efficiency and power. When these factors are appropriately considered, e.g., performing some variable selections if the data dimension is high before adjusting covariate, these methods are expected to be useful.

Current or recent malaria infection is associated with elevated inflammation-adjusted ferritin concentrations in pre-school children: a secondary analysis of the BRINDA database.

Inflammation and infections such as malaria affect micronutrient biomarker concentrations and hence estimates of nutritional status. It is unknown whether correction for C-reactive protein (CRP) and α1-acid glycoprotein (AGP) fully captures the modification in ferritin concentrations during a malaria infection, or whether environmental and sociodemographic factors modify this association. Cross-sectional data from eight surveys in children aged 6-59 months (Cameroon, Cote d'Ivoire, Kenya, Liberia, Malawi, Nigeria and Zambia; n 6653) from the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anaemia (BRINDA) project were pooled. Ferritin was adjusted using the BRINDA adjustment method, with values < 12 μg/l indicating iron deficiency. The association between current or recent malaria infection, detected by microscopy or rapid test kit, and inflammation-adjusted ferritin was estimated using pooled multivariable linear regression. Age, sex, malaria endemicity profile (defined by the Plasmodium falciparum infection prevalence) and malaria diagnostic methods were examined as effect modifiers. Unweighted pooled malaria prevalence was 26·0 % (95 % CI 25·0, 27·1) and unweighted pooled iron deficiency was 41·9 % (95 % CI 40·7, 43·1). Current or recent malaria infection was associated with a 44 % (95 % CI 39·0, 52·0; P < 0·001) increase in inflammation-adjusted ferritin after adjusting for age and study identifier. In children, ferritin increased less with malaria infection as age and malaria endemicity increased. Adjustment for malaria increased the prevalence of iron deficiency, but the effect was small. Additional information would help elucidate the underlying mechanisms of the role of endemicity and age in the association between malaria and ferritin.

Unraveling the glycosphingolipid metabolism by leveraging transcriptome-weighted network analysis on neuroblastic tumors

BackgroundGlycosphingolipids (GSLs) are membrane lipids composed of a ceramide backbone linked to a glycan moiety. Ganglioside biosynthesis is a part of the GSL metabolism, which involves sequential reactions catalyzed by specific enzymes that in part have a poor substrate specificity. GSLs are deregulated in cancer, thus playing a role as potential biomarkers for personalized therapy or subtype classification. However, the analysis of GSL profiles is complex and requires dedicated technologies, that are currently not included in the commonly utilized high-throughput assays adopted in contexts such as molecular tumor boards.MethodsIn this study, we developed a method to discriminate the enzyme activity among the four series of the ganglioside metabolism pathway by incorporating transcriptome data and topological information of the metabolic network. We introduced three adjustment options for reaction activity scores (RAS) and demonstrated their application in both exploratory and comparative analyses by applying the method on neuroblastic tumors (NTs), encompassing neuroblastoma (NB), ganglioneuroblastoma (GNB), and ganglioneuroma (GN). Furthermore, we interpreted the results in the context of earlier published GSL measurements in the same tumors.ResultsBy adjusting RAS values using a weighting scheme based on network topology and transition probabilities (TPs), the individual series of ganglioside metabolism can be differentiated, enabling a refined analysis of the GSL profile in NT entities. Notably, the adjustment method we propose reveals the differential engagement of the ganglioside series between NB and GNB. Moreover, MYCN gene expression, a well-known prognostic marker in NTs, appears to correlate with the expression of therapeutically relevant gangliosides, such as GD2. Using unsupervised learning, we identified subclusters within NB based on altered GSL metabolism.ConclusionOur study demonstrates the utility of adjusting RAS values in discriminating ganglioside metabolism subtypes, highlighting the potential for identifying novel cancer subgroups based on sphingolipid profiles. These findings contribute to a better understanding of ganglioside dysregulation in NT and may have implications for stratification and targeted therapeutic strategies in these tumors and other tumor entities with a deregulated GSL metabolism.

Adjustable viewpoint allocation for enhancing the viewing angle and reducing the crosstalk in super multi-view display.

