Similar Performance Research Articles

Mind-wandering increases in frequency over time during task performance: An individual-participant meta-analytic review.

Attention has a seemingly inevitable tendency to turn inward toward our thoughts. Mind-wandering refers to moments when this inward focus diverts attention away from the current task-at-hand. Mind-wandering is thought to be ubiquitous, having been estimated to occur between 30% and 50% of our waking moments. Yet, it is unclear whether this frequency is similar within-task performance contexts and unknown whether mind-wandering systematically increases with time-on-task for a broad range of tasks. We conducted a systematic literature search and individual participant data meta-analysis of rates of occurrence of mind-wandering during task performance. Our search located 68 research reports providing almost a half-million total responses to experience sampling mind-wandering probes from more than 10,000 unique individuals. Latent growth curve models estimated the initial occurrence of mind-wandering and linear change in mind-wandering over sequential probes for each study sample, and effects were summarized using multivariate meta-analysis. Our results confirm that mind-wandering increases in frequency over time during task performance, implicating mind-wandering in characteristic within-task psychological changes, such as increasing boredom and patterns of worsening behavioral performance with time-on-task. The systematic search and meta-analysis provide the most comprehensive assessment of normative rates of mind-wandering during task performance reported to date. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparison of three frailty measures for predicting hospitalization and mortality in the Canadian Longitudinal Study on Aging

BackgroundFew studies have compared different measures of frailty for predicting adverse outcomes. It remains unknown which frailty measurement approach best predicts healthcare utilization such as hospitalization and mortality.AimsThis study aims to compare three approaches to measuring frailty—grip strength, frailty phenotype, and frailty index—in predicting hospitalization and mortality among middle-aged and older Canadians.MethodsWe analyzed baseline and the first 3-year follow-up data for 30,097 participants aged 45 to 85 years from the comprehensive cohort of the Canadian Longitudinal Study on Aging (CLSA). Using separate logistic regression models adjusted for multimorbidity, age and biological sex, we predicted participants' risks for overnight hospitalization in the past 12 months and mortality, at the first 3-year follow-up, using each of the three frailty measurements at baseline. Model discrimination was assessed using Harrell’s c-statistic and calibration assessed using calibration plots.ResultsThe predictive performance of all three measures of frailty were roughly similar when predicting overnight hospitalization and mortality risk among CLSA participants. Model discrimination measured using c-statistics ranged from 0.67 to 0.69 for hospitalization and 0.79 to 0.80 for mortality. All measures of frailty yielded strong model calibration.Discussion and conclusionAll three measures of frailty had similar predictive performance. Discrimination was modest for predicting hospitalization and superior in predicting mortality. This likely reflects the objective nature of mortality as an outcome and the challenges in reducing the complex concept of healthcare utilization to a single variable such as any overnight hospitalization.

Probing atomistic deformation behavior of graphene-coated Al0.3CoCrFeNi high-entropy alloy under nanoindentation

This study employs a series of molecular dynamics (MD) simulations to investigate the atomistic deformation mechanism of graphene (Gr)-coated Al[Formula: see text]CoCrFeNi high-entropy alloy (HEA) during nanoindentation. In the elastic region, both substrates exhibit similar indentation performance. However, at greater indentation depths, the Gr-coated HEA offered higher resistance against indentation, leading to a remarkable 78% increase in hardness compared to pristine HEA. The graphene-coated HEA also shows extended unloading time (52.3[Formula: see text]ps) compared to the pristine HEA (44.7[Formula: see text]ps), indicating improved structural recovery. Importantly, graphene coating raises the critical indentation depth for the emergence of the initial embryonic plastic deformation loop (PDL) from 0.628[Formula: see text]nm in pristine HEA to 0.86[Formula: see text]nm in the Gr-coated HEA, signifying its hindrance to the elastoplastic transition. Gr coating suppresses dislocation nucleation at first, but eventually stimulates dislocation formation at greater depths, resulting in a higher dislocation density in Gr-coated HEA, which contributes to increased material strength. Additionally, the post-indentation surface morphology of the Gr-coated HEA substrate displays reduced pile-up formation due to the restraining action of the Gr coating. The findings of this study provide a comprehensive understanding of the deformation mechanism in the Gr-coated Al[Formula: see text]CoCrFeNi HEA and its potential implications for surface engineering.

