Dialect and mathematics performance in African American children who use AAE: Insights from explanatory IRT and error analysis.

Little is known about mismatches between the language of mathematics testing instruments and the rich linguistic repertoires that African American children develop at home and in the community, in part because research paradigms with African American English (AAE) dialect speakers face complex challenges in measurement, historical exclusion, and other social, economic, cultural, and linguistic confounds. The current study aims to provide a proof of concept and novel explanatory item response design that uses error analysis to investigate the relationship between AAE child language and children's mathematics assessment outcomes. Here, we illustrate 2nd and 3rd grade children's qualitative patterns of performance on arithmetic tasks in relation to their AAE dialect use and elaborate a unified framework for examining child and item level linguistic characteristics. Results suggest that children draw upon their emerging (bi)dialectal repertoire with arithmetic problems when selecting appropriate problem-solving strategies on language-formatted problems. The mismatch of assessment language formatting with children's repertoires may disadvantage AAE speakers' strategy selections and result in a language-based performance disadvantage unrelated to mathematical ability. Research designs that look beyond correct/incorrect scoring to examine qualitative patterns of performance in AAE speaking children can provide valuable and oft-overlooked evidence when considering equity in mathematics assessment formats.

Metaphor comprehension and production in verbally able children with Autism Spectrum Disorder.

Research in the field of figurative language processing in Autism Spectrum Disorders (ASD) has demonstrated that autistic individuals experience systematic difficulties in the comprehension of different types of metaphors. However, there is scarce evidence regarding metaphor production skills in ASD. Importantly, the exact source of metaphor processing difficulties in ASD remains largely controversial. The debate has mainly focused on the mediating role of structural language skills (i.e., lexical knowledge) and cognitive abilities (i.e., Theory of Mind and executive functions) in ASD individuals' ability to comprehend and generate metaphors. The present study examines metaphor comprehension and production in 18 Greek-speaking verbally able children with ASD and 31 typically-developing (TD) controls. Participants completed two tasks, namely, a low-verbal multiple-choice sentence-picture matching task that tested their ability to comprehend conventional predicate metaphors, and a sentence continuation task that assessed their ability to generate metaphors. The study also included measures of fluid intelligence, expressive vocabulary, and working memory within the sample. The results show that the ASD group had significantly lower performance than the TD group in both metaphor comprehension and production. The findings also reveal that expressive vocabulary skills were a key factor in the metaphor comprehension and production performance of the children with ASD. Working memory capacity was also found to correlate significantly with metaphor comprehension performance in the ASD group. Conversely, no correlations were found in the TD group with neither of the above factors. Of note, children with ASD generated significantly more inappropriate responses and no-responses to the metaphor production task compared with the control group. The overall results reveal that children with ASD had difficulty with both comprehending and using metaphorical language. The findings also indicate that TD children may employ diverse cognitive strategies or rely on different underlying skills when processing metaphors compared with children with ASD.

Impacts of vertical variations in soil properties on H*(10) simulations for 137Cs deposition.

Photon transport simulations based on the Monte Carlo method have played a crucial role in assessing and estimating the ambient dose equivalent rates H*(10), resulting from the deposition of 137Cs in soil following the nuclear power plant accident in Fukushima. However, a comprehensive examination of the effect of vertical variations in soil properties on the simulation outcomes has not yet been performed. Disregarding the vertical distribution of soil properties not only leads to potential inaccuracies in the shielding responses of soil layers but also in the determination of the radioactive source inventory, particularly when using the concentration data in Bq/kg. These oversights diminish the reliability of the simulation results. This study addresses several soil property factors that could potentially influence the simulation results, including variations in chemical composition induced by water content, bulk density profile, and estimated inventory profile, all evaluated through an examined simulation model. The results show that inappropriate assignment of the soil density profile can cause considerable errors in the H*(10) simulation outcomes. Furthermore, the sensitivity of H*(10) to variations in soil vertical density is analyzed, with the results indicating that H*(10) can be highly sensitive to changes in the bulk density of the top 0-5 cm soil layers. These results should facilitate the establishment of appropriate simulation strategies and support the reassessment of past simulation results.

Measurement Error Analysis of Seawater Refractive Index: A Measurement Sensor Based on a Position-Sensitive Detector.

