Distinct Metrics Research Articles

Addressing agricultural challenges: An identification of best feature selection technique for dragon fruit disease recognition

Dragon fruit is a prominent substance in global agriculture. Despite this, it is gaining popularity and is a viable solution in resource-poor, environmentally degraded areas because of its many health benefits. Nevertheless, many dragon fruit plantations have been impacted by the disease, reducing their yield, and the detection system is still conventional. Farmers’ lack of disease identification and management expertise diminished crop quality and products. As a result, little research was carried out to assist those specific farmers requiring adequate agricultural support. This research has proposed an autonomous agro-based system to recognize dragon diseases using in-depth analysis of feature selection techniques. After the collection of real-time images of the dragon, the images are preprocessed using various image-processing techniques. The two important features are retrieved after segmentation. The analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) are used as feature selection techniques to assess the feature rank based on the mutual score. To analyze the effectiveness of the machine learning algorithms that were used, six distinct machine learning classifiers were applied to the top-ranked feature sets, and their performance was measured using seven distinct performance evaluation metrics. AdaBoost and Random Forest classifiers for the LASSO feature ranking approach got the maximum accuracy, which is 96.29%, based on a comparison of classifiers based on the ANOVA and LASSO feature set. Despite this, we have optimized the computational resources of each classifier for the LASSO feature set.

Multi-objective distributed cooperative spectrum sensing for cognitive radio networks using greedy-based coalitional game

In this work, we propose a distributed cooperative spectrum sensing (CSS) scheme for cognitive radio networks applying a greedy-based coalitional game. We formulate the cooperative spectrum sensing problem as a multi-objective optimization problem using the weighted global criteria method. The objective of the formulated problem is to jointly consider the relative impacts of the three key performance metrics in CSS: throughput gain, penalty cost, and energy overhead. By integrating these three distinct metrics into a global optimization problem, we aimed to achieve a well-balanced trade-off, essential for deploying the CSS scheme across diverse network and application environments. We prove that the formulated CSS problem is non-deterministic polynomially hard (NP-hard). The computational complexity of the formulated CSS problem is decreased by proposing a heuristic scheme that produces a sub-optimal solution in polynomial time. We develop the heuristic scheme by putting forward a greedy-based coalitional game theoretic model. The proposed scheme exploits the spatial diversity of secondary users (SUs) in forming distributed coalitions to minimize the effects of attenuation and distortion on the transmitted signal. Further sensing energy overhead is reduced by dynamically deciding the sensing duration for the SUs based on their received signal strength. To establish the stability of the coalitional game model, we carry out proof which shows that the proposed heuristic algorithm converges to a stable coalition structure. Simulation-based results show the effectiveness of the proposed scheme in terms of average detection accuracy, utility, and energy overhead compared to conventional schemes.

Effects of regional accessibility on productivity: An analysis based on composite indicators

The relationship between transport infrastructure and accessibility has long stood as a central research area in regional and transport economics. Often invoked by governments to justify large public spending on infrastructure, the study of this relationship has led to conflicting arguments on the role that transport plays in productivity. This paper expands the existing body of knowledge by adopting a spatial analysis (with spillover effects) that considers the physical effects of investment in terms of accessibility (using distinct metrics). The authors have used the Portuguese experience at regional level over the last 30 years as a case study. The main conclusions are as follows: i) the choice of transport variables matters when explaining productivity, and more complex accessibility indicators are more correlated with; ii) it is important to account for spill-over effects; and iii) the evidence of granger causality is not widespread but depends on the regions.