İki Dinamik Koşullu Korelasyon (DCC) Modeli Arasındaki Tahmin Doğruluğunun Karşılaştırılması

This study compares two commonly used DCC-family models to predict the linkage between the US equity and REIT markets within a global minimum-variance portfolio. Equity and REIT portfolios are constructed using variance-covariance matrices, which represent forward-looking covariance information. These matrices are constructed out-of-sample with ex-ante forecasting. By assessing the predictive precision of each model, the study aims to determine which one produces the lowest forecasting errors and performs better economically. According to a statistical comparison, ex-ante correlation forecasts based on the Asymmetric DCC model were more accurate than those based on the standard DCC model. An empirical comparison of the economic performance of these two models in a dynamic portfolio allocation framework reveals that, despite its complexity, the Asymmetric DCC model exhibits similar economic performance characteristics to the standard DCC model. Despite the lack of emphasis on the economic overperformance of the Asymmetric DCC model, investors who recalibrate their portfolios weekly will benefit from reduced forecast errors and the ability to create more efficient portfolios by using an asymmetric model instead of a standard model.

Evaluating the Performance of SPDK-based io uring and AIO Block Device

Abstract: In the context of contemporary data processing and storage, this article examines the performance of asynchronous I/O interfaces that are provided by the Linux Kernel. Specifically, this study utilizes an SPDK-based block device module of io uring and AIO which offers features like request queueing and lockless queues. Our benchmarks comparing io uring and aio performance revealed an interesting dichotomy in their handling of random I/O patterns. Both approaches exhibited similar performance trends for random-read operations, showcasing a clear benefit from increasing the IO queue depth. This suggests both methods efficiently leverage parallel processing to boost read throughput. However, the story flips for random-write operations. Unlike read workloads, increasing queue depth for writes yielded no performance improvement. This increase significantly inflated latency, introducing undesired delays.

Elevational patterns and assembly processes of multifaceted bird diversity in a subtropical mountain system

AbstractAimMountain systems harbour disproportionate biodiversity on Earth. However, the mechanism underlying community assembly along elevational gradients remains unclear due to the complexity of environmental constraints and biotic interactions. Birds play a crucial role in mountain system and are sensitive to environmental changes, making them an ideal taxon for exploring diversity patterns and the underlying assembly processes. We aimed to identify the elevational patterns of multifaceted bird diversity and explore how different ecological processes drive community assembly.LocationA 2600 m elevational gradient on the eastern margin of the Qinghai–Tibetan Plateau.MethodsWe identified the elevational patterns and assembly processes of alpha‐ and beta‐diversity in the taxonomic, phylogenetic, and functional dimensions. We compared the multidimensional beta‐diversity components and assessed the response of beta‐diversity to variations in elevational and environmental distances. Finally, we examined the roles of spatial and environmental factors in structuring the diversity patterns.ResultsWe observed bimodal elevational patterns across species richness, phylogenetic diversity, and functional diversity but distinct community structures in terms of mean pairwise phylogenetic, and functional diversity. We detected a linear decline in phylogenetic and functional diversity with increasing elevation after controlling for the confounding effect of species richness. We documented relatively high taxonomic and phylogenetic total beta‐diversity, but low functional total beta‐diversity. Turnover dissimilarity contributed much more to total beta‐diversity than nestedness‐resultant dissimilarity. The elevational patterns of standardised measures indicated that competition exclusion and environmental filtering jointly shaped the community assembly process. In general, mean annual temperature, annual precipitation, and area significantly influenced the majority of diversity measures.ConclusionsOur results revealed incongruent community structures across phylogenetic and functional diversity. Additionally, our results indicated that species with similar ecological performances or distinct lineages may exhibit convergent adaptations to cold and dry environments. These findings provide insights into the mechanistic understanding of the community assembly process and assist in conservation planning in mountain systems by integrating multifaceted diversity across different dimensions.