The seawater refractive index is an essential parameter in ocean observation, making its high-precision measurement necessary. This can be effectively achieved using a position-sensitive detector-based measurement system. However, in the actual measurement process, the impact of the jitter signal measurement error on the results cannot be ignored. In this study, we theoretically analysed the causes of long jitter signals during seawater refractive index measurements and quantified the influencing factors. Through this analysis, it can be seen that the angle between the two windows in the seawater refractive index measurement area caused a large error in the results, which could be effectively reduced by controlling the angle to within 2.06°. At the same time, the factors affecting the position-sensitive detector's measurement accuracy were analysed, with changes to the background light, the photosensitive surface's size, and the working environment's temperature leading to its reduction. To address the above factors, we first added a 0.9 nm bandwidth, narrow-band filter in front of the detector's photosensitive surface during system construction to filter out any light other than that from the signal light source. To ensure the seawater refractive index's measuring range, a position-sensitive detector with a photosensitive surface size of 4 mm × 4 mm was selected; whereas, to reduce the working environment's temperature variation, we partitioned the measurement system. To validate the testing error range of the optimised test system, standard seawater samples were measured under the same conditions, showing a reduction in the measurement system's jitter signal from 0.0022 mm to 0.0011 mm, before and after optimisation, respectively, as well as a reduction in the refractive index's deviation. The experimental results show that the refractive index of seawater was effectively reduced by adjusting the measurement system's optical path and structure.

Quantifying uncertainty in trans-membrane stresses and moments in simulation.

The lateral stress profile of a lipid bilayer constitutes a valuable link between molecular simulation and mesoscopic elastic theory. Even though it is frequently calculated in simulations, its statistical precision (or that of observables derived from it) is often left unspecified. This omission can be problematic, as uncertainties are prerequisite to assessing statistical significance. In this chapter, we provide a comprehensive yet accessible overview of the statistical error analysis for the lateral stress profile. We detail two relatively simple but powerful techniques for generating error bars: block-averaging and bootstrapping. Combining these methods allows us to reliably estimate uncertainties, even in the presence of both temporal and spatial correlations, which are ubiquitous in simulation data. We illustrate these techniques with simple examples like stress moments, but also more complex observables such as the location of stress profile extrema and the monolayer neutral surface.

Error Correction of the RapidEye Sub-Pixel Correlation: A Case Study of the 2019 Ridgecrest Earthquake Sequence.

The optical image sub-pixel correlation (SPC) technique is an important method for monitoring large-scale surface deformation. RapidEye images, distinguished by their short revisit period and high spatial resolution, are crucial data sources for monitoring surface deformation. However, few studies have comprehensively analyzed the error sources and correction methods of the deformation field obtained from RapidEye images. We used RapidEye images without surface deformation to analyze potential errors in the offset fields. We found that the errors in RapidEye offset fields primarily consist of decorrelation noise, orbit error, and attitude jitter distortions. To mitigate decorrelation noise, the careful selection of offset pairs coupled with spatial filtering is essential. Orbit error can be effectively mitigated by the polynomial fitting method. To address attitude jitter distortions, we introduced a linear fitting approach that incorporated the coherence of attitude jitter. To demonstrate the performance of the proposed methods, we utilized RapidEye images to extract the coseismic displacement field of the 2019 Ridgecrest earthquake sequence. The two-dimensional (2D) offset field contained deformation signals extracted from two earthquakes, with a maximum offset of 2.8 m in the E-W direction and 2.4 m in the N-S direction. A comparison with GNSS observations indicates that, after error correction, the mean relative precision of the offset field improved by 92% in the E-W direction and by 89% in the N-S direction. This robust enhancement underscores the effectiveness of the proposed error correction methods for RapidEye data. This study sheds light on large-scale surface deformation monitoring using RapidEye images.

A mathematical fractal-fractional model to control tuberculosis prevalence with sensitivity, stability, and simulation under feasible circumstances.