A general approach for evaluating of the coverage, resolution, and representation of streamflow monitoring networks

Streamflow monitoring networks provide information for a wide range of public interests in river and streams. A general approach to evaluate monitoring for different interests is developed to support network planning and design. The approach defines three theoretically distinct information metrics (coverage, resolution, and representation) based on the spatial distribution of a variable of interest. Coverage is the fraction of information that a network can provide about a variable when some areas are not monitored. Resolution is the information available from the network relative to the maximum information possible given the number of sites in the network. Representation is the information that a network provides about a benchmark distribution of a variable. Information is defined using Shannon entropy where the spatial discretization of a variable among spatial elements of a landscape or sites in a network indicates the uncertainty in the spatial distribution of the variable. This approach supports the design of networks for monitoring of variables with heterogeneous spatial distributions (“hot spots” and patches) that might otherwise be unmonitored because they occupy insignificant portions of the landscape. Areas where monitoring will maintain or improve the metrics serve as objective priorities for public interests in network design. The approach is demonstrated for the streamflow monitoring network operated by the United States Geological Survey during water year 2020 indicating gaps in the coverage of coastal rivers and the resolution of low flows.

Visualizing bowling performance in cricket using contour plot

Batting and bowling are widely considered to be the pivotal skills in cricket. Traditionally, cricket has relied on three distinct metrics to measure bowling performance: the bowling average, the bowler's strike rate, and the economy rate. While each of these measures attempts to quantify the performance of bowlers, their respective scopes are unique. Researchers have made several attempts to integrate these measures into a composite index to rank bowlers. However, the integration process inevitably results in the loss of the individual metrics' distinct natures. This paper endeavours to visualize the three measures in a two-dimensional plane using a contour plot. To better illustrate the proximity amongst bowlers concerning the three distinct measures, a few improvements to the plot are proposed. The proposed graphical extension utilizes the career statistics of bowlers across different eras and demonstrates how the style of bowling has evolved as the scoring patterns in cricket change. Additionally, an R program has been developed to facilitate the drawing of said graphical extension.

FE‐NN: Efficient‐scale transition for heterogeneous microstructures using neural networks

AbstractNumerical modeling and optimization of advanced composite materials can require huge computational effort when considering their heterogeneous mesostructure and interactions between different material phases within the framework of multiscale modeling. Employing machine learning methods for computational homogenization enables the reduction of computational effort for the evaluation of the mesostructural behavior while retaining high accuracy. Classically, one unit cell with representative characteristics of the material is chosen for the description of the heterogeneous structure, which presents a simplification of the actual composite. This contribution presents a neural network‐based approach for computational homogenization of composite materials with the ability to consider arbitrary compositions of the mesostructure. Therefore, various statistical volume elements and their respective constitutive responses are evaluated. Thereby, the naturally occurring fluctuation within the composition of the phases can be considered. Different approaches using distinct metrics to represent the arbitrary mesostructures are investigated in terms of required computational effort and accuracy.

The impact of mindfulness training on posttraumatic stress disorder symptoms, subjective sleep quality, and objective sleep outcomes in police officers.

Sleep disturbances cooccur with posttraumatic stress disorder (PTSD) and are often correlated with PTSD severity. Previous research has shown that sleep problems mediate the relationship between PTSD and negative physical and mental health outcomes but has relied on self-reported sleep quality. We tested the effects of mindfulness training-previously shown to improve sleep quality and reduce PTSD symptoms-on subjective and objective sleep metrics and relationships with reduced PTSD symptoms. Following baseline data collection in 114 law enforcement officers, we randomly assigned participants to either an 8-week mindfulness training group or a waitlist control group. We repeated assessments immediately posttraining and at 3-month follow-up. Self-reported PTSD symptoms and subjective sleep quality were measured at each visit with the PTSD checklist and Pittsburgh Sleep Quality Index (PSQI), respectively. Participants also wore a Fitbit Charge 2 continuously over the course of a 4- to 6-day work week following each visit, from which we extracted two distinct objective sleep metrics: total minutes of sleep and sleep efficiency. At baseline, PTSD symptoms were correlated with PSQI scores but not objective Fitbit metrics. Relative to waitlist, mindfulness training led to improved subjective sleep quality and reduced PTSD symptoms. Reduced PTSD symptoms mediated the improvement in subjective sleep quality following mindfulness training. Neither objective sleep metric demonstrated improvements following mindfulness training, nor did these metrics mediate reduced PTSD symptoms. This study provides evidence linking improved subjective sleep quality, but not objective sleep markers, to reductions in PTSD symptoms following mindfulness training. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Adherence to Life’s Essential 8 and incident chronic kidney disease: a prospective study of 147,988 UK Biobank participants

