A novel nanocomposite for photocatalytic rhodamine B dye removal from wastewater using visible light.

Providing safe access to water and addressing the impact of waterborne diseases, which claim over two million lives annually, is a major contribution to water purification. The study introduces a novel nanocomposite, Ch/Fe3O4/α-MoO3, which exhibits outstanding photocatalytic efficacy under visible light. An in-depth investigation of the nanocomposite's synthesis, characterization, and photodegradation mechanisms reveals its outstanding capabilities. Photocatalytic activity is influenced by the catalytic dose, pH, dye concentration, and reaction time, according to the study. A response surface method is used to determine the optimal conditions for Rhodamine B degradation, which results in 96.3% removal efficiency at pH 8.5, dye concentration 25 mg/L, nanocomposite dose at 22 mg/L, and reaction time 50 min. As a result of its high surface area, biocompatibility, availability, and magnetization with iron compounds, Chitosan is an excellent substrate for enhancing the photocatalytic properties of MoO3 nanoparticles. A nanocomposite with an energy band of 3.18 eV exhibits improved visible light absorption. This study confirms the nanocomposite's recyclability and stability, affirming its practicality. Besides dye removal, it offers hope for the global quest for clean water sources by addressing a broader range of waterborne contaminants. By combining molybdenum and magnetite, nanocomposite materials facilitate the degradation of pollutant and bacteria, contributing positively to society's quest for clean and safe water. It emphasizes the role nanotechnology plays in preserving human health and well-being in combating waterborne diseases.

Assessment of nonlinear dose-response relationships via nonparametric regression.

We propose a simple approach to assess whether a nonlinear parametric model is appropriate to depict the dose-response relationships and whether two parametric models can be applied to fit a dataset via nonparametric regression. The proposed approach can compensate for the ANOVA, which is sometimes conservative, and is very easy to implement. We illustrate the performance by analyzing experimental examples and a small simulation study.

Optimization of traction parameters for lumbar scoliosis.

BACKGROUND: Scoliosis is a high incidence disease that endangers the physical and mental health of adolescents. Traction therapy, as a conservative treatment plan, is helpful to improve the recovery speed of patients by studying the influence of different traction factors on the therapeutic effect. METHODS: Based on the thin layer CT data of the lumbar spine of a 16-year-old patient with scoliosis, Mimics21.0 was used to extract the 3D digital model, and Geomagic Wrap2021 was used to perform the smooth surface. After that, SolidWorks was used to manually construct the structures, such as the intervertebral disc, and Ansys17.0 was used to add constraints, ligaments, and other features. Three-factor ANOVA was carried out after an orthogonal experiment that considered traction mode, traction angle, and traction force was finished. RESULTS: ① A three-dimensional biomechanical model of lumbar scoliosis was created. ② The model's correctness was confirmed by comparing it to the corpse and other finite element models, as well as by verifying it under a range of working settings. ③ Traction force (P = 0.000), traction angle (P = 0.000), the interaction between traction force and traction angle (P = 0.000), and the interaction between traction mode and traction angle (P = 0.045) were all significant. ④ The interaction between traction force and traction angle has the most significant effect on Cobb, and traction with a certain angle is better than traditional axial traction. ⑤ Traction mode is not significant, but the interaction between traction mode and traction angle is significant. CONCLUSIONS: A certain angle of traction can aid in improving outcomes and the traction force can be suitably decreased in the clinical formulation of the traction plan. The uniformity of correcting effect is more favorable when higher fixation techniques like positive suspension or traction bed traction are used, as opposed to overhanging traction.

Evaluating the Diagnostic Performance of Large Language Models on Complex Multimodal Medical Cases.

Large language models showed interpretative reasoning in solving diagnostically challenging medical cases.

The interval between the onset of increased blood pressure and proteinuria in preeclampsia and the contributing factors.