Using of the Weibull distribution in developing global solar radiation forecasting models

AbstractIn this study, a solar radiation prediction model was developed using the Weibull distribution function (WDF). The main purpose of this study is to develop a new model for solar radiation (SR) estimation using the WDF and to bring this model to the literature. Although the WDF is widely used in wind energy forecasting due to its compatibility with wind speed data, it has not been used before in solar radiation forecasting model development. In this study, it is aimed to make SR estimation with the WDF for the first time. SR data for all regions where this new model was developed and its performance was examined were obtained from the General Directorate of Meteorology. In order to decide the success of these developed models, five different statistical metrics (RPE, MPE, MAPE, SSRE, and t‐stat) were discussed in the study. When the results are evaluated in general, it is seen that the new developed model has an acceptable performance. According to all test results in the region where the model was developed, it is seen that the new developed model showed the best performance with 0.0353, 0.4068, 7.1851, 0.1144, and 0.0162 values, respectively. The performance of this new developed model was observed in three different regions. When the statistical test results in these regions were examined, it was seen that the new model developed had a similar performance to the popular solar forecasting models widely used in the literature.

Enzymatic Pretreatment of Slaughterhouse Wastewater: Application of Whole Lipolytic Cells of Rhizopus oryzae Produced from Residual Vegetable Oil

This study assessed the application of whole lipolytic cells in the pretreatment of slaughterhouse wastewater to reduce its lipid content. The fungal biomass of Rhizopus oryzae was evaluated in the hydrolysis of slaughterhouse wastewater containing high lipid concentrations, focusing on the biomass’s concentration and the effect of using an emulsifier and surfactant. The use of the whole-cells lipase of Rhizopus oryzae grown in a residual vegetable oil medium proved effective in the hydrolysis of slaughterhouse wastewater, generating concentrations of free fatty acids (FFA) ranging from 40.36 to 90.14 mM. The action of lipase in the hydrolysis of slaughterhouse residues indicated its effectiveness in pretreating lipid-rich liquid residues, potentially boosting the microbiota of this anaerobic treatment. The results showed that lipase activity without surfactant exhibited a similar performance to that of Triton X-100 in the hydrolysis of liquid residues. However, the combination of lipase and surfactant could represent a promising strategy to optimize free fatty acid production from slaughterhouse residues, strengthening anaerobic treatment processes and potentially enhancing the overall efficiency of waste management systems.

Zirconium and hafnium catalyzed C–C single bond hydroboration

Selective cleavage and subsequent functionalization of C−C single bonds present a fundamental challenge in synthetic organic chemistry. Traditionally, the activation of C−C single bonds has been achieved using stoichiometric transition-metal complexes. Recently, examples of catalytic processes were developed in which use is made of precious metals. However, the use of inexpensive and Earth-abundant group IV metals for catalytic C−C single-bond cleavage is largely underdeveloped. Herein, the zirconium-catalyzed C−C single-bond cleavage and subsequent hydroboration reactions is realized using Cp2ZrCl2 as a catalytic system. A series of structures of various γ-boronated amines are readily obtained, which are otherwise difficult to obtain. Mechanistic studies disclose the formation of a N–ZrIV species, and then a β-carbon elimination route is responsible for C–C single bond activation. Besides zirconium, hafnium exhibits a similar performance for this transformation.

The serodiagnositic value of Chlamydia trachomatis antigens in antibody detection using luciferase immunosorbent assay.