BackgroundThe American Heart Association recently updated Life’s Essential 8 (LE8) score. This amalgamation of health factors, recognized for their individual associations with chronic kidney disease (CKD) risk, provides a robust tool to assess overall cardiovascular health (CVH), which could potentially be extrapolated to predict CKD risk. ObjectivesThis study aimed to investigate the association between levels of CVH, as measured by the LE8 score, and risk of CKD in the UK Biobank. MethodsA total of 147,988 participants free of CKD and cardiovascular disease from the UK Biobank were included in this prospective study. CVH levels were categorized as low (0–49), moderate (50–79), and high (80–100) using LE8 score. An adjusted Cox proportional hazard model was used to investigate the association between LE8 and CKD. The population attributable-risk (PAR) was also calculated. ResultsDuring a median follow-up of 10 y, 1936 CKD cases were documented. A higher LE8 score was associated with a significant lower risk of CKD (P < 0.001), and a linear dose–response relationship was observed. Similar patterns were also found in the associations of the LE8 behavior and biological subscale scores with CKD. Compared with participants with a low CVH category, participants with a moderate CVH were associated with a 39% lower risk of developing CKD (hazard ratio [HR]: 0.61; 95% confidence interval [CI]: 0.52, 0.72); and those with a high CVH had a 57% lower risk of CKD incidence (HR: 0.43; 95% CI: 0.35, 0.53) after adjustment for covariates. Among 8 distinct metrics of LE8 score, the BMI metric had the highest PAR (24.6%; 95% CI: 18.8, 30.2). Of the total CKD risk, 3.2% (95% CI: 1.4, 5.0) was attributable to inadequate or excessive sleep duration. ConclusionsHigh CVH, defined by LE8, is significantly associated with a lower risk of CKD. These results suggest that promoting optimal cardiovascular health may lower the burden of CKD.

The Effects of Business Strategy and Product Market Competition on Real Earnings Management

This research investigated the singular and combined effects of firm-level business strategy (BS) and industry-level market competition (MC) on real activities-based earnings management (REM). A composite strategy score based on Miles and Snow’s framework was used to empirically assess BS, while MC was measured through three distinct metrics, and REM was calculated based on Roychowdhury’s (2006) models of abnormal level of production, sales, and discretionary expenditures. Archival data from United Stated (U.S.) non-financial public listed firms in the period 1987-2020 was analyzed using Ordinary Least Squares (OLS) regressions, controlled for industry and year fixed effects. Findings suggested that firms following an innovation-oriented prospector strategy are associated with lower levels of REM than firms following an efficiency-oriented defender strategy. While MC alone did not have a significant effect on REM, the combined effect of BS and MC reveals that prospectors in more competitive markets engage less in REM activities, which confirms the relevance of competition in the relationship between BS and REM. This research contributes to earnings management literature by documenting that REM practices are affected not only by internal choices of resource allocation in accordance with a business strategy, but also by exogenous determinants of market competition.

Camera Attributes Control for Visual Odometry With Motion Blur Awareness

The practical application of visual odometry (VO) is challenging under High Dynamic Range (HDR) environments which has a significant impact on the quality of feature point tracking. There are two key attributes that affect the image quality of a camera, namely exposure time and gain. The goal of camera attributes control research is to design algorithms that can actively adjust camera acquisition attributes to obtain clear, information-rich images. We propose an efficient and stable camera attributes control method with distinct image quality metrics and a linear convergence search algorithm. Specifically, we propose to use the weighted sum of image gradient and entropy to represent the image quality. Its first order derivative is also computed for a line search algorithm to determine the optimal camera exposure. Then, the algorithm estimates scene change speed by optical flow and finds out the maximum exposure time without motion blur. Ultimately, the maximum exposure time and optimal camera exposure are used to determine the camera properties including exposure time and gain. Additionally, the proposed method speeds up the convergence of the algorithm with the help of simulated image generation which enables nearly optimal attributes to be obtained directly from an overexposed or underexposed image. To validate the proposed approach, we test it in challenging HDR environments with fast motion and compare it with built-in automatic exposure. It will be open source for the benefit of the community.

Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation

In this paper, a new methodology is proposed that allows for the low-complexity development of neural network (NN) based equalizers for the mitigation of impairments in high-speed coherent optical transmission systems. In this work, we provide a comprehensive description and comparison of various deep model compression approaches that have been applied to feed-forward and recurrent NN designs. Additionally, we evaluate the influence these strategies have on the performance of each NN equalizer. Quantization, weight clustering, pruning, and other cutting-edge strategies for model compression are taken into consideration. In this work, we propose and evaluate a Bayesian optimization-assisted compression, in which the hyperparameters of the compression are chosen to simultaneously reduce complexity and improve performance. Next, this paper presents four distinct metrics (RMpS, BoP, NABS, and NLGs) that are discussed here that can be used to evaluate the amount of computing complexity required by various compression algorithms. These measurements can serve as a benchmark for evaluating the relative effectiveness of various NN equalizers when compression approaches are used. In conclusion, the trade-off between the complexity of each compression approach and its performance is evaluated by utilizing both simulated and experimental data in order to complete the analysis. By utilizing optimal compression approaches, we show that it is possible to design an NN-based equalizer that is simpler to implement and has better performance than the conventional digital back-propagation (DBP) equalizer with only one step per span. This is accomplished by reducing the number of multipliers used in the NN equalizer after applying the weighted clustering and pruning algorithms. Furthermore, we demonstrate that an equalizer based on NN can also achieve superior performance while still maintaining the same degree of complexity as the full electronic chromatic dispersion compensation block. We conclude our analysis by highlighting open questions and existing challenges, as well as possible future research directions.

Intelligent Energy-Aware Thermal Exchange Optimization with Deep Learning Model for IoT-Enabled Smart Healthcare.

In recent years, Internet of Things (IoT) and advanced sensor technologies have gained considerable interest in linking different medical devices, patients, and healthcare professionals to improve the quality of medical services in a cost-effective manner. The evolution of the smart healthcare sector has considerably enhanced patient safety, accessibility, and operational competence while minimizing the costs incurred in healthcare services. In this background, the current study develops intelligent energy-aware thermal exchange optimization with deep learning (IEA-TEODL) model for IoT-enabled smart healthcare. The aim of the proposed IEA-TOEDL technique is to group the IoT devices into clusters and make decisions in the smart healthcare sector. The proposed IEA-TEODL technique constructs clusters using the energy-aware chaotic thermal exchange optimization-based clustering (EACTEO-C) scheme. In addition, the disease diagnosis model also intends to classify the collected healthcare data as either presence or absence of the disease. To accomplish this, the proposed IEA-TODL technique involves several subprocesses such as preprocessing, K-medoid clustering-based outlier removal, multihead attention bidirectional long short-term memory (MHA-BLSTM), and weighted salp swarm algorithm (WSSA). The utilization of outlier removal and WSSA-based hyperparameter tuning process assist in achieving enhanced classification outcomes. In order to demonstrate the enhanced outcomes of the IEA-TEODL approach, a wide range of simulations was conducted against benchmark datasets. The simulation results inferred the enhanced outcomes of the IEA-TEODL technique over recent techniques under distinct evaluation metrics.

