Omission Error Rates Research Articles

Ensemble automated approaches for producing high‐quality herbarium digital records

AbstractPremiseOne of the slowest steps in digitizing natural history collections is converting labels associated with specimens into a digital data record usable for collections management and research. Here, we address how herbarium specimen labels can be converted into digital data records via extraction into standardized Darwin Core fields.MethodsWe first showcase the development of a rule‐based approach and compare outcomes with a large language model–based approach, in particular ChatGPT4. We next quantified omission and commission error rates across target fields for a set of labels transcribed using optical character recognition (OCR) for both approaches. For example, we find that ChatGPT4 often creates field names that are not Darwin Core compliant while rule‐based approaches often have high commission error rates.ResultsOur results suggest that these approaches each have different strengths and limitations. We therefore developed an ensemble approach that leverages the strengths of each individual method and documented that ensembling strongly reduced overall information extraction errors.DiscussionThis work shows that an ensemble approach has particular value for creating high‐quality digital data records, even for complicated label content. While human validation is still needed to ensure the best possible quality, automated approaches can speed digitization of herbarium specimen labels and are likely to be broadly usable for all natural history collection types.

Wildfire CNN: An Enhanced Wildfire Detection Model Leveraging CNN and VIIRS in Indian Context

Introduction Wildfires are an unexpected global hazard that significantly impact environmental change. An accurate and affordable method of identifying and monitoring on wildfire areas is to use coarse spatial resolution sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Compared to MODIS, wildfire observations from VIIRS sensor data are around three times as extensive. Objective The traditional contextual wildfire detection method using VIIRS data mainly depends on the threshold value for classifying the fire or no fire which provides less performance for detecting wildfire areas and also fails in detecting small fires. In this paper, a wildfire detection method using Wildfiredetect Convolution Neural Network model is proposed for an effective wildfire detection and monitoring system using VIIRS data. Methods The proposed method uses the Convolutional Neural Network model and the study area dataset containing fire and non-fire spots is tested. The performance metrics such as recall rate, precision rate, omission error, commission error, F-measure and accuracy rate are considered for the model evaluation. Results The experimental analysis of the study area shows a 99.69% recall rate, 99.79% precision rate, 0.3% omission error, 0.2% commission error, 99.73% F-measure and 99.7% accuracy values for training data. The proposed method also proves to detect small fires in Alaska forest dataset for the testing data with 100% recall rate, 99.2% precision rate, 0% omission error, 0.7% commission error, 99.69% F-measure and 99.3% accuracy values. The proposed model achieves a 26.17% higher accuracy rate than the improved contextual algorithm. Conclusion The experimental findings demonstrate that the proposed model identifies small fires and works well with VIIRS data for wildfire detection and monitoring systems.

Attention improvement to transcranial alternating current stimulation at gamma frequency over the right frontoparietal network: a preliminary report.

Applying transcranial alternating current stimulation (tACS) at 40 Hz to the frontal and parietal regions, either unilaterally (left or right) or bilaterally, can improve cognitive dysfunctions. This study aimed to explore the influence of tACS at gamma frequency over right fronto-parietal (FP) region on attention. The analysis is based on retrospective data from a clinical intervention. We administered test of variables of attention (TOVA; visual mode) to 44 participants with various neuropsychiatric diagnoses before and after 12 sessions of tACS treatment. Alternating currents at 2.0 mA were delivered to the electrode positions F4 and P4, following the 10-20 EEG convention, for 20 mins in each session. We observed significant improvement across 3 indices of the TOVA, including reduction of variability in reaction time (p = 0.0002), increase in d-Prime (separability of targets and non-targets; p = 0.0157), and decrease in commission error rate (p = 0.0116). The mean RT and omission error rate largely remained unchanged. Artificial injection of tACS at 40 Hz over right FP network may improve attention function, especially in the domains of consistency in performance, target/non-target discrimination, and inhibitory control.

