Agreement Errors Research Articles

Age-specific ASPECTS atlas of Chinese subjects across different age groups for assessing acute ischemic stroke

The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a valuable and easy-to-use method for assessing acute ischemic stroke. It aids in identifying suitable candidates for thrombolytic therapies and evaluating treatment effectiveness. However, ASPECTS evaluation primarily relies on visual observation in current clinical practice, lacking a common standardized space. Additionally, different doctors may have varying clinical experiences, leading to a poor inter-reader agreement and potential errors in the final ASPECTS scoring. To address these issues and fill in the absence of a publicly available ASPECTS atlas, this work constructs age-specific Chinese ASPECTS atlases based on non-contrast computed tomography images of 281 healthy subjects across different age groups. Images of different age groups are warped into respective common averaged spaces, where the average intensity atlases are computed. More importantly, 10 ASPECTS regions can be obtained during this process. We develop an automated ASPECTS region mapping pipeline and collect an independent dataset to validate our atlas. The results prove that the age-specific ASPECTS atlas is of great promise in clinical availability.

Validity and Concordance of a Linear Position Transducer (Vitruve) for Measuring Movement Velocity during Resistance Training.

This study aimed to analyze the intra-device agreement of a new linear position transducer (Vitruve, VT) and the inter-device agreement with a previously validated linear velocity transducer (T-Force System, TF) in different range of velocities. A group of 50 healthy, physically active men performed a progressive loading test during a bench press (BP) and full-squat (SQ) exercise with a simultaneous recording of two VT and one TF devices. The mean propulsive velocity (MPV) and peak of velocity (PV) were recorded for subsequent analysis. A set of statistics was used to determine the degree of agreement (Intraclass correlation coefficient [ICC], Lin's concordance correlation coefficient [CCC], mean square deviation [MSD], and variance of the difference between measurements [VMD]) and the error magnitude (standard error of measurement [SEM], smallest detectable change [SDC], and maximum errors [ME]) between devices. The established velocity ranges were as follows: >1.20 m·s-1; 1.20-0.95 m·s-1; 0.95-0.70 m·s-1; 0.70-0.45 m·s-1; ≤0.45 m·s-1 for BP; and >1.50 m·s-1; 1.50-1.25 m·s-1; 1.25-1.00 m·s-1; 1.00-0.75 m·s-1; and ≤0.75 m·s-1 for SQ. For the MPV, the VT system showed high intra- and inter-device agreement and moderate error magnitude with pooled data in both exercises. However, the level of agreement decreased (ICC: 0.790-0.996; CCC: 0.663-0.992) and the error increased (ME: 2.8-13.4% 1RM; SEM: 0.035-0.01 m·s-1) as the velocity range increased. For the PV, the magnitude of error was very high in both exercises. In conclusion, our results suggest that the VT system should only be used at MPVs below 0.45 m·s-1 for BP and 0.75 m·s-1 for SQ in order to obtain an accurate and reliable measurement, preferably using the MPV variable instead of the PV. Therefore, it appears that the VT system may not be appropriate for objectively monitoring resistance training and assessing strength performance along the entire spectrum of load-velocity curve.

Accuracy of oscillometric blood pressure measurement using a Cardell Touch multiparameter monitor in anesthetized pigs.

To assess the accuracy of an oscillometric monitor in anesthetized pigs and its ability to track changes in mean arterial pressure (MAP) and to detect arterial hypotension and hypertension in reference to direct measurements. Mean arterial pressure was measured simultaneously from a catheter in a femoral artery and with an oscillometric cuff placed over the metatarsus in 9 anesthetized pigs (∼6 months old, 35 to 55 kg). Pigs were subjected to maneuvers to alter MAP. Paired values for invasive and noninvasive MAP (iMAP and NiMAP) were recorded every 2 minutes. Bland-Altman plots with bias, limits of agreement, and percentage error were constructed using each pair and the average of 5 consecutive values. Concordance was calculated using changes in MAP at 20-minute intervals. Receiver operating characteristics (ROC) curves were constructed to test NiMAP for detection of hypotension (< 70 mm Hg) and hypertension (≥ 120 mm Hg). Bias of NiMAP was -8.59 mm Hg for consecutive pairs and -8.85 mm Hg for averaged pairs, relative to iMAP. Limits of agreement and percentage error were reduced for averages (19%) over individual pairs (26%). Concordance was 82%. The area under the ROC curve for detecting hypotension with NiMAP was 0.936, with a best cutoff value of 63 mm Hg NiMAP. The area under the ROC curve for hypertension was 0.940, with a best cutoff value of 101 mm Hg NiMAP. Averaging several consecutive values improves the accuracy of NiMAP measurements. This device correctly tracked changes in MAP approximately 80% of the time and appears reliable for diagnosing arterial hypotension.