Among the different antigens used in the detection of anti-Chlamydia trachomatis antibodies, significant differences in sensitivity and specificity have been observed. Further evaluation of C. trachomatis antigens in antibody detection is urgently needed for the development and application of C. trachomatis serologic assays. Chlamydia trachomatis antigens Pgp3, TmeA, InaC, and HSP60 were selected and used in luciferase immunosorbent assay (LISA). The detection results obtained from well-defined C. trachomatis positive and negative samples were compared with the commercial C. trachomatis ELISA (Mikrogen) for performance evaluation. Pgp3, TmeA, InaC, and HSP60-based LISA showed sensitivity of 92.8, 88.8, 90.4, and 94.4%, and specificity of 99.2, 99.2, 99.2, and 92%, respectively. ROC analysis indicated that Pgp3-based LISA showed similar performance to Mikrogen ELISA (AUC 0.986 vs. 0.993, p = 0.207). Furthermore, four C. trachomatis antigens achieved strong diagnostic efficiency, i.e., positive likelihood ratios [+LR] ≥ 10 in C. trachomatis-infected women and negative likelihood ratios [-LR] ≤ 0.1 in C. trachomatis negative low exposure risk children, but only Pgp3 and TmeA showed strong diagnostic value in general adults. In addition, Pgp3, TmeA, and InaC, but not HSP60, achieved high performance, i.e., both positive predictive value (PPV) and negative predictive value (NPV) ≥ 90.9%, and showed no significant cross-reactivity with anti-Chlamydiapneumoniae. Three C. trachomatis species-specific antigens Pgp3, TmeA, and InaC show superior performance in the detection of anti-C. trachomatis antibody, indicating the potential to be used in developing C. trachomatis serologic tests.

Toward interpretable LSTM-based modeling of hydrological systems

Abstract. Several studies have demonstrated the ability of long short-term memory (LSTM) machine-learning-based modeling to outperform traditional spatially lumped process-based modeling approaches for streamflow prediction. However, due mainly to the structural complexity of the LSTM network (which includes gating operations and sequential processing of the data), difficulties can arise when interpreting the internal processes and weights in the model. Here, we propose and test a modification of LSTM architecture that is calibrated in a manner that is analogous to a hydrological system. Our architecture, called “HydroLSTM”, simulates the sequential updating of the Markovian storage while the gating operation has access to historical information. Specifically, we modify how data are fed to the new representation to facilitate simultaneous access to past lagged inputs and consolidated information, which explicitly acknowledges the importance of trends and patterns in the data. We compare the performance of the HydroLSTM and LSTM architectures using data from 10 hydro-climatically varied catchments. We further examine how the new architecture exploits the information in lagged inputs, for 588 catchments across the USA. The HydroLSTM-based models require fewer cell states to obtain similar performance to their LSTM-based counterparts. Further, the weight patterns associated with lagged input variables are interpretable and consistent with regional hydroclimatic characteristics (snowmelt-dominated, recent rainfall-dominated, and historical rainfall-dominated). These findings illustrate how the hydrological interpretability of LSTM-based models can be enhanced by appropriate architectural modifications that are physically and conceptually consistent with our understanding of the system.

Optimisation of electrochemical performance of Sr(Fe,Sb)O3-δ air electrodes for intermediate-temperature solid oxide cells through spray-pyrolysis processing

The effects of replacing iron with cobalt and employing either Pechini or spray pyrolysis synthesis routes have been studied for the perovskite system SrFe0.9-xCoxSb0.1O3-δ (x = 0; 0.45; 0.9) to optimise sustainable air electrodes for intermediate-temperature solid oxide cells. The electrochemical performance of Pechini-prepared compositions improves with increasing cobalt content, attributable to higher electronic conductivity and oxygen-vacancy concentration, and lower processing temperature from 1300 ᵒC, for SrFe0.9Sb0.1O3-δ, to 1200 and 1100 ᵒC, for SrFe0.45Co0.45Sb0.1O3−δ and SrCo0.9Sb0.1O3−δ, respectively, with SrCo0.9Sb0.1O3-δ exhibiting an area specific resistance (ASR) of 0.084 Ω cm2 at 700 °C. The electrochemical stability of Fe-containing Pechini-based compositions is impaired, most likely due to a higher processing temperature. However, the very low processing temperature of 800 °C for spray-pyrolysed electrodes produces similar electrochemical performances for Co-containing and Co-free compositions (ASR≈ 0.065 Ω cm2 at 700 °C for SrFe0.9Sb0.1O3-δ) and high electrochemical stability with practically unaltered electrode polarisation resistance on thermal cycling.

Comparison of machine learning-based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease.