BACKGROUND: Tuberculosis, a global health concern, was anticipated to grow to 10.6 million new cases by 2021, with an increase in multidrug-resistant tuberculosis. Despite 1.6 million deaths in 2021, present treatments save millions of lives, and tuberculosis may overtake COVID-19 as the greatest cause of mortality. This study provides a six-compartmental deterministic model that employs a fractal-fractional operator with a power law kernel to investigate the impact of vaccination on tuberculosis dynamics in a population. METHODS: Some important characteristics, such as vaccination and infection rate, are considered. We first show that the suggested model has positive bounded solutions and a positive invariant area. We evaluate the equation for the most important threshold parameter, the basic reproduction number, and investigate the model's equilibria. We perform sensitivity analysis to determine the elements that influence tuberculosis dynamics. Fixed-point concepts show the presence and uniqueness of a solution to the suggested model. We use the two-step Newton polynomial technique to investigate the effect of the fractional operator on the generalized form of the power law kernel. RESULTS: The stability analysis of the fractal-fractional model has been confirmed for both Ulam-Hyers and generalized Ulam-Hyers types. Numerical simulations show the effects of different fractional order values on tuberculosis infection dynamics in society. According to numerical simulations, limiting contact with infected patients and enhancing vaccine efficacy can help reduce the tuberculosis burden. The fractal-fractional operator produces better results than the ordinary integer order in the sense of memory effect at diffract fractal and fractional order values. CONCLUSION: According to our findings, fractional modeling offers important insights into the dynamic behavior of tuberculosis disease, facilitating a more thorough comprehension of their epidemiology and possible means of control.

The development of English language connected speech perception skills: an empirical study on Chinese EFL children.

Connected speech processes (CSPs) occur randomly in everyday conversations of native speakers; however, such phonological variations can bring about challenges for non-native listeners. Looking at CSP literature, there seems to be very few studies that involved young foreign language learners. Therefore, the present study aimed to explore the development of connected speech perception skills by focusing on 201 9- to 12-year-old Chinese EFL children. It also incorporated systematic error analysis to further probe into the specific perceptual difficulties. The results indicate that: (1) Despite a significantly ascending trend for the overall growth of perception skills, no significant differences were found between 11 and 12 year olds in elision and contraction, which suggests that the developmental trend varied depending on different CSP types; (2) Although random errors decreased with age, the number of lexicon and syntax errors gradually increased, and the distribution of perceptual errors shifted from the level of words and syllables to that of phonemes; (3) The primary types of errors resulting in the perception difficulties for elision and contraction were consonant errors, grammatical errors and morphology errors. Ergo, this study enhances the understanding of connected speech perception among EFL children and provides some implications for EFL/ESL listening instructions.

An evaluation of resolution, accuracy, and precision in FT-IR spectroscopy.

Infrared spectroscopy is a foundational technique for the elucidation of chemical structures. The advancements in interferometric spectroscopy, and specifically the development of Fourier transform infrared (FT-IR) spectroscopy, are responsible for the widespread usage of IR spectrometers ranging from teaching labs to pharmaceutical quality control. FT-IR affords an excellent signal-to-noise ratio that permits sensitive sampling with quantitative accuracy and high wavenumber precision based on well documented advantages (Jacquinot, Fellgett, Connes). However, the effect of resolution and instrument-to-instrument variation on wavenumber accuracy is not well understood, with previous work grossly overestimating error. Here, a recommendation of wavenumber accuracy as a function of spectral resolution, accounting for instrument variation among leading manufacturers, is given based on an experimental study of polystyrene and acetaminophen. For peaks that are well resolved and not saturated, the position can be known within 1.1 cm-1 at a spectral resolution of 4 cm-1 or higher, and within 2.2 cm-1 at 8 cm-1 resolution. Other sources of variation are also discussed (e.g., poorly resolved peaks, peak saturation, water interference, spectral noise) to give general recommendations on when IR peak positions can be considered significantly different. Such guidelines are critical for interpreting subtle positional variations, as are often present in different crystal forms of pharmaceuticals.

Patterns of prospective memory errors differ in persons with multiple sclerosis.