Association between urban greenspace, tree canopy cover and intentional deaths: An exploratory geospatial analysis

Greenspaces can provide restorative experiences, offer opportunities for outdoor recreation, and reduce mental fatigue; all of which may improve community health and safety. Yet few studies have examined the neighborhood-level benefits of greenspace in reducing violent deaths. This study explored the association between three distinct greenspace metrics: public greenspace quantity, public greenspace accessibility, neighborhood tree canopy cover, and intentional deaths (i.e., homicides and suicides). Generalized linear models and spatial error models investigated the association between greenspace, tree canopy and intentional deaths in three geographically distinct cities in North Carolina, USA. Results revealed that increased neighborhood greenspace accessibility and tree canopy cover were associated with reduced intentional deaths in all three urban areas. Neighborhood greenspace accessibility was the most protective factor across all study areas. The association between neighborhood greenspace accessibility and intentional deaths was more significant for non-firearm deaths as compared to firearm deaths, indicating that weapon type may be an important consideration for neighborhood greenspace interventions. Compared to predominantly White neighborhoods, predominantly Black neighborhoods had higher rates of homicide in Asheville and Durham and higher rates of suicide in Charlotte. Future policy and research should focus on improving equitable access to existing and future greenspaces, especially in primarily Black neighborhoods.

Immune resilience despite inflammatory stress promotes longevity and favorable health outcomes including resistance to infection

Some people remain healthier throughout life than others but the underlying reasons are poorly understood. Here we hypothesize this advantage is attributable in part to optimal immune resilience (IR), defined as the capacity to preserve and/or rapidly restore immune functions that promote disease resistance (immunocompetence) and control inflammation in infectious diseases as well as other causes of inflammatory stress. We gauge IR levels with two distinct peripheral blood metrics that quantify the balance between (i) CD8+ and CD4+ T-cell levels and (ii) gene expression signatures tracking longevity-associated immunocompetence and mortality-associated inflammation. Profiles of IR metrics in ~48,500 individuals collectively indicate that some persons resist degradation of IR both during aging and when challenged with varied inflammatory stressors. With this resistance, preservation of optimal IR tracked (i) a lower risk of HIV acquisition, AIDS development, symptomatic influenza infection, and recurrent skin cancer; (ii) survival during COVID-19 and sepsis; and (iii) longevity. IR degradation is potentially reversible by decreasing inflammatory stress. Overall, we show that optimal IR is a trait observed across the age spectrum, more common in females, and aligned with a specific immunocompetence-inflammation balance linked to favorable immunity-dependent health outcomes. IR metrics and mechanisms have utility both as biomarkers for measuring immune health and for improving health outcomes.

Accuracy assessment of RFerns, NB, SVM, and kNN machine learning classifiers in aquaculture

BackgroundFish diseases are the primary cause of decreased productivity and financial losses in fish farms. Detecting and monitoring fish disease using human eye is a time-consuming manual process. Technological advancements have made possible that data may now be collected at unprecedented speeds, quantities, and complexity, with far less effort and expense. Species must be provided optimum environmental conditions for healthy production. However, sub-optimal conditions and managerial issues could lead to disease outbreaks in fish farms. Machine learning (ML) classifiers can provide solutions to fish farm difficulties by collecting data with less efforts. MethodsThis study investigated water physico-chemical parameters potentially responsible for bacterial disease outbreak in fish farms. Four most popular ML algorithms, i.e., support vector machine (SVM), naïve bayes (NB), random ferns (RFerns), and K-nearest neighbor (kNN) were used to detect the physico-chemical parameters of water causing the disease. Data were collected from 3 different farms in two-month periods for 1 year. Models were developed by using 10-fold cross validation procedure to the training dataset data for each model. The models were examined using seven distinct metrics throughout the training and testing phases. ResultsThe SVM and RFerns classifiers produced accurate results (100% for both) during the testing phase, while kNN and NB classifiers achieved lower accuracy (91.3% accuracy for both). ConclusionThe SVM and RFerns algorithms performed better than kNN and NB algorithms in both the training and testing phases of the study. Although earlier research confirms the efficacy of the SVM algorithm in aquaculture, comparable efficacy of RFerns with SVM has been reported in this sector for the first time, which is a significant addition to the literature.