The effects of teacher, peer and self-feedback on error correction with corpus use

The strengths of corpora in language learning have been stated, while not many studies have explored the effects of feedback on error correction in the settings of data-driven learning (DDL), which is an approach where learners use corpora to learn language patterns inductively. Therefore, this study examines the effects of feedback on second language (L2) error correction with corpus use. The author hypothesizes that seeing many example sentences of the target word(s) with corpus use is useful in correcting L2 errors and that different sources of feedback have different effects on error correction. To test the hypotheses, the effects of teacher feedback on 55 participants’ error correction with use of the Corpus of Contemporary American English (COCA) were compared with those of peer feedback along with those of self-feedback. The results show that teacher feedback especially worked well for correcting omission errors and agreement errors. The strength of teacher feedback was identifying correctable errors. The results suggest that efficient corpus use for error correction requires teachers to consider appropriate combinations of feedback and error types (e.g., teacher feedback for omission errors and agreement errors).

A - 136 Clinical Features of Agrammatism in Spanish Speakers with Non-Fluent/Agrammatic Variant of Primary Progressive Aphasia (Nappa): a Case Study Analysis

Abstract Objective A clinical diagnosis of naPPA includes presence of agrammatism and effortful speech (Gorno-Tempini et al., 2011). Agrammatism, specifically verb agreement, has emerged as a key clinical feature distinguishing PPA subtyping in English (Thomson et al., 2012). Patterns of agrammatism in English are well-established and were thought to extend to other languages (Gorno-Tempini et al., 2004; Benedet et al., 1998). Agrammatism in post-stroke Spanish patients has unique linguistic characteristics including gender-object agreement errors (Benedet et al., 1998; Reznik et al., 1995, Ardila, 2001). Research assessing agrammatism in PPA Spanish speakers is limited (Gonzalez et al., 2022). These three cases describe gender agreement errors as a clinical feature of agrammatism in Spanish speakers with naPPA. Method Three Spanish-dominant patients with primary speech deficits underwent neuropsychological evaluation across three outpatient clinics and were diagnosed with naPPA (all had brain MRI, two had FDG-PET). Narrative language samples were analyzed to explore patterns of agrammatic language production. Results Patients made few to no verb agreement errors. Instead, common errors included inconsistent speech sounds and gender disagreement within common object-article pairings, including substitutions in Spanish-language articles like “el” or “la” (e.g., “el silla”). Conclusions These cases revealed gender-object agreement errors as a consistent clinical feature of agrammatism in Spanish naPPA patients. Research assessing the linguistic profiles and agrammatisms of Spanish-speaking PPA patients, with confirmatory neuroimaging and biomarker correlates, is needed to aid clinicians in differentiating PPA subtypes among Spanish speakers. This is especially important with emergence of new disease modifying treatments specifically helpful in some but not all subtypes.

Evaluation of several routine methods for fosfomycin and mecillinam susceptibility testing of Enterobacterales urine isolates.

Performance evaluation of routine laboratory methods to determine the susceptibility of Enterobacterales urinary isolates to fosfomycin (oral administration) and mecillinam. We collected 347 Enterobacterales isolates from monomicrobial midstream urine samples from women with significant bacteriuria and leukocyturia. Mostly non-Escherichia coli isolates (i.e. Klebsiella spp., Citrobacter koseri, Enterobacter cloacae complex and Proteus mirabilis) were included (n = 298). Performance of VITEK®2, ETEST®, and disc diffusion to determine fosfomycin and mecillinam susceptibility was evaluated following International Organization for Standardization (ISO) 20776-2:2021 (or 20776-2:2007 for disc diffusion) in comparison with the agar dilution reference method. For fosfomycin testing, VITEK®2 and ETEST® were close to reaching ISO requirements (essential agreement ≥ 90%; bias ±30%) for C. koseri, E. coli and P. mirabilis. Categorical agreement (CA) and major error rates were acceptable for disc diffusion. Fosfomycin displayed lower activity against E. cloacae complex and Klebsiella spp., with MIC50 (minimum inhibitory concentration required to inhibit the growth of 50% of tested isolates) equal to the E. coli EUCAST breakpoint (8 mg/L). For these species, the three alternative techniques overestimated MICs and resistance, and did not meet performance criteria. For mecillinam testing of Enterobacterales isolates, apart from P. mirabilis, ETEST® nearly fulfilled ISO requirements, and CA rates were acceptable for disc diffusion. ISO criteria were reached for C. koseri and E. coli testing with VITEK®2, apart from too high rates of very major errors. For P. mirabilis, performances were unacceptable, whatever the routine method used. Commercially available tests may serve as alternatives to agar dilution to assess fosfomycin (oral) and mecillinam susceptibility of Enterobacterales urinary isolates, with important interspecies variabilities. Additional studies comprising more fosfomycin- and mecillinam-resistant isolates are needed to strengthen our conclusions.