Forest Fire Detection Based on Spatial Characteristics of Surface Temperature

Amidst the escalating threat of global warming, which manifests in more frequent forest fires, the prompt and accurate detection of forest fires has ascended to paramount importance. The current surveillance algorithms employed for forest fire monitoring—including, but not limited to, fixed threshold algorithms, multi-channel threshold algorithms, and contextual algorithms—rely primarily upon the degree of deviation between the pixel temperature and the background temperature to discern pyric events. Notwithstanding, these algorithms typically fail to account for the spatial heterogeneity of the background temperature, precipitating the consequential oversight of low-temperature fire point pixels, thus impeding the expedited detection of fires in their initial stages. For the amelioration of this deficiency, the present study introduces a spatial feature-based (STF) method for forest fire detection, leveraging Himawari-8/9 imagery as the main data source, complemented by the Shuttle Radar Topography Mission (SRTM) DEM data inputs. Our proposed modality reconstructs the surface temperature information via selecting the optimally designated machine learning model, subsequently identifying the fire point through utilizing the difference between the reconstructed surface temperatures and empirical observations, in tandem with the spatial contextual algorithm. The results confirm that the random forest model demonstrates superior efficacy in the reconstruction of the surface temperature. Benchmarking the STF method against both the fire point datasets disseminated by the China Forest and Grassland Fire Prevention and Suppression Network (CFGFPN) and the Wild Land Fire (WLF) fire point product validation datasets from Himawari-8/9 yielded a zero rate of omission errors and a comprehensive evaluative index, predominantly surpassing 0.74. These findings show that the STF method proposed herein significantly augments the identification of lower-temperature fire point pixels, thereby amplifying the sensitivity of forest surveillance.

Developing a Semi-Supervised Strategy in Time Series Mapping of Wetland Covers: A Case Study of Zrebar Wetland, Iran

Wetlands, essential for Earth’s health, ecological balance, and local economies, require accurate monitoring and assessment for effective conservation. Data-driven models based on remote sensing are highly capable of monitoring the status and classification of wetlands. This study developed a semi-supervised framework for mapping wetland covers in Zrebar, Iran, using Landsat time series data from 1984 to 2022. A pixel purification technique was applied to the temporal candidate images to refine the initial training data (conventional scenario) and generate purified training data (proposed scenario). The Support Vector Machine (SVM) algorithm was utilized to classify the land cover within the wetland, and the accuracy of the two scenarios was evaluated and compared. Over the study period, the analysis of land cover changes within Zrebar Wetland revealed significant spatial and temporal changes in soil and farmland, reed, and water from 1984 to 2022. The omission error rates for the classes soil and farmland, reed, and water were decreased from 0.14, 0.14, and 0.12 for scenario 1 to 0.03, 0.05, and 0.05 for scenario 2, respectively. In addition, the commission error for these classes decreased from 0.13, 0.18, and 0.09 for scenario 1 to 0.04, 0.06, and 0.04 after applying the filtered training data in the scenario 2. Finally, the overall accuracy of the initial training data (scenario 1) and the filtered training data (scenario 2) were 0.86 and 0.94, respectively. These results underscore the effectiveness of the proposed strategy in enhancing the accuracy of land cover classification within the wetland over time, highlighting its potential for future wetland conservation efforts.

Unveiling the influence of task-relevance of emotional faces on behavioral reactions in a multi-face context using a novel Flanker-Go/No-go task

Recent research indicates that emotional faces affect motor control only when task-relevant. However, these studies utilized a single-face presentation, which does not accurately mirror real-life situations wherein we frequently engage with multiple individuals simultaneously. To overcome this limitation, we gave 40 participants two versions of a novel Flanker-Go/No-go task, where we presented three-face stimuli with a central target and two task-irrelevant flankers that could be congruent or incongruent with the target for valence and gender. In the Emotional Discrimination Task (EDT), participants had to respond to fearful or happy targets and refrain from moving with neutral ones. In the Gender Discrimination Task (GDT), the same images were shown, but participants had to respond according to the target's gender. In line with previous studies, we found an effect of valence only in EDT, where fearful targets increased reaction times and omission error rates compared to happy faces. Notably, the flanker effect, i.e., slower and less accurate responses in incongruent than congruent conditions, was not found. This likely stems from the higher perceptual complexity of faces than that of stimuli traditionally used in the Eriksen Flanker task (letters or signs), leading to a capacity limit in face feature processing.

