Evaluation of ERA5 Precipitation Accuracy Based on Various Time Scales over Iran during 2000–2018

In regional studies, reanalysis datasets can extend precipitation time series with insufficient observations. In the present study, the ERA5 precipitation dataset was compared to observational datasets from meteorological stations in nine different precipitation zones of Iran (0.125° × 0.125° grid box) for the period 2000–2018, and measurement criteria and skill detection criteria were applied to analyze the datasets. The results of the daily analysis revealed that the correlation between ERA5 and observed precipitation were larger than 0.5 at 90% of stations. Also, The daily standard relative bias indicated that precipitation was overestimated in zone 6. As detection criteria, the frequency bias index (FBI) and proportion correct (PC) showed that the ERA5 data could capture daily precipitation events. Correlation confidence comparisons between the ERA5 and observational time series at daily, monthly, and seasonal scales revealed that the correlation confidence was higher at monthly and seasonal scales. The standard relative bias results at monthly and seasonal scales followed the daily relative bias results, and most of the ERA5 underestimations during the summer belonged to zone 1 in the coastal area of the Caspian Sea with convective precipitation. In addition, some complex mountainous regions were associated with overestimated precipitation, especially in northwest Iran (zone 6) in different time scales.

Learning Repetition, but not Syllable Reversal

Reduplication is common, but analogous reversal processes are rare, even though reversal, which involves nested rather than crossed dependencies, is less complex on the Chomsky hierarchy. We hypothesize that the explanation is that repetitions can be recognized when they match and reactivate a stored trace in short-term memory, but recognizing a reversal requires rearranging the input in working memory before attempting to match it to the stored trace. Repetitions can thus be recognized, and repetition patterns learned, implicitly, whereas reversals require explicit, conscious awareness. To test these hypotheses, participants were trained to recognize either a reduplication or a syllable-reversal pattern, and then asked to state the rule. In two experiments, above-chance classification performance on the Reversal pattern was confined to Correct Staters, whereas above-chance performance on the Reduplication pattern was found with or without correct rule-stating. Final proportion correct was positively correlated with final response time for the Reversal Correct Staters but no other group. These results support the hypothesis that reversal, unlike reduplication, requires conscious, time-consuming computation.

Mapping of Difficult Mathematics Topics in Vocational High Schools Based on National Examination Data

The educational achievement in Indonesia is measured by implementing the National Examination or Ujian Nasional (UN). Throughout its implementation, the UN results shown unsatisfying results, including in mathematics subjects. This fact indicated that there are mathematics topics that are considered to be difficult for students. This study aimed to investigate the difficult topics of mathematics in Vocational High School (VHS) based on UN data. This study was a descriptive research with the quantitative approach by using the UN report results in the level of VHS from Center for Educational Assessment of the Ministry of Education and Culture in the period of 2008 to 2017 (ten years) as a research data source. Data analysis was carried out in a quantitative descriptive by mapping the mathematics topics based on proportion correct answers or “daya serap”. A topic was considered difficult if the proportion correct answer was less than 50%. The results showed that there were 14 difficult mathematics topics in the UN implementation at the VHS level from 2008 to 2017. These topics include the comparison of trigonometric functions, solid figure, limit, measure of data dispersion, permutation and combination, differential, area between two curves, a measure of central data tendentious, probability, logarithm, integral, sequence and series, linear program, and polar coordinates system. The implications of the research findings for learning practice and future research opportunities are discussed.