In this Letter, we propose an adjustable viewpoint allocation method with forward and backward ray tracing to enhance the viewing angle and reduce the crosstalk in super multi-view (SMV) display. The synthetic image (SI) is initially calculated by backward ray tracing according to the viewing distance and the viewpoint interval. Forward ray tracing is then performed on the result of the viewpoint allocation to correct the deviation between the actual and the ideal viewpoint positions. Optical experiments are performed to verify the effectiveness of the proposed method. The results show that the proposed method effectively suppresses the crosstalk between viewpoints and eliminates artifacts in adjacent viewing areas within the ideal viewing angle, thereby enhancing the actual viewing angle. Meanwhile, due to the ability of reallocating information on the SI, the proposed method can achieve varying information density from different views. It is expected that our proposed method could be widely used in light field display to realize high quality display in the future.

Differential evolution optimization based ensemble framework for accurate cervical cancer diagnosis

Cervical cancer remains a significant global health concern, demanding accurate and efficient diagnostic approaches for early detection and treatment. However, there are situations where a single deep learning model might not be sufficient to adequately capture the pertinent information required for precise disease prediction from intricate datasets. In this paper, we introduce an innovative ensemble learning framework that leverages the Differential Evolution (DE) algorithm to enhance the detection of cervical cancer. Our innovative approach combines the collective strength of a diverse set of base models using feature fusion techniques to achieve precise and robust classification. We have developed three base models, denoted as Fusion 1, Fusion 2, and Fusion 3, where each fusion model incorporates two pre-trained models using a feature fusion technique. To capture sequential dependencies and improve feature attention, we have integrated ConvLSTM layers and Squeeze-and-Excitation (SE) blocks into these base models. Subsequently, we've amalgamated the predictions of each base model using a weighted aggregation scheme, with the weights optimized using the DE algorithm. Our ensemble framework introduces a novel method for dynamic weight adjustment, utilizing advanced fusion techniques to improve cervical cancer detection accuracy. This approach ensures robust diagnostic performance across cancer cells datasets, enhancing patient outcomes and community health. Our evaluation covers two datasets, SIPaKMeD and Mendeley LBC, demonstrating the remarkable performance and robustness of our ensemble model. It consistently outperforms individual base models and even surpasses state-of-the-art methods, achieving an impressive accuracy rate of 98.96 % on the Mendeley LBC dataset and 95.68 % on the SIPaKMeD dataset. The incorporation of ConvLSTM layers, SE blocks, and DE optimization significantly contributes to the model's exceptional accuracy, its ability to generalize effectively, and its adaptability to diverse scenarios. Additionally, we employ Grad-CAM to visualize how the base models focus on crucial regions for accurate predictions, providing insights into the model's decision-making process. Our ensemble model exhibits outstanding Area under the Curve (AUC) values in Receiver Operating Characteristic (ROC) analysis, further confirming its effectiveness. This research represents a significant advancement in cervical cancer detection, underscoring the potential of ensemble learning techniques optimized through DE for enhancing diagnostic accuracy and advancing patient care.

Design-Based Theory for Lasso Adjustment in Randomized Block Experiments and Rerandomized Experiments

Blocking, a special case of rerandomization, is routinely implemented in the design stage of randomized experiments to balance the baseline covariates. This study proposes a regression adjustment method based on the least absolute shrinkage and selection operator (Lasso) to efficiently estimate the average treatment effect in randomized block experiments with high-dimensional covariates. We derive the asymptotic properties of the proposed estimator and outline the conditions under which this estimator is more efficient than the unadjusted one. We provide a conservative variance estimator to facilitate valid inferences. Our framework allows one treated or control unit in some blocks and heterogeneous propensity scores across blocks, thus including paired experiments and finely stratified experiments as special cases. We further accommodate rerandomized experiments and a combination of blocking and rerandomization. Moreover, our analysis allows both the number of blocks and block sizes to tend to infinity, as well as heterogeneous treatment effects across blocks without assuming a true outcome data-generating model. Simulation studies and two real-data analyses demonstrate the advantages of the proposed method.