INTRODUCTION: Prospective memory (PM) deficits have been documented in multiple sclerosis (MS). This study aimed to explore the specific types of errors made by persons with MS (PwMS), including differences between PwMS and healthy controls (HC) and PwMS who do and do not have impairments in processing speed and/or verbal learning and memory. METHOD: PwMS (n = 111) and HC (n = 75) completed the Memory for Intentions Test (MIST), an objective measure of PM that has five types of errors that can be coded (PM failure, task substitution, loss of content, loss of time, and random errors). The number and types of PM errors were calculated for the overall MIST and six subscales, which break down performance by types of delay (2-Minute and 15-Minute), cue (Time and Event), and response (Verbal and Action). Impairment was defined as performing < 1.5 SD on either the Symbol Digit Modalities Test (SDMT) or Rey Auditory Verbal Learning Test (RAVLT). Bivariate analyses were used to examine group differences, with post-hoc pairwise comparisons with Bonferroni corrections. RESULTS: Nearly 93% of PwMS made at least one PM error, compared to 76% of HC (V = .24, p = .001). The most commonly made PM error by PwMS was loss of content errors (45.0%). PwMS made significantly more task substitution errors (26.4% vs. 7.6%, p < .001) and fewer loss of time errors (9.5% vs. 21.2%, p < .001) than HC. Impaired PwMS made more errors than non-impaired PwMS, specifically PM failures on time-based tasks. CONCLUSIONS: PM errors are common in PwMS, particularly when there are longer delays and time-based cues. Not only do PwMS make more errors than demographically similar HC, but they exhibit different cognitive process failures.

A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.

BACKGROUND: Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting error analysis can be complex, requiring a combination of NLP expertise and domain-specific knowledge. Due to the high heterogeneity of electronic health record (EHR) settings across different institutions, challenges may arise when attempting to standardize and reproduce the error analysis process. OBJECTIVES: This study aims to facilitate a collaborative effort to establish common definitions and taxonomies for capturing diverse error types, fostering community consensus on error analysis for clinical concept extraction tasks. MATERIALS AND METHODS: We iteratively developed and evaluated an error taxonomy based on existing literature, standards, real-world data, multisite case evaluations, and community feedback. The finalized taxonomy was released in both .dtd and .owl formats at the Open Health Natural Language Processing Consortium. The taxonomy is compatible with several different open-source annotation tools, including MAE, Brat, and MedTator. RESULTS: The resulting error taxonomy comprises 43 distinct error classes, organized into 6 error dimensions and 4 properties, including model type (symbolic and statistical machine learning), evaluation subject (model and human), evaluation level (patient, document, sentence, and concept), and annotation examples. Internal and external evaluations revealed strong variations in error types across methodological approaches, tasks, and EHR settings. Key points emerged from community feedback, including the need to enhancing clarity, generalizability, and usability of the taxonomy, along with dissemination strategies. CONCLUSION: The proposed taxonomy can facilitate the acceleration and standardization of the error analysis process in multi-site settings, thus improving the provenance, interpretability, and portability of NLP models. Future researchers could explore the potential direction of developing automated or semi-automated methods to assist in the classification and standardization of error analysis.

Identifying and preventing human error in the sugar production process: A multi-stage approach using HTA, HEC and PHEA techniques.

This article discusses the importance of identifying and preventing human error in industrial environments, specifically in the sugar production process. The article emphasizes the importance of choosing the right technique for risk assessment studies resulting from human errors. A cross-sectional study was conducted using a multi-stage approach - Hierarchical Task Analysis (HTA), Human Error Calculator (HEC), and Predictive Human Error Analysis (PHEA) - to identify potential human errors in the sugar production process. The HTA, HEC, and PHEA techniques were employed to evaluate each stage of the process for potential human errors. The results of the HTA technique identified 35 tasks and 83 sub-tasks in 14 units of the sugar production process. According to HEC technique 4 tasks with 80 % probability of human error and 2 tasks with 50 % probability of human error had the highest calculated error probabilities. The factors of individual skill, task repetition and importance were the most important factors of human error in the present study. The analysis of PHEA worksheets showed that the number of human errors identified in the tasks with highest probability were 8 errors, of which 50 % were action errors, 25 % checking errors, 13 % selection errors, and 12 % retrieval errors. To mitigate the consequences of human error, it was recommended training courses, raising operator awareness of error consequences, and installing instructions in the sugar production process. Based on the findings, the article concludes that the HEC and PHEA techniques are applicable and effective in identifying and analyzing human errors in process and food industries.

Sixth order compact multi-phase block-AGE iteration methods for computing 2D Helmholtz equation.