75 The Association Between Cognitive Function and Older Adults Performance on a Naturalistic Cooking Task in the Home Environment

Objective:Cognitive impairment can affect an individual’s ability to perform routine tasks. In this study, we investigate how cognitive abilities relate to the accuracy and efficiency of performance on a naturalistic cooking task completed in older adults’ home environments. We hypothesized a positive association between task accuracy and global cognitive status, and task efficiency and executive functioning. We further hypothesized a negative association between omission errors and immediate and delayed memory recall.Participants and Methods:Fourteen community-dwelling older adults (Age, M = 73.92 years; Female = 9; Education, M = 16.38 years) along the continuum from normal aging to mild dementia completed a “Cooking Task” in their home environment. Specifically, participants were instructed to fry or scramble an egg, prepare slice of toast with jelly, serve side of sliced apple, pour glass of water, bring prepared items to table, and clean dishes used. Participants received ingredients necessary for task completion and a task list to reference. The task efficiency score (range 0-6) was based on multi-tasking and organizational skills (e.g., beginning the egg task early in session, plating items as prepared). Overall accuracy was computed by identifying error types (e.g., inefficiencies, substitutions, omissions, and subtasks attempted) and scaling accuracy (range 1-5) for each subtask, then summing all six subtask accuracy scores to get overall accuracy (range 6-30). Participants also completed a range of neuropsychological assessments, which included the Telephone Interview for Cognitive Status, Letter and Category Fluency from the Delis-Kaplan Executive Function System, and immediate and delayed recall measures from the Repeatable Battery for the Assessment of Neuropsychological Status. Due to the small sample size, findings are preliminary, and scatterplots were evaluated for outliers that might influence findings.Results:Consistent with hypotheses, as overall accuracy on the Cooking Task increased so did performance on the global cognitive measure (TICS: r = 0.61, p = 0.02). Lower rates of omission errors were also associated with better performance on both immediate (r = -0.75, p < 0.01) and delayed (r = -0.55, p = 0.04) recall indices. However, these findings were not specific, as overall accuracy also significantly correlated with the memory indices and verbal fluency measures (ps < 0.05). Additionally, lower rates of omission errors significantly correlated with performance on the TICS and the D-KEFS Letter Fluency (ps < 0.05). Contrary to our hypothesis, no significant associations were found between cooking task efficiency and executive functioning (D-KEFS subtests). There were also no significant correlations between cooking task efficiency and global cognitive status or memory.Conclusions:The present study supported our hypotheses that better overall task accuracy is associated with higher cognitive status and lower rates of omission errors correlate with better immediate and delayed recall abilities. However, the findings were not specific to these domains of functioning but rather suggest that clinical assessments measuring a range of cognitive abilities are related to the accuracy of daily task performance and omission errors on routine daily tasks. Future research will explore the validity of the efficiency measure.

Determining Medication Errors in an Adult Intensive Care Unit.

Research addressing the costs of Medication errors (MEs) is still scarce despite issues related to patient safety having significant economic and health impacts, making it imperative to analyze the costs and adverse events related to MEs for a better patient, professional, and institutional safety. To identify the number of medication errors and verify whether this number was associated with increased hospitalization costs for patients in an Intensive Care Unit (ICU). This retrospective cross-sectional cohort study evaluated secondary data from patients' electronic medical records to compile variables, create a model, and survey hospitalization costs. The statistical analysis included calculating medication error rates, descriptive analysis, and simple and multivariate regression. The omission error rate showed the highest number of errors per drug dose (59.8%) and total errors observed in the sample (55.31%), followed by the time error rate (26.97%; 24.95%). The omission error had the highest average when analyzing the entire hospitalization (170.40) and day of hospitalization (13.79). Hospitalization costs were significantly and positively correlated with scheduling errors, with an increase of BRL 121.92 (about USD $25.00) (95% CI 43.09; 200.74), and to prescription errors, with an increase of BRL 63.51 (about USD $3.00) (95% CI 29.93; 97.09). We observed an association between two types of medication errors and increased hospitalization costs in an adult ICU (scheduling and prescription errors).

Executive Functioning as a Predictor of Adverse Driving Outcomes in Teen Drivers With ADHD

Objective: The present study examined the association between executive functioning (EF) and risky driving behaviors in teens with ADHD. Method: Teens diagnosed with ADHD (n = 179; Mage = 17.4 years) completed two 15-min drives in a fixed-base driving simulator. EF was assessed using parent- and self-report Behavior Rating Inventory of Executive Functioning (BRIEF-2), a temporal reproduction task, and a Go/No-Go task (GNG). Driving outcomes included known predictors of crashes: count of long (>2 s) off-road glances, standard deviation (SD) of lane position (SDLP), mean speed, and SD speed. Generalized linear mixed models, controlling for intelligence and driving experience, were conducted. Results: Higher rates of GNG commission errors predicted higher rates of long off-road glances. Lower parent-rated EF and increased rates of GNG omission errors predicted SDLP. Higher rates of GNG commission errors also predicted faster average driving speed. Conclusion: Heterogeneity in EF is associated with differences in teen ADHD risky driving behaviors.