English grammar intelligent error correction technology based on the n-gram language model

Abstract With the development of the Internet, the number of electronic texts has increased rapidly. Automatic grammar error correction technology is an effective safeguard measure for the quality of electronic texts. To improve the quality of electronic text, this study introduces a moving window algorithm and linear interpolation smoothing algorithm to build a Cn-gram language model. On this basis, a syntactic analysis strategy is introduced to construct a syntactic error correction model integrating Cn-gram and syntactic analysis, and English grammar intelligent error correction is carried out through the model. The results show that compared with the Bi-gram and Tri-gram, the precision of the Cn-gram model is 0.85 and 0.91% higher, and the F1 value is 0.97 and 1.14% higher, respectively. Compared with the results of test set Long, the Cn-gram model has better performance on verb error correction of the Short test set, and the precision rate, recall rate, and F1 value are increased by 0.86, 3.94, and 1.87%, respectively. The comparison of the precision, recall rate, and F1 value of the proposed grammar error correction model on the complete test set shows that the precision of the study is 19.10 and 5.41% higher for subject–verb agreement errors. The recall rate is 9.55 and 10.77% higher, respectively; F1 values are higher by 12.65 and 10.59%, respectively. The above results show that the error-correcting technique of the research design has excellent error-correcting performance. It is hoped that this experiment can provide a reference for the relevant research of automatic error correction technology of electronic text.

A Comparative Study of Detrending Methods on Crop Yield Time Series for Drought Studies

Detrending is a statistical technique that removes systematic variations or trends from time series data, allowing analysts to focus on the underlying patterns or fluctuations. While multiple detrending approaches have been applied but rarely discussed their consistency of outcomes and effectiveness in accurately capturing better yield trends. The validation of drought occurrences has proven to be a challenging task due to the non-stationary characteristics of time series data related to crop yield. This research utilizes time series of cotton yield data from the Marathwada region covering the period from 1998 to 2021. Three traditional trend models, including simple linear regression, second-order polynomial regression and central moving average were applied. Additionally, two machine learning models (random forest and support vector regression) were tested with a novel approach. Moreover, two decomposition models (additive and multiplicative) were used to remove non-linear trends in crop yield time series data. The performance of the chosen models was evaluated based on metrics such as root mean square error, mean absolute error, Nash-Sutcliffe efficiency, and index of agreement. The results suggest that the most effective detrending approach involves combining a random forest machine learning model with an additive decomposition model.

Modeling the response of sesame (Sesamum indicum L.) to different soil fertility levels under rain-fed conditions in the semi-arid areas of western Tigray, Ethiopia

Sesame, a crucial oilseed crop in Ethiopia, ranks second only to coffee in its importance as an exported agricultural commodity. However, inadequate soil fertility management has hampered its productivity despite its substantial international market demand. Hence, this study was conducted to model the response of sesame to different nitrogen fertilizer levels using the AquaCrop model, and to assess the capability of the model as a decision-support tool for optimizing soil fertility management strategies in the study area. The experiment was laid out in a randomized complete block design, consisting of four nitrogen fertilizer rates (0, 23, 46, and 69 kg/ha nitrogen) and three distinct sesame varieties (Setit-1, Setit-2, and Humera-1). Over the course of the three cropping seasons, data on soil physical and chemical properties, crop growth, yield and yield components were collected for each treatment. Evaluation of model performance relied upon established metrics of coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (N-RMSE), model efficiency (E), and degree of agreement (D). Analysis of results revealed the AquaCrop model appropriately calibrated for simulation of soil water content, showing R2 values ranging from 0.92 to 0.98, RMSE values varying from 6.5 to 13.9 mm, E values from 0.78 to 0.94, and D values from 0.95 to 0.99. Similarly, simulation outputs for aboveground biomass (AB) demonstrated good accuracy of the model, with R2 values varying from 0.92 to 0.98, RMSE values ranging from 0.33 to 0.54 tons/ha, and D values from 0.9 to 0.98. Notable accuracy was also observed in the simulation of canopy cover (CC), revealing R2 values between 0.95 and 0.99, and RMSE values ranging from 5.3 to 8.6 %. In conclusion, this study substantiates the successful calibration and validation of the AquaCrop model for predicting sesame response to diverse nitrogen fertilizer levels. The performance of the model in predicting soil water content, CC, AB, and yield highlights its potential as a valuable tool for optimizing soil fertility management and enhancing sesame cultivation practices in Ethiopia.