We discuss sixth order accurate 9-point compact 2- and 3-phase block alternating group explicit (block-AGE) iteration methods for computing 2D Helmholtz equation. We use Dirichlet boundary conditions and no fictitious points are involved outside the solution region for computation. The proposed 2- and 3-phase block-AGE methods require only two and three sweeps for computation and the error analysis of the suggested approximation is analyzed. We have compared the 2- and 3-phase block-AGE iteration methods with the corresponding block successive over relaxation (block-SOR) method in three experiments, in regard to number of iterations required for convergence and cpu time, where the importance of the role performed by optimal relaxation parameters of the proposed block-AGE iteration methods become evident in stipulating the convergence and precision of the calculated results. In all cases we use the tridiagonal solver and obtain the optimal relaxation parameters through computation.

A Novel Methodology for Measuring Ambient Thermal Effects on Machine Tools.

Large machine tools are critically affected by ambient temperature fluctuations, impacting their performance and the quality of machined products. Addressing the challenge of accurately measuring thermal effects on machine structures, this study introduces the Machine Tool Integrated Inverse Multilateration method. This method offers a precise approach for assessing geometric error parameters throughout a machine's working volume, featuring a low level of uncertainty and high speed suitable for effective temperature change monitoring. A significant innovation is found in the capability to automatically realise the volumetric error characterisation of medium- to large-sized machine tools at intervals of 40-60 min with a measurement uncertainty of 10 µm. This enables the detailed study of thermal errors which are generated due to variations in ambient temperature over extended periods. To validate the method, an extensive experimental campaign was conducted on a ZAYER Arion G™ large machine tool using a LEICA AT960™ laser tracker with four wide-angle retro-reflectors under natural workshop conditions. This research identified two key thermal scenarios, quasi-stationary and changing environments, providing valuable insights into how temperature variations influence machine behaviour. This novel method facilitates the optimization of machine tool operations and the improvement of product quality in industrial environments, marking a significant advancement in manufacturing metrology.

Analysis and Compensation of Phase Current Measuring Error Caused by Sensing Resistor in PMSM Application.

Field Oriented Control (FOC) effectively realizes independent control of flux linkage and torque, and is widely used in application of Permanent Magnet Synchronous Motor (PMSM). However, it is necessary to detect the phase current information of the motor to realize the current closed-loop control. The phase current detection method based on a sampling resistor will cause a measurement error due to the influence of parasitic parameters of the sampling resistor, which will lead to the decrease in PMSM control performance. This paper reveals the formation mechanism of the current sampling error caused by parasitic inductance and capacitance of the sampling resistor, and further confirms that the above error will lead to the fluctuation of the electromagnetic torque output by simulation. Moreover, we propose an approach for online observation and compensation of the current sampling error based on PI-type observer to suppresses the torque pulsation of PMSM. The phase current sampling error is estimated by the proportional and integral (PI) observer, and the deviation value of current sampling is obtained by low-pass filter (LPF). The above deviation value is further injected into the phase current close-loop for error compensation. The PI observer continues to work to keep the current sampling error close to zero. The simulation platform of Matlab/Simulink (Version: R2021b) is established to verify the effectiveness of online error observation and compensation. Further experiments also prove that the proposed method can effectively improve the torque fluctuation of the PMSM and enhance its control accuracy performance of rotation speed.

Design and Optimization of UAV Aerial Recovery System Based on Cable-Driven Parallel Robot.

Aerial recovery and redeployment can effectively increase the operating radius and the endurance of unmanned aerial vehicles (UAVs). However, the challenge lies in the effect of the aerodynamic force on the recovery system, and the existing road-based and sea-based UAV recovery methods are no longer applicable. Inspired by the predatory behavior of net-casting spiders, this study introduces a cable-driven parallel robot (CDPR) for UAV aerial recovery, which utilizes an end-effector camera to detect the UAV's flight trajectory, and the CDPR dynamically adjusts its spatial position to intercept and recover the UAV. This paper establishes a comprehensive cable model, simultaneously considering the elasticity, mass, and aerodynamic force, and the static equilibrium equation for the CDPR is derived. The effects of the aerodynamic force and cable tension on the spatial configuration of the cable are analyzed. Numerical computations yield the CDPR's end-effector position error and cable-driven power consumption at discrete spatial points, and the results show that the position error decreases but the power consumption increases with the increase in the cable tension lower limit (CTLL). To improve the comprehensive performance of the recovery system, a multi-objective optimization method is proposed, considering the error distribution, power consumption distribution, and safety distance. The optimized CTLL and interception space position coordinates are determined through simulation, and comparative analysis with the initial condition indicates an 83% reduction in error, a 62.3% decrease in power consumption, and a 1.2 m increase in safety distance. This paper proposes a new design for a UAV aerial recovery system, and the analysis lays the groundwork for future research.