Comparing the Psychophysical Capabilities on Fingertip and Wrist using Method of Adjustment

Haptic technology, which refers to creating the sense of touch artificially, offers a crucial source of communication between humans and computers or machines. While conventional haptic devices are designed to render vibrotactile information on the fingertip, recent trends in the field expand the tactile communication to other body locations, like the wrist. Even though the literature has many successful applications showing the validity of such haptic applications, there is no study comparing the user perception for meaningful virtual or teleoperated task scenarios due to the lack of calibration methods between alternative body locations. In this paper, we attempt to compare the perceived intensities at the fingertip and the wrist through psychophysical experiments and to answer: (i) Is there a perpetual difference between the haptic stimuli on the wrist compared to the fingertip? (ii) Is possible to form a reasonable, linear relationship (or a pattern) between the stimuli rendered at the fingertip and the wrist? (iii) If so, do different users require different relationships that would need to be obtained through calibration? We designed a user study with 13 healthy participants, receiving three levels of haptic stimuli at their fingertips while adjusting the intensities of the stimuli rendered at their wrist using the method of adjustments. Our results indicate that there is a linear pattern between the vibrotactile stimuli rendered at the fingertip and the wrist, and each participant exhibits a different pattern. Our results can be used to equalize the perceived intensities of different forms of tactile stimuli for future research investigating the perceived performance under different haptic scenarios.

A self-adaptive machining parameters adjustment method for stabilizing the machining-induced surface roughness

A self-adaptive machining parameters adjustment method for stabilizing the machining-induced surface roughness

The role of age and sex in non-linear dilution adjustment of spot urine arsenic

BackgroundPrevious research introduced V-PFCRC as an effective spot urinary dilution adjustment method for various metal analytes, including the major environmental toxin arsenic. V-PFCRC normalizes analytes to 1 g/L creatinine (CRN) by adopting more advanced power-functional corrective equations accounting for variation in exposure level. This study expands on previous work by examining the impacts of age and sex on corrective functions.MethodsLiterature review of the effects of sex and age on urinary dilution and the excretion of CRN and arsenic. Data analysis included a Data Set 1 of 5,752 urine samples and a partly overlapping Data Set 2 of 1,154 combined EDTA blood and urine samples. Both sets were classified into age bands, and the means, medians, and interquartile ranges for CRN and TWuAs in uncorrected (UC), conventionally CRN-corrected (CCRC), simple power-functional (S-PFCRC), sex-aggregated (V-PFCRC SA), and sex-differentiated V-PFCRC SD modes were compared. Correlation analyses assessed residual relationships between CRN, TWuAs, and age. V-PFCRC functions were compared across three numerically similar age groups and both sexes. The efficacy of systemic dilution adjustment error compensation was evaluated through power-functional regression analysis of residual CRN and the association between arsenic in blood and all tested urinary result modes.ResultsSignificant sex differences in UC and blood were neutralized by CCRC and reduced by V-PFCRC. Age showed a positive association with blood arsenic and TWuAs in all result modes, indicating factual increments in exposure. Sex-differentiated V-PFCRC best matched the sex-age kinetics of blood arsenic. V-PFCRC formulas varied by sex and age and appeared to reflect urinary osmolality sex-age-kinetics reported in previous research.V-PFCRC minimized residual biases of CRN on TWuAs across all age groups and sexes, demonstrating improved standardization efficacy compared to UC and CCRC arsenic.InterpretationSex differences in UC and CCRC arsenic are primarily attributable to urinary dilution and are effectively compensated by V-PFCRC. While the sex and age influence on V-PFCRC formulas align with sex- and age-specific urinary osmolality and assumed baseline vasopressor activities, their impact on correction validity for entire collectives is minimal.ConclusionThe V-PFCRC method offers a robust correction for urinary arsenic dilution, significantly reducing systemic dilution adjustment errors. Its application in various demographic contexts enhances the accuracy of urinary biomarker assessments, benefiting clinical and epidemiological research. V-PFCRC effectively compensates for sex differences in urinary arsenic. Age-related increases in TWuAs are exposure-related and should be additionally accounted for by algebraic normalization, covariate models, or standard range adjustments.

Statistical Methods for Risk Adjustment in Health Care

Statistical Methods for Risk Adjustment in Health Care