The effect of cycloplegia in the accuracy of autorefraction, keratometry and axial length using the Myopia Master

BackgroundAssessing refractive errors under cycloplegia is recommended for paediatric patients; however, this may not always be feasible. In these situations, refraction has to rely on measurements made under active accommodation which may increase measurements variability and error. Therefore, evaluating the accuracy and precision of non-cycloplegic refraction and biometric measurements is clinically relevant. The Myopia Master, a novel instrument combining autorefraction and biometry, is designed for monitoring refractive error and ocular biometry in myopia management. This study assessed its repeatability and agreement for autorefraction and biometric measurements pre- and post-cycloplegia.MethodsA prospective cross-sectional study evaluated a cohort of 96 paediatric patients that underwent ophthalmologic examination. An optometrist performed two repeated measurements of autorefraction and biometry pre- and post-cycloplegia. Test-retest repeatability (TRT) was assessed as differences between consecutive measurements and agreement as differences between post- and pre-cycloplegia measurements, for spherical equivalent (SE), refractive and keratometric J0/J45 astigmatic components, mean keratometry (Km) and axial length (AL).ResultsCycloplegia significantly improved the SE repeatability (TRT, pre-cyclo: 0.65 D, post-cyclo: 0.31 D). SE measurements were more repeatable in myopes and emmetropes compared to hyperopes. Keratometry (Km) repeatability did not change with cycloplegia (TRT, pre-cyclo: 0.25 D, post-cyclo:0.27 D) and AL repeatability improved marginally (TRT, pre-cyclo: 0.14 mm, post-cyclo: 0.09 mm). Regarding pre- and post-cycloplegia agreement, SE became more positive by + 0.79 D, varying with refractive error. Myopic eyes showed a mean difference of + 0.31 D, while hyperopes differed by + 1.57 D. Mean keratometry, refractive and keratometric J0/J45 and AL showed no clinically significant differences.ConclusionsRefractive error measurements, using the Myopia Master were 2.5x less precise pre-cycloplegia than post-cycloplegia. Accuracy of pre-cycloplegic refractive error measurements was often larger than the clinically significant threshold (0.25 D) and was refractive error dependent. The higher precision compared to autorefraction measurements, pre- and post-cycloplegia agreement and refractive error independence of AL measurements emphasize the superiority of AL in refractive error monitoring.

Children’s early negative auxiliaries are true auxiliaries

ABSTRACT This study investigates young children’s acquisition of functional categories through their use of negative words and negative auxiliaries in particular. Drawing from CHILDES, we analyze twelve months of spontaneous speech by 14 children (youngest age 1;9, oldest age 3;1) and their mothers, in order to assess whether children’s earliest negative productions are morphological combinations and reflect possession of abstract syntactic categories or are instead input-driven formulae. In five analyses we show that (i) two-year-olds use a wide and overlapping range of negative and positive auxiliaries; (ii) the range of the negative auxiliaries children produce is strongly correlated with the range of the positive auxiliaries they produce; (iii) children’s most common negative auxiliary, don’t, is used grammatically with respect to the syntactic category being negated and with respect to overt markings of tense; (iv) children’s subject agreement errors with don’t are mirrored by subject agreement errors with do, have, and haven’t; and (v) children omit auxiliaries with not at rates that cannot be attributed to properties of their input. Our findings support the hypothesis that children’s earliest negations are syntactically adult-like and reflect the possession of abstract syntactic categories. By age 2, English-learning children productively combine auxiliary, negation, and tense categories and syntactically differentiate different negative morphemes.