The Institute on Methods and Protocols for Advancement of Clinical Trials in ADRD (IMPACT-AD): A Novel Clinical Trials Training Program

Background: Alzheimer’s Disease and Related Dementias (ADRD) clinical trials require multidisciplinary expertise in medicine, biostatistics, trial design, biomarkers, ethics, and informatics. Objectives: To provide focused interactive training in ADRD clinical trials to a diverse cadre of investigators. Design: The Institute on Methods and Protocols for Advancement of Clinical Trials in ADRD (IMPACT-AD) is a novel multidisciplinary clinical trial training program funded by the National Institute on Aging and the Alzheimer’s Association with two educational tracks. The Professionals track includes individuals who fill a broad variety of roles including clinicians, study coordinators, psychometricians, and other study professionals who wish to further their knowledge and advance their careers in ADRD trials. The Fellowship track includes current and future principal investigators and focuses on the design, conduct and analysis of ADRD clinical trials. Setting: The 2020 inaugural iteration of IMPACT-AD was held via Zoom. Participants: Thirty-five trainees (15 Fellowship track; 20 Professionals track) were selected from 104 applications (34% acceptance rate). Most (n=25, 71%) identified as female. Fifteen (43%) were of a non-white race; six (18%) were of Hispanic ethnicity; eight (23%) indicated they were the first person in their family to attend college. Measurements: Participants completed daily evaluations as well as pre- and post-course assessments of learning. Results: Across topic areas, >90% of trainees evaluated their change in knowledge based on the lectures as “very much” or “somewhat increased.” The mean proportion correct responses in pre- and post-course assessments increased from 55% to 75% for the Professionals track and from 54% to 78% for the Fellowship track. Conclusions: IMPACT-AD successfully launched a new training opportunity amid a global pandemic that preliminarily achieved the goals of attracting a diverse cohort and providing meaningful training. The course is funded through 2025.

Performance of small group majorities in a single task

Aggregating decisions from larger groups typically results in outcomes with higher accuracy than decision outcomes from single individuals or smaller groups. Here I argue that it is important to consider not only overall proportion of correct decisions, but also individual competencies in terms of hits (h) and correct rejections (cr). I show that small groups can perform better than randomly selected individuals and larger groups in a single task when the average individual proportion correct is above .5, h and cr are asymmetric around .5, and h+cr>1. If the average individual proportion correct is below .5 and h+cr<1, small groups perform worse than individuals and larger groups. I also demonstrate that these two performance patterns can occur in empirical data from studies on violent recidivism, psychiatric morbidity, anxiety, and deception detection. I also show that the presence of correlations between decisions in a single task has both beneficial and detrimental effects when it comes to small group performance.

Absorbent Power Power Subject Matter and Reliability Math Test Form Item Essay

This study aims to determine the proportion correct of mathematics subject matter and compare the reliability of essay tests that are scored with 3 different rubric models. The method used in this research is an experiment using design treatments by subjects, an experiment using one group (one group experiment) which is also an experimental group and a control group in different experimental periods. Schools that were sampled were 6 public and private junior high schools and students were sampled as many as 452 students. The research sample was determined by two-stage stratified random sampling. The objects in this study consisted of 22 essay questions in Mathematics for junior high school compiled from the same test specifications. Scoring uses 3 scoring rubrics models, namely scoring rubric Model 1 (0,1), Model 2 (0,1,2), and Model 3 (keywords). The results obtained from this study indicate the reliability of the mathematical essay test on the scoring rubric of model 1 is 0.6859, model 2 is 0.7025, and model 3 is 0.6986. Test reliability of the three scoring rubrics is high or reliable. Students' competence in completing mathematical essay questions is still low, which is under 50%. Only mathematical essay questions calculate volume at the cognitive level of knowledge and understanding above 70%. Student competencies in solving questions that measure reasoning is an average of 12.20%. In other words, the proportion correct of subject matter in two-dimentional figure and space less than 50%, this shows that students still do not understand the concept of the subject matter, especially the material of the central angular relationship, arc length, sector area, which can only be answered correctly by 17.58% of students.