To compare the diagnostic performance of machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis. Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2weeks. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenosis was defined as FFR ≤ 0.8 or > 90% stenosis on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference. The study protocol was completed in 110 participants (mean age, 62years ± 8; 73 men), and hemodynamically significant stenosis was detected in 36 (33%). Among the quantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant stenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0.89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperformed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with improved specificity (95% vs 82%, p = 0.01) and accuracy (95% vs 81%, p < 0.01). ML-based CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantitative perfusion mapping had a favorable performance in per-patient analysis. ML-based CT-FFR and MPR derived from cardiac MR performed well in diagnosing vessel-specific hemodynamically significant stenosis, both of which showed no statistical discrepancy with each other. • Both machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis. • Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis. • ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis.

Accelerated Development of AAV Purification Process Using a High-Throughput and Automated Crossflow System

Adeno-associated viruses (AAV) are currently predominant viral transfer tools for gene therapy, and efforts are being made to design faster and more efficient methods and technologies for their manufacturing. The early selection of high-performing filters is essential for developing an ultrafiltration and diafiltration (UF/DF) process, especially when feed material is scarce, and timelines are short. However, few methods and technologies exist to enable process optimization with multiple variations in a single run. In this study, we explored the potential of Ambr® Crossflow for high-throughput, automated screening of different membrane materials, pore sizes and different process conditions for the UF/DF step of AAV8. The best overall performance was obtained with a 100 kDa PES flat sheet cassette. The UF/DF process was further transferred to a larger scale to the Sartoflow® Smart Tangential Flow Filtration (TFF) system using a 100 kDa PES Sartocon® Slice 200 cassette and compared to a 100 kDa PES hollow fiber. Virus recovery, permeate flux and total protein removal values of the flat sheet cassette were similar to those achieved in small-scale devices, and higher than those of the hollow fiber, thus demonstrating similar performance at a larger process scale. The high-throughput, automated method described herein allowed to screen multiple materials and process parameters of a UF/DF process in a time- and resource-efficient way, making it a useful tool to accelerate early-stage downstream process development of AAV.

Role of an Aqueous Extract of Duckweed (Lemna minor L.) in Increasing Salt Tolerance in Olea europaea L.

Salt stress is one of the preeminent abiotic stressors capable of strongly impacting crop productivity and quality. Within the array of strategies garnering interest in safeguarding crops against abiotic stresses, the use of plant biostimulants is emerging as a noteworthy avenue. For the above, there is an increasing interest in finding new plant extracts showing biostimulating effects in crops. In the present study, the efficacy of an aqueous extract from an aquatic species, the duckweed (Lemna minor L.), was assessed in olive plants (cv. Arbequina) grown in hydroponics and exposed to severe saline stress (150 mM NaCl). Salt stress caused considerable diminutions in biomass production, leaf net photosynthesis (Pn), leaf transpiration rate (E), and stomatal conductance (gs). The application of the duckweed extract resulted in a notable plant functionality recovery and counteracted the detrimental effects of the NaCl stress. Indeed, the plants stressed with NaCl and treated with the extract showed enhanced physiological and biometric traits compared to samples treated with NaCl alone. In particular, the duckweed extract improved photosynthetic activity and stomatal conductance, reduced the intercellular CO2 concentration, and ameliorated other physiological and morphological parameters. All these benefits influenced the whole plant growth, allowing samples treated with the extract to maintain a similar performance to that exhibited by the Control plants.

Comparative efficiency of conventional and green functionalized carbon-based materials for the uptake of priority produced water contaminants

Green chemistry has brought to the spotlight new sets of principles for reducing or eliminating hazardous substances in the synthesis and application of processes and products. Despite its noteworthy growth, this area still lacks studies that compare the potential of greener materials with those conventionally synthesized. Herein, novel materials based on chitosan and reduced graphene oxide were developed via the green (G-Fe/CS/rGO) and conventional routes (C–Fe/CS/rGO). Eucalyptus leaves extract and proanthocyanidin were used in the green approach as reducing/capping and crosslinking agents, respectively, replacing hydrazine and glutaraldehyde, commonly used in conventional synthesis. In addition, carbon nanotubes were also modified with plant extracts. The effect of different rGO contents was evaluated through adsorptive assays. Furthermore, the adsorption potential of C–Fe/CS/rGO, G-Fe/CS/rGO and green CNTs. Priority “Produced Water” contaminants of such as anthracene (ANT), fluoranthene (FLT), phenol (PE), and cyclohexane carboxylic acid (CHA) were used as target contaminants. The best materials were characterized by XRD, FTIR, SEM-EDS and Zeta potential analyses. C–Fe/CS/rGO-3% removed 86.6%, 89.3%, 13.4%, and 33.6%, G-Fe/CS/rGO-7% removed 83.2%, 83.9%, 6.7% and 30.3 % and CNT-E 82.9%, 97.6%, 10.7% and 12.4% of ANT, FLT, PE, and CHA, respectively. Green and conventional beads showed similar performance, indicating the promising use of green synthesis. CNT-E, which demonstrated the best adsorption capacity, was used in reuse assays to assess its feasibility. The material kept FLT removal above 85% after four consecutive adsorption-desorption cycles. Molecular modelling analysis indicated that CHA has greater stability, followed by PE, FLT and ANT, agreeing with the experimental results.