Calibration and Evaluation of CERES-Maize and CROPGRO-Dry Bean Crop Simulation Models of the DSSAT in the Great Rift Valley Region of Ethiopia

Maize (Zea mays L.) is one of the most principal cereal crops ranking first in production in Ethiopia, predominantly produced and consumed directly by the smallholder farmers in the Great Rift Valley (GRV) of Ethiopia. Common bean (Phaseolus vulgaris) is also the most important legume crops as the source of protein and export commodity in the GRV. However, the average maize and common bean yields in Ethiopia are still low due to abiotic, biotic and socioeconomic constraints. In this regard, Crop simulation models (CSMs) are used in predicting growth and yield of crops and associated yield gaps under various management options and changing climatic parameters that are profitable with minimal unwanted impacts on the environment. Before using the CSMs, it is necessary to specify model parameters and understand the uncertainties associated with simulating variables that are needed for decision-making. Therefore, the research objective of this study was to calibrate and evaluate the performance of the CERES-Maize and CROPGRO-Dry bean CSMs of the Decision Support System for Agrotechnology Transfer (DSSAT) in the GRV of Ethiopia. The generalized likelihood uncertainty estimation (GLUE) method was used to estimate the genetic parameters of the CSM-CERES-Maize and CROPGRO-Dry bean models. Root mean squared error (RMSE) and Index of agreement (I) were used to evaluate the performance of the models. The DSSAT model reasonably reproduced observations for days to anthesis, days to physiological maturity, and grain yields, with values for the index of agreement of 0.97, 0.88 and 0.61 for CERES-Maize and 0.84, 0.75 and 0.51 for CROPGRO-Dry bean. Similarly, root mean square errors were moderate for days to anthesis (1.2 and 1.2 days), maturity (4.1 and 1.6 days), and yield (0.8 and 1.1 t/ha) for CERES-Maize and CROPGRO-Dry bean, respectively. The model has been successfully calibrated and evaluated for maize and common bean crop varieties and can now it can be taken for further applications in evaluating various crop and soil management options including climate smart agriculture technologies and climate change impact studies.

Detection of volatile organic compounds as new paradigm to accelerate antimicrobial susceptibility testing: performance evaluation of VITEK® REVEAL™.

The measurement of VOCs release in the headspace of a bacterial culture represents a new approach to rapidly assess antimicrobial susceptibility. Herein, we evaluated the diagnostic performance of the VITEK® REVEAL™ system directly from a collection of Gram-negative positive blood cultures. One hundred and twenty-eight positive blood cultures were included in the analysis (Enterobacterales, n = 95; Pseudomonas aeruginosa, n = 21; Acinetobacter baumannii complex, n = 12). Samples were processed using VITEK® REVEAL™ according to the manufacturer's recommendations, and MICs of 22 antimicrobials were compared with those obtained using reference methods. Categorical agreement (CA), essential agreement (EA) and categorical errors were calculated. Overall, 2220 strain/antibiotic pair combinations were analysed. Of these, most were classified as resistant by reference antimicrobial susceptibility testing (1091/2220; 48.7%). The overall CA and EA were 97.6% and 97.7%, respectively. CA ranged from 97.5% in Enterobacterales to 97.9% in both P. aeruginosa and A. baumannii complex. The overall number of categorical discrepancies were: 18 very major errors (1.6%), 13 major errors (1.2%) and 22 minor errors (2.4%). EA ranged from 95.2% in P. aeruginosa to 98.1% in Enterobacterales. Screening test for ESBL phenotype was positive, indeterminate and negative in 13.7%, 32.6% and 27.4% of Enterobacterales isolates tested by both VITEK® REVEAL™ and the reference method, showing 100% CA. VITEK® REVEAL™ represents a reliable tool to obtain antimicrobial susceptibility results of the main Gram-negative species directly from positive blood cultures with time to results of less than 8 h.