Projection-Angle-Sensor-Assisted X-ray Computed Tomography for Cylindrical Lithium-Ion Batteries.

X-ray computed tomography (XCT) has become a powerful technique for studying lithium-ion batteries, allowing non-destructive 3D imaging across multiple spatial scales. Image quality is particularly important for observing the internal structure of lithium-ion batteries. During multiple rotations, the existence of cumulative errors and random errors in the rotary table leads to errors in the projection angle, affecting the imaging quality of XCT. The accuracy of the projection angle is an important factor that directly affects imaging. However, the impact of the projection angle on XCT reconstruction imaging is difficult to quantify. Therefore, the required precision of the projection angle sensor cannot be determined explicitly. In this research, we selected a common 18650 cylindrical lithium-ion battery for experiments. By setting up an XCT scanning platform and installing an angle sensor to calibrate the projection angle, we proceeded with image reconstruction after introducing various angle errors. When comparing the results, we found that projection angle errors lead to the appearance of noise and many stripe artifacts in the image. This is particularly noticeable in the form of many irregular artifacts in the image background. The overall variation and residual projection error in detection indicators can effectively reflect the trend in image quality. This research analyzed the impact of projection angle errors on imaging and improved the quality of XCT imaging by installing angle sensors on a rotary table.

Rethinking the applicability domain analysis in QSAR models.

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.

Deep analysis of adsorption isotherm for rapid sorption of Acid Blue 93 and Reactive Red 195 on reactive graphene.

Graphene-based adsorbent was prepared by adopting a green synthetic route via the chemical exfoliation of graphite and low-temperature thermal activation. Prepared reactive graphene (RG) was characterized through various techniques, and its adsorption capabilities for textile dye removal were investigated for Acid Blue-93 (AB) and Reactive Red-195 (RR) under different operational conditions. The dye sorption equilibrium and mechanism were comprehensively studied using isotherm and kinetic models and compared statistically to explain the sorption behavior. Results show AB and RR adsorption by RG attains equilibrium in 60 min and 70 min, with a high sorption quantity of 397 mg g-1 and 262 mg g-1 (initial dye concentration of 100 mg L-1), respectively. The dye sorption anticipates that the high surface area (104.52 m2 gm-1) and constructed meso-macroporous features of RG facilitated the interaction between the dye molecules and graphitic skeleton. The R-P isotherm fitted the best of equilibrium data, having the least variance in residuals for both dyes (AB = 0.00031 and RR = 0.00047). The pseudo-second order model best fitted the kinetics of sorption on RG, with chemisorption being the predominant process delimiting step. The overall results promise the dye removal capability of RG to be an efficient adsorbent for azo-based dyes from textile effluents.

A Novel Method for Precision Measurement and Result Optimization of Detuning Angle for KDP Crystals.

In this paper, we investigate the theory of energy distribution when divergent light undergoes harmonic conversion in KDP crystals, and based on this theory, we design and construct a precision measuring instrument for the detuning angle of (KDP) Crystals (MIDC). The device can obtain the detuning angle of the crystal by a single measurement with an average measurement error of 72.78 urad. At the same time, it also has the function of scanning the full aperture of the crystals. Using the MIDC, it is possible to quickly measure the KDP crystal at a single point and quickly scan the crystal detuning angle at full aperture. In addition, we conduct a theoretical study on the variation of detuning angle caused by gravity-influencing factors under online conditions, propose an optimization formula for the offline measurement results of detuning angle, and calculate the optimized values of detuning angle for two kinds of crystals under 45° online conditions. We finally study the error source of the MIDC device, analyze the trend of the influence of positioning errors of the crystal and optical elements on the detuning angle measurement results, and provide theoretical support for the error monitoring and correction of MIDC.