PURPOSE: New-onset proteinuria, as a pivotal sign of representative renal lesions in preeclampsia, is still the most common diagnostic tool for this condition and has been proven to be related to a significantly abnormal sFlt-1/VEGF ratio in circulation. At the same time, blood pressure control plays a vital role in the occurrence and evolution of proteinuria. Therefore, it is particularly helpful to investigate their interval, not only for performing urinalysis for protein more accurately but also for evaluating blood pressure as well as the aggravation of illness, as the related research is limited. METHODS: This retrospective study included 515 preeclampsia patients and 358 normotensive pregnant women who labored in the Second Hospital of Tianjin Medical University from January 2016 to January 2020. First, we described the onset circumstance of high blood pressure and proteinuria as well as the interval among the case group and the subgroups. Then, we determined whether there were significant differences in the basic information, laboratory test results, and newborns between the case and normal groups. Finally, multifactor ANOVA was used to determine the factors influencing the interval. RESULTS: 1. The two most common complications in preeclampsia were proteinuria (88.35%) and placental dysfunction (5.05%). Moreover, 72.04% of preeclampsia cases were diagnosed by abnormal blood pressure together with new-onset proteinuria. 2. The average interval between high blood pressure and proteinuria was 22 gestational days (from 0 to 106 days), and this interval was not significantly different between mild and severe PE (26 days vs. 21 days, P > 0.05) but significantly differed between early-onset and late-onset PE (9 days vs. 28 days, P < 0.05). 3. The number of prenatal visits, serum creatinine in the early trimester, gestational time and diastolic blood pressure value when increased blood pressure was initially detected may influence the interval between the onset of increased blood pressure and proteinuria. CONCLUSION: New-onset proteinuria was still the main parameter for identifying preeclampsia. The interval between increased blood pressure and proteinuria was probably related to the imbalance in the sFlt-1/VEGF ratio; therefore, we should pay attention to monitor proteinuria during the prenatal visits, especially for patients with a lower frequency of prenatal visits, higher serum creatinine in the early trimester, earlier onset and higher diastolic blood pressure at the initial onset of increased blood pressure.

Bayesian Analysis of Multi-Factorial Experimental Designs Using SEM.

Latent repeated measures ANOVA (L-RM-ANOVA) has recently been proposed as an alternative to traditional repeated measures ANOVA. L-RM-ANOVA builds upon structural equation modeling and enables researchers to investigate interindividual differences in main/interaction effects, examine custom contrasts, incorporate a measurement model, and account for missing data. However, L-RM-ANOVA uses maximum likelihood and thus cannot incorporate prior information and can have poor statistical properties in small samples. We show how L-RM-ANOVA can be used with Bayesian estimation to resolve the aforementioned issues. We demonstrate how to place informative priors on model parameters that constitute main and interaction effects. We further show how to place weakly informative priors on standardized parameters which can be used when no prior information is available. We conclude that Bayesian estimation can lower Type 1 error and bias, and increase power and efficiency when priors are chosen adequately. We demonstrate the approach using a real empirical example and guide the readers through specification of the model. We argue that ANOVA tables and incomplete descriptive statistics are not sufficient information to specify informative priors, and we identify which parameter estimates should be reported in future research; thereby promoting cumulative research.

Integrated Wearable System for Monitoring Skeletal Muscle Force of Lower Extremities.

Continuous monitoring of lower extremity muscles is necessary, as the muscles support many human daily activities, such as maintaining balance, standing, walking, running, and jumping. However, conventional electromyography and physiological cross-sectional area methods inherently encounter obstacles when acquiring precise and real-time data pertaining to human bodies, with a notable lack of consideration for user comfort. Benefitting from the fast development of various fabric-based sensors, this paper addresses these current issues by designing an integrated smart compression stocking system, which includes compression garments, fabric-embedded capacitive pressure sensors, an edge control unit, a user mobile application, and cloud backend. The pipeline architecture design and component selection are discussed in detail to illustrate a comprehensive user-centered STIMES design. Twelve healthy young individuals were recruited for clinical experiments to perform maximum voluntary isometric ankle plantarflexion contractions. All data were simultaneously collected through the integrated smart compression stocking system and a muscle force measurement system (Humac NORM, software version HUMAC2015). The obtained correlation coefficients above 0.92 indicated high linear relationships between the muscle torque and the proposed system readout. Two-way ANOVA analysis further stressed that different ankle angles (p = 0.055) had more important effects on the results than different subjects (p = 0.290). Hence, the integrated smart compression stocking system can be used to monitor the muscle force of the lower extremities in isometric mode.