Predicting the Posture of High-Rise Building Machines Based on Multivariate Time Series Neural Network Models.

High-rise building machines (HBMs) play a critical role in the successful construction of super-high skyscrapers, providing essential support and ensuring safety. The HBM's climbing system relies on a jacking mechanism consisting of several independent jacking cylinders. A reliable control system is imperative to maintain the smooth posture of the construction steel platform (SP) under the action of the jacking mechanism. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolutional Network (TCN) are three multivariate time series (MTS) neural network models that are used in this study to predict the posture of HBMs. The models take pressure and stroke measurements from the jacking cylinders as inputs, and their outputs determine the levelness of the SP and the posture of the HBM at various climbing stages. The development and training of these neural networks are based on historical on-site data, with the predictions subjected to thorough comparative analysis. The proposed LSTM and GRU prediction models have similar performances in the prediction process of HBM posture, with medians R2 of 0.903 and 0.871, respectively. However, the median MAE of the GRU prediction model is more petite at 0.4, which exhibits stronger robustness. Additionally, sensitivity analysis showed that the change in the levelness of the position of the SP portion of the HBM exhibited high sensitivity to the stroke and pressure of the jacking cylinder, which clarified the position of the cylinder for adjusting the posture of the HBM. The results show that the MTS neural network-based prediction model can change the HBM posture and improve work stability by adjusting the jacking cylinder pressure value of the HBM.

Polyethylene glycol compared to lactulose for constipation in pregnancy: A randomized controlled trial.

To compare polyethylene glycol 4000 versus lactulose in chronic constipation during pregnancy. Women at 28-32 weeks' gestation attending antenatal clinic for routine care were screened using the Rome IV chronic constipation criterion. Eligible women were approached and consented. Participants were randomized to oral polyethylene glycol (10 g/day) or lactulose (10 g/day) for 4 weeks. A bowel movement diary was kept and outcomes using the Patient Assessment of Constipation Symptoms questionnaire (PAC-SYM), Patient Assessment of Constipation Quality of Life questionnaire (PAC-QoL) and Bristol Stool Form Scale (BSFS), which were evaluated at the start and end of the four-week period. Relative risks (RR) were determined for the coprimary outcomes of complete spontaneous bowel movement (CSBM) and PAC-SYM mean score improvement (decrease in score of >1 from the baseline). A total of 4323 women underwent screening, of which 780 fulfilled the Rome IV criterion, and 360 consented to participate (180 randomized to PEG and lactulose, respectively). Data from 247 women who completed the study were analyzed. CSBM was achieved in 107/124 (86.3%) versus 102/123 (82.9%) (RR 1.04, 95% CI: 0.93-1.16, P = 0.464) for PEG and lactulose trial arms, respectively. PAC-SYM mean score improvement was 62/118 (52.5%) in the PEG arm versus 44/118 (37.3%) in the lactulose arm (RR 1.40, 95% CI: 1.05-1.88). Of secondary outcomes, a significant difference was found in favor of PEG, with respect to PAC-SYM abdominal symptoms subscale, normal stool versus loose stool consistency and side effects of vomiting and diarrhea. After controlling for parity, baseline PAC-SYM, PAC-QoL scores, characteristics different at baseline, only diarrhea and loose stools remained significant. Both PEG 4000 and lactulose are effective laxatives in pregnancy with similar performance after adjusted analysis. Diarrhea and loose stools are less frequently reported with PEG.

