Differences in Attention Skill between Children with ADHD and Typically Developing Children in Indonesian Primary Schools

Background: Attention Deficit Hyperactivity Disorder (ADHD) is the most common neuropsychological developmental disorder in children. ADHD is characterized by inattention, overactivity, and impulsivity. However, the attention skill is not clearly studied in children with ADHD in Indonesia. Objective: The aim of the present study was to investigate the differences in attention skill between children with ADHD and typically developing children (TD), and identify the differences between ADHD subtypes at primary schools in Bandung city Indonesia in terms of the parameter of attention. Methods: This study used the quantitative method. The population of this study was children with ADHD and typically developing (TD) school-age children. The data sampling technique was purposive sampling, consisting of 30 children as a group of ADHD children and 30 children as a group of TD children. The instrument to collect data was Wechsler Intelligence Scale for Children (WISC), Indonesian ADHD Rating Scale (IARS), and The D2 test of attention. The analysis method used t-test and SPSS V.24 for Windows. Results: This study showed significant differences in attention skill performance between children with ADHD and TD children in the variables including the total number of tasks completed, omission, commission, error rate, total amount minus errors or selective attention skill, fluctuation rate and concentration performance. There have been observed significant differences between ADHD subtypes in terms of total number, omission, commission, error rate, total amount minus errors or selective attention skill, and concentration performance. Conclusion: Children with ADHD were found to have lower results than typically developing children in terms of attention skill, inhibiting control, and ability in performance accuracy. Children with ADHD-C subtypes were found to have more inattentive tendencies, hyperactive, and impulsive compared to ADHD-I and ADHD-H. ADHD-C subtype showed more deficits than ADHD-I and ADHD-H in response inhibition and accuracy of performance.

Combining Global Geopotential Models, Digital Elevation Models, and GNSS/Leveling for Precise Local Geoid Determination in Some Mexico Urban Areas: Case Study

This work shows improvements of geoid undulation values obtained from a high-resolution Global Geopotential Model (GGM), applied to local urban areas. The methodology employed made use of a Residual Terrain Model (RTM) to account for the topographic masses effect on the geoid. This effect was computed applying the spherical tesseroids approach for mass discretization. The required numerical integration was performed by 2-D integration with 1DFFT technique that combines DFT along parallels with direct numerical integration along meridians. In order to eliminate the GGM commission error, independent geoid undulations values obtained from a set of GNSS/leveling stations are employed. A corrector surface from the associated geoid undulation differences at the stations was generated through a polynomial regression model. The corrector surface, in addition to the GGM commission error, also absorbs the GNSS/leveling errors as well as datum inconsistencies and systematic errors of the data. The procedure was applied to five Mexican urban areas that have a geodetic network of GNSS/leveling points, which range from 166 to 811. Two GGM were evaluated: EGM2008 and XGM2019e_2159. EGM2008 was the model that showed relatively better agreement with the GNSS/leveling stations having differences with RMSE values in the range of 8–60 cm and standard deviations of 5–8 cm in four of the networks and 17 cm in one of them. The computed topographic masses contribution to the geoid were relatively small, having standard deviations on the range 1–24 mm. With respect to corrector surface estimations, they turned out to be fairly smooth yielding similar residuals values for two geoid models. This was also the case for the most recent Mexican gravity geoid GGM10. For the three geoid models, the second order polynomial regression model performed slightly better than the first order with differences up to 1 cm. These two models produced geoid correction residuals with a standard deviation in one test area of 14 cm while for the others it was of about 4–7 cm. However, the kriging method that was applied for comparison purposes produced slightly smaller values: 8 cm for one area and 4–6 cm for the others.