Bootstrap tests for simultaneous monotone ordering of effects in a two-way ANOVA.

In a two-way additive analysis of variance (ANOVA) model, we consider the problem of testing for homogeneity of both row and column effects against their simultaneous ordering. The error variances are assumed to be heterogeneous with unbalanced samples in each cell. Two simultaneous test procedures are developed-the first one using the likelihood ratio test (LRT) statistics of two independent hypotheses and another based on the consecutive pairwise differences of estimators of effects. The parametric bootstrap (PB) approach is used to find critical points of both the tests and the asymptotic accuracy of the bootstrap is established. An extensive simulation study shows that the proposed tests achieve the nominal size and have very good power performance. The robustness of the tests is also analyzed under deviation from normality. An "R" package is developed and shared on "GitHub" for ease of implementation of users. The proposed tests are illustrated using a real data set on the mortality due to alcoholic liver disease and it is shown that age and gender have a significant impact on the increasing incidence of mortality.

Application of multiple-intake temporal check all that apply: a case study of strawberry yoghurt formulated with alternative sweeteners.

BACKGROUND: It is crucial to reduce the high sugar content of fruit yoghurts in response to the excessive weight gain epidemic. The use of alternative sweeteners in yoghurts is often associated with the negative sensory attributes that can have an impact on yoghurt liking. The main objective of this research was to investigate the effect of alternative sweeteners and strawberry puree addition on the temporal sensory profile of yoghurt using multiple-intake temporal check all that apply (TCATA). A novel approach to the statical analysis of the temporal sensory data was employed by using aligned rank transformation-analysis of variance to investigate the differences between sensory attributes within different products and within different intakes. RESULTS: Results showed that the attributes sweet and fruity decreased when the concentration of fruit puree was increased at low concentration of sucrose. Interestingly, when the concentration of fruit puree was increased, fruitiness increased and mouthcoating decreased at low concentration of stevia. With successive intakes, the attributes sweet, sour, creamy and fruity significantly decreased in yoghurts sweetened with sucrose, xylitol and stevia. Yoghurts containing low concentrations of sucrose or xylitol and fruit puree were liked the most. However, stevia-sweetened yoghurts varying in sweetener and puree concentration were not significantly different in liking. In order to investigate the consumer acceptance of yoghurts, a novel approach was used - that is, utilizing TCATA temporal data to investigate temporal drivers of liking for each yoghurt type. CONCLUSION: The use of multiple statistical analysis to analyse temporal data suggested that both sweetener and puree concentration need to be considered when developing products using alternative sweeteners. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

On the bias in the AUC variance estimate.

The area under the Receiver Operating Characteristic (ROC) curve (AUC) is a standard metric for quantifying and comparing binary classifiers. A popular approach to estimating the AUCs and the associated variabilities - the variance of the AUC or the full covariance matrix of multiple correlated AUCs - is the one proposed by DeLong et al [1], which is based on the Mann Whitney two-sample U-statistics. The bias of a variance estimator is an important factor in applications such as hypothesis testing and construction of confidence intervals - a negatively biased variance estimator may lead to incorrect conclusions, and a positive bias is conservative hence preferable. In this work, we show that the (co-)variance estimate in DeLong's approach is always positively biased. More specifically, the difference matrix between the expectation of the estimated covariance and the true covariance is a positive semi-definite matrix. This bias is non-negligible when the sample size is small, and quickly diminishes as the sample size increases. Our method relies on constructing, from the AUC kernel, a random variable whose (co-)variance matrix coincides with the bias, thereby establishing the claim. We also discuss alternative approaches to AUC variance estimation that may potentially reduce the bias.