0114 Interplay of School and Free Days on the Association of Circadian Misalignment with Heart Rate Variability in Adolescents

Abstract Introduction The circadian timing system, which governs the sleep-wake cycle, is involved in cardiometabolic and autonomic regulation. Heart rate variability (HRV), a marker of cardiac autonomic modulation (CAM), is known to predict cardiovascular morbidity. Therefore, circadian misalignment of the sleep-wake cycle, which is highly prevalent in adolescents and may express differently if youth are in school or free-days, is likely to impact CAM. We examined whether deviations in the circadian timing of sleep are associated with blunted HRV in adolescents as a function of being entrained to school or not. Methods We studied 337 population-based randomly-selected adolescents from the Penn State Child Cohort (median 16 years; 47% female; 21% racial/ethnic minority) who had at least 3-night at-home actigraphy (ACT), in-lab 9-h polysomnography (PSG) and 24-h Holter-monitoring heart rate variability (HRV) data. ACT-measured sleep midpoint (SM) was calculated as the midpoint of the sleep period for weekdays (5-nights) and weekends (2-nights), whereas ACT-measured sleep regularity (SR) was calculated as the intra-individual standard deviation of the SM across 5- and 7-nights. Frequency and time-domain HRV indices were primary outcomes. Linear regression models were stratified by “in school” and “on break” to test SM and SR as predictors of HRV indices while accounting for sex, race/ethnicity, age, BMI percentile, ACT-sleep duration, ACT-sleep variability, and PSG-apnea/hypopnea index. Results Lower SR on schooldays was associated with lower nighttime Log-HF, Log-LF and SDNN, yet higher HR (all p&amp;lt; 0.01); e.g., each standard deviation increase in SR on schooldays (i.e., 45m higher) was associated with -7.15ms (2.57) in nighttime SDNN (p=0.006). A later SM on free weekends was associated with lower nighttime Log-HF, Log-LF, SDNN and RMSSD, yet higher HR and LF-HF ratio (all p&amp;lt; 0.03); e.g., each standard deviation increase in SM on free weekends (i.e., 1.5h later) was associated with -7.33ms (2.85) in nighttime RMSSD (p=0.011). Conclusion A delayed and an irregular sleep midpoint during breaks and during schooldays, respectively, were associated with impaired CAM in adolescents. These data suggest that not only circadian misalignment contributes to adverse cardiovascular outcomes but its distinct metrics require measurement under different entrainment conditions in adolescents. Support (if any) NIH (R01HL136587,R01MH118308,UL1TR000127), American Heart Association (23PRE1011962).

Rainfall changes perceived by farmers and captured by meteorological data: two sides to every story

Subsistence farmers with high dependency on natural resources are exceptionally vulnerable to rainfall changes. Besides, they are in the front row when it comes to observing these changes. Studies that systematically investigate similarities and differences between measured and perceived rainfall changes are typically limited to trends in rainfall amounts, thereby disregarding changes in extreme events, rainy season durations, and timing. We address this gap by contrasting rainfall changes perceived by subsistence farmers in the Ethiopian highlands with meteorological daily rainfall data derived from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). We applied nine distinct metrics for rainfall dynamics, accounting for rainfall variability, including extreme events and changes in the onset and cessation of the two rainy seasons. Farmers perceived increasingly unreliable rainfall for both the short and the long rainy seasons, with later onset and earlier cessation, increasing rainfall intensity, and increasing occurrence of untimely rainfall and droughts. This partially disagrees with the CHIRPS data that indicate most significant rainfall changes for the short rainy season only. Since the early 1980s, this season has been experiencing decreasing rainfall amounts, with high variability between years and an increasingly uncertain – yet delayed – onset. In contrast, the long rainy season experienced little changes in rainfall. Our results point towards changing farmers' water availability and water demand as an explanation for the perceived deteriorating rainfall conditions. As farmers’ perceptions partly diverge from meteorological observations, both data sources should be used complementarily to improve our understanding of climatic change.