Photosynthetically Active Radiation Temperature Dependent Models and Its Variability with Climatic Variables over Akure, Nigeria

Photosynthetically Active Radiation (PAR) is an essential input for applications pertaining to biomass production, plant physiology, and natural lighting in greenhouses. It is the amount of light energy necessary for photosynthesis to occur, and the wavelengths of this light are typically between 400 and 700 nm. In this study, the photosynthetically active radiation for Akure (latitude 7.28°N, longitude 5.30°E, and 375 m asl.), located in the coastal region of Nigeria, was estimated and investigated using data on the monthly averaged daily global solar radiation, relative humidity, wind speed, minimum and maximum temperature from the National Aeronautics and Space Administration (NASA) archives over a thirty-eight-year period (1984–2021). In order to determine the accuracy of the models, eight (8) new temperature PAR-based models were developed for the location and statistical tests were conducted using the coefficient of determination (R2), mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), t-test, and index of agreement (IA). The findings demonstrated that PAR for Akure is lowest in the month of August with 6.5320 MJm-2day-1 during the rainy season (April to October) and highest during the dry season (November to March), with 8.6670 MJm-2day-1 in March. The best model equation for calculating PAR for Akure was found to be the one that links the PAR and natural logarithm of temperature changes; its R2, MBE, RMSE, MPE, t-test, and IA values were 68.1%, 0.0032 MJm-2day-1, 0.5208 MJm-2day-1, -0.4479%, 0.0204, and 82.8013%, respectively.

A Segmentation-based Automated Calculation of Patient Size and Size-specific Dose Estimates in Pediatric Computed Tomography Scans

Background and Purpose: Size-specific dose estimates (SSDE) have been introduced into computed tomography (CT) dosimetry to tailor patients’ unique sizes to facilitate accurate CT radiation dose quantification and optimization. The purpose of this study was to develop and validate an automated algorithm for the determination of patient size (effective diameter) and SSDE. Materials and Methods: A MATLAB platform was used to develop software of algorithms based on image segmentation techniques to automate the calculation of patient size and SSDE. The algorithm was used to automatically estimate the individual size and SSDE of four CT dose index phantoms and 80 CT images of pediatric patients comprising head, thorax, and abdomen scans. For validation, the American Association of Physicists in Medicine (AAPM) manual methods were used to determine the patient’s size and SSDE for the same subjects. The accuracy of the proposed algorithm in size and SSDE calculation was evaluated for agreement with the AAPM’s estimations (manual) using Bland–Altman’s agreement and Pearson’s correlation coefficient. The normalized error, system bias, and limits of agreement (LOA) between methods were derived. Results: The results demonstrated good agreement and accuracy between the automated and AAPM’s patient size estimations with an error rate of 1.9% and 0.27% on the patient and phantoms study, respectively. A 1% percentage difference was found between the automated and manual (AAPM) SSDE estimates. A strong degree of correlation was seen with a narrow LOA between methods for clinical study (r &gt; 0.9771) and phantom study (r &gt; 0.9999). Conclusion: The proposed automated algorithm provides an accurate estimation of patient size and SSDE with negligible error after validation.

Lesion-symptom Mapping of Acceptability Judgments in Chronic Poststroke Aphasia Reveals the Neurobiological Underpinnings of Receptive Syntax.

Disagreements persist regarding the neural basis of syntactic processing, which has been linked both to inferior frontal and posterior temporal regions of the brain. One focal point of the debate concerns the role of inferior frontal areas in receptive syntactic ability, which is mostly assessed using sentence comprehension involving complex syntactic structures, a task that is potentially confounded with working memory. Syntactic acceptability judgments may provide a better measure of receptive syntax by reducing the need to use high working memory load and complex sentences and by enabling assessment of various types of syntactic violations. We therefore tested the perception of grammatical violations by people with poststroke aphasia (n = 25), along with matched controls (n = 16), using English sentences involving errors in word order, agreement, or subcategorization. Lesion data were also collected. Control participants performed near ceiling in accuracy with higher discriminability of agreement and subcategorization violations than word order; aphasia participants were less able to discriminate violations, but, on average, paralleled control participants discriminability of types of violations. Lesion-symptom mapping showed a correlation between discriminability and posterior temporal regions, but not inferior frontal regions. We argue that these results diverge from models holding that frontal areas are amodal core regions in syntactic structure building and favor models that posit a core hierarchical system in posterior temporal regions.

Neural Networks as Cognitive Models of the Processing of Syntactic Constraints.