Recognition and evaluation of mental workload in different stages of perceptual and cognitive information processing using a multimodal approach.

This study explores the effects of different perceptual and cognitive information processing stages on mental workload by assessing multimodal indicators of mental workload such as the NASA-TLX, task performance, ERPs and eye movements. Repeated measures ANOVA of the data showed that among ERP indicators, P1, N1 and N2 amplitudes were sensitive to perceptual load (P-load), P3 amplitude was sensitive to P-load only in the prefrontal region during high cognitive load (C-load) states, and P3 amplitude in the occipital and parietal regions was sensitive to C-load. Among the eye movement indicators, blink frequency was sensitive to P-load in all C-load states, but to C-load in only low P-load states; pupil diameter and blink duration were sensitive to both P-load and C-load. Based on the above indicators, the k-nearest neighbours (KNN) algorithm was used to propose a classification method for the four different mental workload states with an accuracy of 97.89%.Practitioner summary: Based on the results of this study, it is possible to implement the monitoring of mental workload states and optimise brain task allocation in operations involving high mental workload, such as human-computer interaction.

Groundwater quality evaluation of the weathering mantle in crystalline basement aquifer system, southern Brazil.

Groundwater is the main source of water for more than 2 billion people worldwide. In southern Brazil, the Crystalline Basement Aquifer System is composed of strategic groundwater reservoirs. Groundwater is mostly taken from shallow wells, and it is often used without any treatment, which poses a risk to public health. The present study aims to evaluate shallow groundwater quality and the geochemistry of shallow and deep groundwater located in the municipality of Canguçu, southern Brazil. The physicochemical and microbiological parameters of groundwater samples collected from shallow wells were monitored and analyzed using ANOVA variance analysis and water quality index (CCME WQI) approaches. Also, the results were compared with secondary data from deep wells. The monitored shallow wells had thermotolerant coliforms, Escherichia coli, pH, potassium, manganese, iron, and nitrate in disagreement with the guidelines of the World Health Organization. Moreover, variance analysis showed that the parameters temperature, dissolved oxygen, pH, chloride, and magnesium were the most influenced by seasonal variations. According to the CCME WQI, most samples had good quality (60%), 28% had fair quality, and 12% had poor quality. In addition, the field campaigns with higher precipitation rates also presented fair quality. Therefore, most of the shallow groundwater quality is affected by surface pollutants from the urban area, aggravated in rainy periods. Whereas deep groundwater is influenced by geochemistry mechanisms. The results revealed the risk of water consumption for public health and the urgent need for better maintenance of these wells and water treatment implementation.

A survey of statistical methods utilized for analysis of randomized controlled trials of behavioral interventions.

OBJECTIVES: Given the many statistical analysis options used for randomized controlled trials (RCTs) of behavioral interventions and the lack of clear guidance for analysis selection, the present study aimed to characterize the predominate statistical analyses utilized in RCTs in palliative care and behavioral research and to highlight the relative strengths and weaknesses of each of these methods as guidance for future researchers and reform. METHODS: All RCTs published between 2015 and 2021 were systematically extracted from 4 behavioral medicine journals and analyzed based on prespecified inclusion criteria. Two independent raters classified each of the manuscripts into 1 of 5 RCT analysis strategies. RESULTS: There was wide variation in the methods used. The 2 most prevalent analyses for RCTs were longitudinal modeling and analysis of covariance. Application of method varied significantly by sample size. SIGNIFICANCE OF RESULTS: Each statistical analysis presents its own unique strengths and weaknesses. The information resulting from this research may prove helpful for researchers in palliative care and behavioral medicine in navigating the variety of statistical methods available. Future discussion around best practices in RCT analyses is warranted to compare the relative impact of interventions in a more standardized way.