Modality differences in ERP components between somatosensory and auditory Go/No-go paradigms in prepubescent children

We investigated modality differences in the N2 and P3 components of event-related potentials (ERPs) between somatosensory and auditory Go/No-go paradigms in eighteen healthy prepubescent children (mean age: 125.9±4.2 months). We also evaluated the relationship between behavioral responses (reaction time, reaction time variability, and omission and commission error rates) and amplitudes and latencies of N2 and P3 during somatosensory and auditory Go/No-go paradigms. The peak latency of No-go-N2 was significantly shorter than that of Go-N2 during somatosensory paradigms, but not during auditory paradigms. The peak amplitude of P3 was significantly larger during somatosensory than auditory paradigms, and the peak latency of P3 was significantly shorter during somatosensory than auditory paradigms. Correlations between behavioral responses and the P3 component were not found during somatosensory paradigms. On the other hand, in auditory paradigms, correlations were detected between the reaction time and peak amplitude of No-go-P3, and between the reaction time variability and peak latency of No-go-P3. A correlation was noted between commission error and the peak latency of No-go-N2 during somatosensory paradigms. Compared with previous adult studies using both somatosensory and auditory Go/No-go paradigms, the relationships between behavioral responses and ERP components would be weak in prepubescent children. Our data provide findings to advance understanding of the neural development of motor execution and inhibition processing, that is dependent on or independent of the stimulus modality.

Missing Burns in the High Northern Latitudes: The Case for Regionally Focused Burned Area Products

Global estimates of burned areas, enabled by the wide-open access to the standard data products from the Moderate Resolution Imaging Spectroradiometer (MODIS), are heavily relied on by scientists and managers studying issues related to wildfire occurrence and its worldwide consequences. While these datasets, particularly the MODIS MCD64A1 product, have fundamentally improved our understanding of wildfire regimes at the global scale, their performance may be less reliable in certain regions due to a series of region- or ecosystem-specific challenges. Previous studies have indicated that global burned area products tend to underestimate the extent of the burned area within some parts of the boreal domain. Despite this, global products are still being regularly used by research activities and management efforts in the northern regions, likely due to a lack of understanding of the spatial scale of their Arctic-specific limitations, as well as an absence of more reliable alternative products. In this study, we evaluated the performance of two widely used global burned area products, MCD64A1 and FireCCI51, in the circumpolar boreal forests and tundra between 2001 and 2015. Our two-step evaluation shows that MCD64A1 has high commission and omission errors in mapping burned areas in the boreal forests and tundra regions in North America. The omission error overshadows the commission error, leading to MCD64A1 considerably underestimating burned areas in these high northern latitude domains. Based on our estimation, MCD64A1 missed nearly half the total burned areas in the Alaskan and Canadian boreal forests and the tundra during the 15-year period, amounting to an area (74,768 km2) that is equivalent to the land area of the United States state of South Carolina. While the FireCCI51 product performs much better than MCD64A1 in terms of commission error, we found that it also missed about 40% of burned areas in North America north of 60° N between 2001 and 2015. Our intercomparison of MCD64A1 and FireCCI51 with a regionally adapted MODIS-based Arctic Boreal Burned Area (ABBA) shows that the latter outperforms both MCD64A1 and FireCCI51 by a large margin, particularly in terms of omission error, and thus delivers a considerably more accurate and consistent estimate of fire activity in the high northern latitudes. Considering the fact that boreal forests and tundra represent the largest carbon pool on Earth and that wildfire is the dominant disturbance agent in these ecosystems, our study presents a strong case for regional burned area products like ABBA to be included in future Earth system models as the critical input for understanding wildfires’ impacts on global carbon cycling and energy budget.

The NIEER AVHRR snow cover extent product over China – a long-term daily snow record for regional climate research

A long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product from 1981 until 2019 over China has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER AVHRR SCE product has a spatial resolution of 5 km and a daily temporal resolution, and it is a completely gap-free product, which is produced through a series of processes such as the quality control, cloud detection, snow discrimination, and gap-filling (GF). A comprehensive validation with reference to ground snow-depth measurements during snow seasons in China revealed the overall accuracy is 87.4 %, the producer's accuracy was 81.0 %, the user's accuracy was 81.3 %, and the Cohen's kappa (CK) value was 0.717. Another validation with reference to higher-resolution snow maps derived from Landsat-5 Thematic Mapper (TM) images demonstrates an overall accuracy of 87.3 %, a producer's accuracy of 86.7 %, a user's accuracy of 95.7 %, and a Cohen's kappa value of 0.695. These accuracies were significantly higher than those of currently existing AVHRR products. For example, compared with the well-known JASMES AVHRR product, the overall accuracy increased approximately 15 %, the omission error dropped from 60.8 % to 19.7 %, the commission error dropped from 31.9 % to 21.3 %, and the CK value increased by more than 114 %. The new AVHRR product is already available at https://doi.org/10.11888/Snow.tpdc.271381 (Hao et al., 2021).