Dynamic Visual Noise Does Not Affect Memory for Fonts

Dynamic visual noise (DVN) selectively impairs memory for some types of stimuli (e.g., colors, textures, concrete words), but not for others (e.g., matrices, Chinese characters, simple shapes). According to the image definition hypothesis, the key difference is whether the stimulus leads to images that are ill-defined or well-defined. The former will be affected because the addition of noise quickly reduces the usefulness of the image in supplying information about the item's identity. The image definition hypothesis predicts that fonts should lead to ill-defined images and therefore should be affected by DVN, and although three previous studies appear to show this result, they lack a key control condition and report only proportion correct. Two experiments reassessed whether DVN affects memory for fonts, but, unlike the previous studies, both included a static visual noise condition and both were analyzed using signal detection measures. There was no evidence that DVN affected memory for font information, thus disconfirming a prediction of the original version of image definition hypothesis. We suggest a revised version that focuses on redintegration can explain the results.

Comparison of SAFNWC/MSG Satellite Cloud Type with Vaisala CL51 Ceilometer-Detected Cloud Base Layer Using the Sky Condition Algorithm and Vaisala BL-View Software

Ceilometer detection can be used to determine cloud type based on cloud layer height. Satellite observations provide images of clouds’ physical properties. During the summer and winter of 2017, Satellite Application Facility on support to Nowcasting/Very Short-Range Forecasting Meteosat Second Generation (SAFNWC/MSG) cloud type was compared to cloud base layers based upon a sky condition algorithm of Vaisala CL51 ceilometer and the BL-View applied range-variant smoothing backscatter profile at the National Atmospheric Observatory in Košetice, Czech Republic. This study investigated whether the larger measurement range of CL51 improved high cloud base detection and the effect of the range-variant smoothing on cloud base detection. The comparison utilized a multi-category contingency table wherein hit rate, false alarm ratio, frequency of bias, and proportion correct were evaluated. The accuracy of low-level and high cloud type detection by satellite was almost identical in both seasons compared to that using the sky condition algorithm. The occurrence of satellite high cloud detection was greatest when the ceilometer detected high cloud base above low and/or medium cloud base. The hit rate of high cloud detection increased significantly when the BL-View-produced cloud base layer was applied as a reference. We conclude that BL-View produces more accurate high cloud base detection.

Validation of Satellite Daily Rainfall Estimates Over Indonesia

Rainfall is the most important factor in the Earth’s water and energy cycles. The aim of this research is to evaluate the accuracy of Global Satellite Mapping of Rainfall (GSMaP) data by referencing daily rain-gauged rainfall measurements across the Indonesian Maritime Continent. We compare the daily rainfall data from GSMaP Moving Kalman Filter (MVK) to readings from 152 rain-gauge observation stations across Indonesia from March 2014 to December 2017. The results show that the correlation coefficient (CC) provides better validation in the rainy season while root mean square error (RMSE) is more accurate in the dry season. The highest proportion correct (PC) value is obtained for Bali-NTT, while the highest probability of detection (POD) and false alarm ratio (FAR) values are obtained for Kalimantan. GSMaP-MVK data is over-estimated compared to observations in Indonesia, with the mean accuracy for daily rainfall estimation being 85.47% in 2014, 85.74% in 2015, 82.73 in 2016, and 82.59% in 2017.

Visual processing: conscious until proven otherwise

Unconscious perception, or perception without awareness, describes a situation where an observer's behaviour is influenced by a stimulus of which they have no phenomenal awareness. Perception without awareness is often claimed on the basis of a difference in thresholds for tasks that do and do not require awareness, for example, detecting the stimulus (requiring awareness) and making accurate judgements about the stimulus (based on unconscious processing). Although a difference in thresholds would be expected if perceptual evidence were processed without awareness, such a difference does not necessitate that this is actually occurring: a difference in thresholds can also arise from response bias, or through task differences. Here we ask instead whether the pattern of performance could be obtained if the observer were aware of the evidence used in making their decisions. A backwards masking paradigm was designed using digits as target stimuli, with difficulty controlled by the time between target and mask. Performance was measured over three tasks: detection, graphic discrimination and semantic discrimination. Despite finding significant differences in thresholds measured using proportion correct, and in observer sensitivity, modelling suggests that these differences were not the result of perception without awareness. That is, the observer was not relying solely on unconscious information to make decisions.