Seasonal influence of physicochemical and climatic parameters on phytoplankton diversity and abundance pattern in community managed semi-impacted floodplain wetland.

Wetlands provide numerous ecological services and are key habitats for aquatic flora and fauna. In the Beledanga wetland, the current study was conducted for 3 years, from July 2019 to June 2021, to evaluate the seasonal influence of physicochemical parameters on phytoplankton diversity and abundance patterns. Overall 48 genera of phytoplankton were observed. Bacillariophyceae (27%) contributed the maximum to the total phytoplankton density. The total abundance of phytoplankton was found utmost during monsoon (4.081 × 103 unit l-1) and least during post-monsoon (3.316 × 103 unit l-1). One-way analysis of variance indicated significant seasonal differences (p < 0.05) for some genera. The study gave the idea about the most influencing physic-chemical parameters (dissolved oxygen, turbidity, total hardness, Ca2+, and total nitrogen) on the growth of phytoplankton with the help of different multivariate and univariate analysis (canonical correspondence analysis and Karl Pearson's correlation). The study again highlighted that climate parameters (temperature and rainfall) had some effect on the phytoplanktonic groups. Our study conceded that N:P in the studied wetland was less than the Redfield ratio (16:1) in all three seasons, while the Si:P ratio was noticed in the high range (15:1) during pre-monsoon. The value of the Shannon diversity index and Margalef's species richness index were noticed to be > 3, which signified quite rich in phytoplankton diversity. But the value of Algal Pollution Index, which describes the ecological pollution level based on the present algal genera was observed high throughout all seasons, indicating organic load. So in future the studied wetland may get adversely affected with influence of anthropogenic activities. Therefore, for sustainable biodiversity of the waterbody, the anthropogenic activities (retting and intensification of agricultural farming) and macrophytes need to be controlled and regulated.

Controlling for false discoveries subsequently to large scale one-way ANOVA testing in proteomics: Practical considerations.

In discovery proteomics, as well as many other "omic" approaches, the possibility to test for the differential abundance of hundreds (or of thousands) of features simultaneously is appealing, despite requiring specific statistical safeguards, among which controlling for the false discovery rate (FDR) has become standard. Moreover, when more than two biological conditions or group treatments are considered, it has become customary to rely on the one-way analysis of variance (ANOVA) framework, where a first global differential abundance landscape provided by an omnibus test can be subsequently refined using various post-hoc tests (PHTs). However, the interactions between the FDR control procedures and the PHTs are complex, because both correspond to different types of multiple test corrections (MTCs). This article surveys various ways to orchestrate them in a data processing workflow and discusses their pros and cons.

Optimization of wear behavior of heat-treated Ti-6Al-7Nb biomedical alloy by response surface methodology.

Titanium-based metals are used most often in biomedical implant studies because they have good qualities like being biocompatible, not being poisonous, Osseo-integration, high specific properties, wear resistance, etc. The main goal of this work is to improve the wear resistance of Ti-6Al-7Nb biomedical metal by using a mix of Taguchi, ANOVA, and Grey Relational Analysis. The effect of changeable control process factors like applied load, spinning speed, and time on wear reaction measures like wear rate (WR), coefficient of friction (COF), and frictional force. The optimal combinations of wear rate, COF, and frictional force minimise wear characteristics. The L9 Taguchi orthogonal array was used to plan the experiments, which were done on a pin-on-disc set-up according to ASTM G99. To find the best set of control factors, Taguchi, ANOVA, and Grey relationship analysis were used. The results show that a load of 30 N, a speed of 700 rpm, and a time of 10 min are the best control settings.

Touchstones of Equivalence and the Houdini Transformation.