A-30 Measures of Attention and Inhibitory Control: Comparing the TOVA to the NIH Toolbox Flanker Test in Children and Adolescents with ADHD

Objective The Attention Comparison Score for the Test of Variables of Attention (TOVA) was developed as a “single score” method of differentiating individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) from controls (Leark, Greenburg, Kindschi, Dupuy & Hughes, 2008). Recent literature has documented that a more nuanced interpretation of TOVA scores, including the Attention Comparison Score, Commission Errors, and Omission Errors, can be more useful in describing the nature of impairment (e.g., sustained attention and/or inhibitory control) experienced by individuals diagnosed with ADHD (Winstone, Logid, Foley & Nicholls, 2019). The NIH Toolbox Cognition battery also assesses attention and inhibitory control by means of a Flanker Test. The current study examines whether the TOVA Attention Comparison Score predicts performance on the NIH Toolbox Flanker Test, and if adding TOVA Commission Error and Omission Error variables would predict greater amounts of variance on the Flanker Test in a pediatric sample. Method A sample of 64 pediatric patients (62.7% male, 37.3% female) diagnosed with ADHD aged 4–17 years (M = 11.25; SD = 3.74) was administered the NIH Toolbox Cognition Battery and TOVA as part of a comprehensive neuropsychological evaluation in a private practice in Scottsdale, Arizona. Results Our data found support that the more nuanced approach of adding commission and omission information better predicted Flanker scores than the Attention Comparison Score alone. Conclusion Based on the findings, clinicians utilizing the TOVA as a means of assessing for ADHD in pediatric populations should consider omission and commission errors to better understand attention and inhibitory control abilities.

Cloud Detection Using an Ensemble of Pixel-Based Machine Learning Models Incorporating Unsupervised Classification

Remote sensing imagery, such as that provided by the United States Geological Survey (USGS) Landsat satellites, has been widely used to study environmental protection, hazard analysis, and urban planning for decades. Clouds are a constant challenge for such imagery and, if not handled correctly, can cause a variety of issues for a wide range of remote sensing analyses. Typically, cloud mask algorithms use the entire image; in this study we present an ensemble of different pixel-based approaches to cloud pixel modeling. Based on four training subsets with a selection of different input features, 12 machine learning models were created. We evaluated these models using the cropped LC8-Biome cloud validation dataset. As a comparison, Fmask was also applied to the cropped scene Biome dataset. One goal of this research is to explore a machine learning modeling approach that uses as small a training data sample as possible but still provides an accurate model. Overall, the model trained on the sample subset (1.3% of the total training samples) that includes unsupervised Self-Organizing Map classification results as an input feature has the best performance. The approach achieves 98.57% overall accuracy, 1.18% cloud omission error, and 0.93% cloud commission error on the 88 cropped test images. By comparison to Fmask 4.0, this model improves the accuracy by 10.12% and reduces the cloud omission error by 6.39%. Furthermore, using an additional eight independent validation images that were not sampled in model training, the model trained on the second largest subset with an additional five features has the highest overall accuracy at 86.35%, with 12.48% cloud omission error and 7.96% cloud commission error. This model’s overall correctness increased by 3.26%, and the cloud omission error decreased by 1.28% compared to Fmask 4.0. The machine learning cloud classification models discussed in this paper could achieve very good performance utilizing only a small portion of the total training pixels available. We showed that a pixel-based cloud classification model, and that as each scene obviously has unique spectral characteristics, and having a small portion of example pixels from each of the sub-regions in a scene can improve the model accuracy significantly.

A Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing

The paper proposes a fully automatic algorithm approach to map burned areas from remote sensing characterized by human interpretable mapping criteria and explainable results. This approach is partially knowledge-driven and partially data-driven. It exploits active fire points to train the fusion function of factors deemed influential in determining the evidence of burned conditions from reflectance values of multispectral Sentinel-2 (S2) data. The fusion function is used to compute a map of seeds (burned pixels) that are adaptively expanded by applying a Region Growing (RG) algorithm to generate the final burned area map. The fusion function is an Ordered Weighted Averaging (OWA) operator, learnt through the application of a machine learning (ML) algorithm from a set of highly reliable fire points. Its semantics are characterized by two measures, the degrees of pessimism/optimism and democracy/monarchy. The former allows the prediction of the results of the fusion as affected by more false positives (commission errors) than false negatives (omission errors) in the case of pessimism, or vice versa; the latter foresees if there are only a few highly influential factors or many low influential ones that determine the result. The prediction on the degree of pessimism/optimism allows the expansion of the seeds to be appropriately tuned by selecting the most suited growing layer for the RG algorithm thus adapting the algorithm to the context. The paper illustrates the application of the automatic method in four study areas in southern Europe to map burned areas for the 2017 fire season. Thematic accuracy at each site was assessed by comparison to reference perimeters to prove the adaptability of the approach to the context; estimated average accuracy metrics are omission error = 0.057, commission error = 0.068, Dice coefficient = 0.94 and relative bias = 0.0046.

A Framework of Filtering Rules over Ground Truth Samples to Achieve Higher Accuracy in Land Cover Maps

Remote Sensing (RS) digital classification techniques require sufficient, accurate and ubiquitously distributed ground truth (GT) samples. GT is usually considered “true” per se; however, human errors, or differences in criteria when defining classes, among other reasons, often undermine this veracity. Trusting the GT is so crucial that protocols should be defined for making additional quality checks before passing to the classification stage. Fortunately, the nature of RS imagery allows setting a framework of quality controls to improve the confidence in the GT areas by proposing a set of filtering rules based on data from the images themselves. In our experiment, two pre-existing reference datasets (rDS) were used to obtain GT candidate pixels, over which inconsistencies were identified. This served as a basis for inferring five key filtering rules based on NDVI data, a product available from almost all RS instruments. We evaluated the performance of the rules in four temporal study cases (under backdating and updating scenarios) and two study areas. In each case, a set of GT samples was extracted from the rDS and the set was used both unfiltered (original) and filtered according to the rules. Our proposal shows that the filtered GT samples made it possible to solve usual problems in wilderness and agricultural categories. Indeed, the confusion matrices revealed, on average, an increase in the overall accuracy of 10.9, a decrease in the omission error of 16.8, and a decrease in the commission error of 14.0, all values in percent points. Filtering rules corrected inconsistencies in the GT samples extracted from the rDS by considering inter-annual and intra-annual differences, scale issues, multiple behaviours over time and labelling misassignments. Therefore, although some intrinsic limitations have been detected (as in mixed forests), the protocol allows a much better Land Cover mapping thanks to using more robust GT samples, something particularly important in a multitemporal context in which accounting for phenology is essential.

Inhibitory function and sustained attention following galvanic vestibular stimulation in children with attention-deficit/hyperactivity disorder

Background and Aim: In recent years, galvanic vestibular stimulation (GVS) has been used as an effective method in rehabilitation and treat­ment of psychological disorders in children and adults. This study was designed to evaluate the effect of GVS on response inhibition and susta­ined attention in children with attention-deficit/ hyperactivity disorder (ADHD). Methods: Seventeen children with ADHD, within the age range of 9−12 years, participated in this study. All participants were exposed to the go/no-go task. The behavioral outcomes and event-related potentials were recorded at baseline status, in sham condition, and after 20 minutes of exposure to GVS polarities, with an anode on the right mastoid region and a cathode on the left mastoid region. Results: The results showed that there was a sig­nificant difference in reducing the behavioral response of the commission error (p < 0.05). But the reduction in behavioral responses to omission error and reaction time were not significant (p > 0.05). However, regarding ERPs, reduced latencies and increased amplitudes of N2 and P3 waves were observed in GVS intervention, com­pared to the baseline and sham conditions (p < 0.05). Conclusion: The present results indicated the potential of GVS in improving of cognition func­tion in children with ADHD and could help us develop a new strategy for rehabilitation of res­ponse inhibition disorders in the future. Keywords: Galvanic vestibular stimulation; attention-deficit/hyperactivity disorder; go/no-go task; event-related potentials; motor control