A Large-Scale Study of the Relationship Between Degree and Type of Hearing Loss and Recognition of Speech in Quiet and Noise.

Understanding speech in noise (SIN) is the dominant complaint of individuals with hearing loss. For decades, the default test of speech perception in routine audiologic assessment has been monosyllabic word recognition in quiet (WRQ), which does not directly address patient concerns, leading some to advocate that measures of SIN should be integrated into routine practice. However, very little is known with regard to how SIN abilities are affected by different types of hearing loss. Here, we examine performance on clinical measures of WRQ and SIN in a large patient base consisting of a variety of hearing loss types, including conductive (CHL), mixed (MHL), and sensorineural (SNHL) losses. In a retrospective study, we examined data from 5593 patients (51% female) who underwent audiometric assessment at the Stanford Ear Institute. All individuals completed pure-tone audiometry, and speech perception testing of monaural WRQ, and monaural QuickSIN. Patient ages ranged from 18 to 104 years (average = 57). The average age in years for the different classifications of hearing loss was 51.1 (NH), 48.5 (CHL), 64.2 (MHL), and 68.5 (SNHL), respectively. Generalized linear mixed-effect models and quartile regression were used to determine the relationship between hearing loss type and severity for the different speech-recognition outcome measures. Patients with CHL had similar performance to patients with normal hearing on both WRQ and QuickSIN, regardless of the hearing loss severity. In patients with MHL or SNHL, WRQ scores remained largely excellent with increasing hearing loss until the loss was moderately severe or worse. In contrast, QuickSIN signal to noise ratio (SNR) losses showed an orderly systematic decrease as the degree of hearing loss became more severe. This effect scaled with the data, with threshold-QuickSIN relationships absent for CHL, and becoming increasingly stronger for MHL and strongest in patients with SNHL. However, the variability in these data suggests that only 57% of the variance in WRQ scores, and 50% of the variance in QuickSIN SNR losses, could be accounted for by the audiometric thresholds. Patients who would not be differentiated by WRQ scores are shown to be potentially differentiable by SIN scores. In this data set, conductive hearing loss had little effect on WRQ scores or QuickSIN SNR losses. However, for patients with MHL or SNHL, speech perception abilities decreased as the severity of the hearing loss increased. In these data, QuickSIN SNR losses showed deficits in performance with degrees of hearing loss that yielded largely excellent WRQ scores. However, the considerable variability in the data suggests that even after classifying patients according to their type of hearing loss, hearing thresholds only account for a portion of the variance in speech perception abilities, particularly in noise. These results are consistent with the idea that variables such as cochlear health and aging add explanatory power over audibility alone.

An exploration of gendered differences in cognitive, motivational and emotional aspects of game‐based math learning

AbstractBackgroundDigital game‐based learning (DGBL) has the potential to provide a gender inclusive learning environment for children.ObjectiveThe present study aimed to explore gendered differences among primary school learners in grades three, four, and five within the context of game‐based fraction learning from cognitive, motivational and emotional perspectives.MethodsTwo hundred and sixty‐nine participants completed a pre‐test and post‐test on fraction conceptual knowledge and surveys in math anxiety, intrinsic motivation and self‐efficacy. In addition, facial expression detection technology was employed to evaluate emotional states.ResultsIn general, within the DGBL environment, boys and girls exhibited similar performance in both their understanding of fraction concepts and their motivational aspects. However, gender differences were identified and manifested uniquely across different grade levels. Specifically, third‐grade girls exhibited significantly lower self‐efficacy than boys, but after DGBL intervention, the gender gap in self‐efficacy was no longer significant. Additionally, third‐grade girls achieved significant improvements in both the competence and interest dimensions of their intrinsic motivation, while boys did not show significant improvements. In the fourth grade, girls exhibited a significantly higher frequency of angry expressions compared to boys during gameplay. Fifth‐grade girls' cognitive performance appeared to be less correlated with motivational factors compared to boys.ConclusionsThe results suggest that DGBL may help narrow the gender difference in math learning, with girls potentially benefiting more from DGBL than boys.