Inspired by Peter Molenaar's Houdini transformation, we consider the idea of touchstones between different models. Touchstones represent instances where models that appear different on the surface can have equivalent characteristics. Touchstones can appear as identical tests of model parameters. They can exist in the mean structure, in the covariance structure, or in both. In the latter case, the models will generate identical mean and covariance structures and will fit the data equally well. After showing some examples of touchstones and how they result from constraints on a general model, we show how that idea can suggest Molenaar's Houdini transformation. This transformation allows one to take a latent variable model and derive an equivalent model comprised solely of manifest (observed) variables. As equivalent models, the parameters of one can be transformed into the parameters of the other.

On the Relationship between ANOVA Main Effects and Average Treatment Effects.

We adopt a causal inference perspective to shed light into which ANOVA type of sums of squares (SS) should be used for testing main effects and whether main effects should be considered at all in the presence of interactions. We consider balanced, proportional and nonorthogonal designs, and models with and without interactions. When the design is balanced, we show that the average treatment effect is estimated by the main effects obtained by type I, II, and III sums of squares. In proportional designs, we show that the average treatment effect is estimated by the the type I and type II main effects, whereas type III SS yield biased estimates of the average treatment effect if there are interactions. When the design is nonorthogonal, ANOVA type I is always highly biased and ANOVA type II and III main effects are biased if there are interactions. We include a simulation study to illustrate the magnitude of the bias in estimating the average treatment effect across a variety of conditions, and provide recommendations for applied researchers.

Novel ANOVA-Statistic-Reduced Deep Fully Connected Neural Network for the Damage Grade Prediction of Post-Earthquake Buildings.

Earthquakes are cataclysmic events that can harm structures and human existence. The estimation of seismic damage to buildings remains a challenging task due to several environmental uncertainties. The damage grade categorization of a building takes a significant amount of time and work. The early analysis of the damage rate of concrete building structures is essential for addressing the need to repair and avoid accidents. With this motivation, an ANOVA-Statistic-Reduced Deep Fully Connected Neural Network (ASR-DFCNN) model is proposed that can grade damages accurately by considering significant damage features. A dataset containing 26 attributes from 762,106 damaged buildings was used for the model building. This work focused on analyzing the importance of feature selection and enhancing the accuracy of damage grade categorization. Initially, a dataset without primary feature selection was utilized for damage grade categorization using various machine learning (ML) classifiers, and the performance was recorded. Secondly, ANOVA was applied to the original dataset to eliminate the insignificant attributes for determining the damage grade. The selected features were subjected to 10-component principal component analysis (PCA) to scrutinize the top-ten-ranked significant features that contributed to grading the building damage. The 10-component ANOVA PCA-reduced (ASR) dataset was applied to the classifiers for damage grade prediction. The results showed that the Bagging classifier with the reduced dataset produced the greatest accuracy of 83% among all the classifiers considering an 80:20 ratio of data for the training and testing phases. To enhance the performance of prediction, a deep fully connected convolutional neural network (DFCNN) was implemented with a reduced dataset (ASR). The proposed ASR-DFCNN model was designed with the sequential keras model with four dense layers, with the first three dense layers fitted with the ReLU activation function and the final dense layer fitted with a tanh activation function with a dropout of 0.2. The ASR-DFCNN model was compiled with a NADAM optimizer with the weight decay of L2 regularization. The damage grade categorization performance of the ASR-DFCNN model was compared with that of other ML classifiers using precision, recall, F-Scores, and accuracy values. From the results, it is evident that the ASR-DFCNN model performance was better, with 98% accuracy.

Correlative Method for Diagnosing Gas-Turbine Tribological Systems.

Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry and analyzed with a correlative model. Customized alarm limits were determined for iron by binning aluminum and zinc concentration into four levels. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests was carried out to study the impact of aluminum and zinc concentration on iron concentration. A strong correlation between iron and aluminum, as well as a weaker but still statistically significant correlation between iron and zinc, was observed. When the model was applied to evaluate a selected engine, deviations of iron concentration from the established limits indicated accelerated wear long before the occurrence of critical damage. Thanks to ANOVA, the assessment of engine health was based on a statistically proven correlation between the values of the dependent variable and the classifying factors.