A dedicated benchmark for contour-based corner detection evaluation

Numerous contour-based corner detection (CBCD) algorithms have been proposed recently, necessitating effective and practical evaluation. Most existing methods evaluate corner detection accuracy through metrics between the testing image and its attacked versions or rely on image-specific ground truth for corner evaluation. These methods use images as input, failing to solely evaluate the corner detection performance but combining it with contour extraction evaluation. Since contour extraction is another important research topic and existing CBCD algorithms almost have no contribution to contour extraction, this intertwining may negatively impact the evaluation results, hindering corner detection development. Furthermore, most evaluation methods directly provide simple statistical scores of evaluation metrics, such as the mean value, which are inadequate to reflect the overall performance distribution. This study presents a novel benchmark that is specifically designed for assessing CBCD methods, which includes two major contributions. Firstly, we design two dedicated datasets, one with the ground-truth corner and the other without them. The dedicated contours instead of images are employed as input to evaluate numerous CBCD methods, eliminating the impact of the extracted contour quality. When the ground-truth corners are unavailable, we employ additional contour attacks, including Gaussian noise, projective, and combined geometry on contours, to simulate real-world complex image processes compared with the attacks in existing evaluation methods. Secondly, we evaluate the performance of twelve CBCD methods using six distinct metrics based on the constructed contour datasets. To gain a deep insight into the overall performance distribution, the sign test method for hypothesis testing is utilized alongside some simple statistical measures for evaluation metric analysis. Experimental results demonstrated that no individual method performs the best across all six evaluation metrics, while different CBCD algorithms have their positive scenarios. The evaluation code will be publicly available at https://github.com/roylin1229/CBCD_eva.

Valid outcomes for screening and progress monitoring: Fluency is superior to accuracy in curriculum-based measurement.

Curriculum-based measurement (CBM) has conventionally included accuracy criteria with recommended fluency thresholds for instructional decision-making. Some scholars have argued for the use of accuracy to directly determine instructional need (e.g., Szadokierski et al., 2017). However, accuracy and fluency have not been directly examined to determine their separate and joint value for decision-making in CBM prior to this study. Instead, there was an assumption that instruction that emphasized accurate responding should be monitored with accuracy data, which evolved into the use of complementing CBM fluency scores with accuracy or using timed assessment to compute percent of responses correct and using accuracy criteria to determine instructional need. The purpose of this article was to examine fluency and accuracy as related but distinct metrics with psychometric properties and associated benefits and limits. Findings suggest that the redundancy between accuracy and fluency causes them to perform comparably overall, but that (a) fluency is superior to accuracy when accuracy is computed on a timed sample of performance, (b) timed accuracy adds no benefit relative to fluency alone, and (c) accuracy when collected under timed assessment conditions has substantial psychometric limitations that make it unsuitable for the formative instructional decisions which are commonly made using CBM data. The conventional inclusion of accuracy criteria in tandem with fluency criteria for instructional decision-making in CBM should be reconsidered as there may be no added predictive value, but rather additional opportunity for error due to the problems associated with unfixed trials in timed assessment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Geometric flows of G2-structures on 3-Sasakian 7-manifolds

A 3-Sasakian structure on a 7-manifold may be used to define two distinct Einstein metrics: the 3-Sasakian metric and the squashed Einstein metric. Both metrics are induced by nearly parallel $G_2$-structures which may also be expressed in terms of the 3-Sasakian structure. Just as Einstein metrics are critical points for the Ricci flow up to rescaling, nearly parallel $G_2$-structures provide natural critical points of the (rescaled) geometric flows of $G_2$-structures known as the Laplacian flow and Laplacian coflow. We study each of these flows in the 3-Sasakian setting and see that their behaviour is markedly different, particularly regarding the stability of the nearly parallel $G_2$-structures. We also compare the behaviour of the flows of $G_2$-structures with the (rescaled) Ricci flow.