Languages are governed by syntactic constraints-structural rules that determine which sentences are grammatical in the language. In English, one such constraint is subject-verb agreement, which dictates that the number of a verb must match the number of its corresponding subject: "the dogs run", but "the dog runs". While this constraint appears to be simple, in practice speakers make agreement errors, particularly when a noun phrase near the verb differs in number from the subject (for example, a speaker might produce the ungrammatical sentence "the key to the cabinets are rusty"). This phenomenon, referred to as agreement attraction, is sensitive to a wide range of properties of the sentence; no single existing model is able to generate predictions for the wide variety of materials studied in the human experimental literature. We explore the viability of neural network language models-broad-coverage systems trained to predict the next word in a corpus-as a framework for addressing this limitation. We analyze the agreement errors made by Long Short-Term Memory (LSTM) networks and compare them to those of humans. The models successfully simulate certain results, such as the so-called number asymmetry and the difference between attraction strength in grammatical and ungrammatical sentences, but failed to simulate others, such as the effect of syntactic distance or notional (conceptual) number. We further evaluate networks trained with explicit syntactic supervision, and find that this form of supervision does not always lead to more human-like syntactic behavior. Finally, we show that the corpus used to train a network significantly affects the pattern of agreement errors produced by the network, and discuss the strengths and limitations of neural networks as a tool for understanding human syntactic processing.

High-resolution inherent strain method using actual layer thickness in laser powder bed fusion additive manufacturing with experimental validations

Laser powder bed fusion (LPBF) additive manufacturing (AM) broadens the horizons of design in academia and industry. However, LPBF contends with challenges like residual stresses and distortions due to uneven heating and cooling, leading to substantial resource wastage. Accurately predicting residual stresses and distortions remains a hurdle, primarily due to the need for high-resolution modeling. In this study, a high-resolution model of the inherent strain method (ISM) with actual layer thickness for a cantilever geometry in LPBF is proposed for the first time. Experimental and numerical findings indicate that as the number of layers increases, distortions tend to decrease, while the residual stresses on the top surface consistently remain constant and close to the material's yield stress. The model achieves good agreement (error of 7.1%) for deformation, while the prediction error of residual stresses is reduced from 69% to 32% compared to a traditional ISM model.

Assessment of Two SWCC models for predicting the curve fitting parameters of lateritic soil: bentonite mixtures

This study compares the capabilities of soil water characteristic curve (SWCC) models by Brooks-Corey (BC) and van Genuchten (vG) in estimating the curve fitting parameters for lateritic soil—bentonite mixtures. The SWCCs of soil treated with 0–10% bentonite and compacted with British standard light (BSL) energy at compaction states representing dry of optimum, optimum, and wet of optimum conditions were measured by sequential desorption using pressure plate extractor. The fitting parameters of the two equations were determined by a non-linear fitting program. The fitting capabilities of the models on the measured data were compared by statistical indices namely the root mean square error (RMSE), linearity (R2) and index of agreement (d). Results revealed that volumetric water content increased as bentonite content increased with specimens containing 10% bentonite recording the highest and therefore has greater capacity to retain water/contaminants, while the air entry value (AEV) for the various soil mixtures also increased with higher bentonite content. The study also found that the estimated volumetric water contents approximated the measured values at all suctions with a high degree of accuracy with RMSE values that ranged from 0.0035 to 0.0150 for vG model which are somewhat lower than the values for BC equation. Similarly, R2 for the vG equation (≥ 0.99) are, on average, slightly higher than those of the BC fits. However, the d values associated with the BC model which varied between 0.788 and 0.971 are higher than those of the vG (0.784–0.968). Overall, the study established that the vG model proved marginally superior in respect of goodness of fit.

Error Analysis of English Narrative Essays of Secondary School Learners in Kirinyaga Central Sub-County, Kenya

The study examined the types of language errors made by learners in the English narrative essays. It also assessed the feedback techniques employed by teachers of English in the handling of language errors in the English narrative essays. A descriptive research design was employed to examine errors of English narrative essays of 181 form two learners obtained through stratified random sampling and simple random sampling techniques. The eight teachers of English selected through purposive and simple random sampling completed interview schedules. Qualitative data generated from the essays of the learners was analyzed using Error Analysis and Content Analysis. The findings reveal that a total of seven categories of language errors which include: word order, verb tense and form, subject – verb agreement, spelling, plurality, word choice, and capital letter errors were made by the learners in their English narrative essays. The most preferred corrective technique was to involve the learners actively by encouraging them to read extensively and write severally on various narratives in the target language. The findings will provide reliable feedback to teachers of English because it will guide on the development of teaching methods and evaluative measures to narrative essays. The study will also be useful to curriculum developers in designing materials for teaching English language in secondary schools. It will also be of invaluable benefit to learners of English as it will guide on the way narrative essays need to be written.