Integrating Machine Learning and a Spatial Contextual Algorithm to Detect Wildfire from Himawari-8 Data in Southwest China

Timely wildfire detection is helpful for fire monitoring and fighting. However, the available wildfire products with high temporal resolutions face problems, such as high omission error and commission error (false alarm) rates. This study proposed a wildfire detection algorithm combined with an improved spatial contextual algorithm and machine learning method in southwest China. First, a dataset consisting of a formation of high-confidence fire pixels combining the WLF (Himawari Wild Fire product) and VIIRS wildfire products was constructed. Then, a model to extract potential fire pixels was built using the random forest method. Additionally, an improved spatial contextual algorithm was used to identify actual fire pixels from potential fire pixels. Finally, strategies such as sun glint rejection were used to remove false alarms. As a result, the proposed algorithm performed better, with both a lower omission error rate and a lower commission error rate than the WLF product. It had a higher F1 score (0.47) than WLF (0.43) with VIIRS for reference, which means it is more suitable for wildfire detection.

A weighted contextual active fire detection algorithm based on Himawari-8 data

ABSTRACT Himawari-8, a geostationary satellite, is equipped with the Advanced Himawari Imager (AHI) sensor, which offers significant advantages for forest fire monitoring. This study proposes a weighted contextual fire detection algorithm (AHI_WFDA) that can apply to the AHI sensor as a global fire detection algorithm. Unlike the traditional pass-by screening algorithms, the algorithm takes into account the characteristics of different bands and assigns different weights and corresponding thresholds to the test conditions based on the bands' sensitivity to fire. To validate the algorithm's performance, we tested it on fires in five target areas. We regard MODIS data as the ground truth data and it was used as the benchmark for comparison with the AHI_WFDA , the Himawari-8 official product WLF, and the traditional spatial contextual algorithm (the reproduced SEVIRI algorithm). The results show that the AHI_WFDA significantly reduces the commission error rate compared to the WLF product. While our algorithm's accuracy rate is not superior to the SEVIRI algorithm, it detects more fire incidents correctly. Compared with the MODIS active fire product, the AHI_WFDA's omission error rate is about 63%. In contrast, the relative commission error rate is about 12%, which is in line with the results of some previous studies. In addition, we conducted detailed verification of our algorithm's results with the support of the Landsat series and Sentinel-2 data. The results show that the algorithm in this paper can effectively exploit the fire detection capability of AHI sensors and provide a new idea for the subsequent algorithms.

Large-Scale Populus euphratica Distribution Mapping Using Time-Series Sentinel-1/2 Data in Google Earth Engine

Accurate and efficient large-scale mapping of P. euphratica distribution is of great importance for managing and protecting P. euphratica forests, policy making, and realizing sustainable development goals in the ecological environments of desert areas. In large regions, numerous types of vegetation exhibit spectral characteristics that closely resemble those of P. euphratica, such as Tamarix, artificial forests, and allée trees, posing challenges for the accurate identification of P. euphratica. To solve this issue, this paper presents a method for large-scale P. euphratica distribution mapping. The geographical distribution characteristics of P. euphratica were first utilized to rapidly locate the appropriate region of interest and to further reduce background complexity and interference from other similar objects. Spectral features, indices, phenological features, and backscattering features extracted from all the available Sentinel-2 MSI and Sentinel-1 SAR data from 2021 were regarded as the input for a random forest model used to classify P. euphratica in the GEE platform. The results were then compared with the results from the method using only spectral features and index features, the results from the method that only added phenological features, and the results from the method that added phenological features and backscattering features by visually and quantitatively referencing field-surveyed samples, UAV data, and high-spatial-resolution data from Google Earth Data and Map World. The comparison indicated that the proposed method, which adds both phenological and time-series backscattering features, could correctly distinguish P. euphratica from other types of vegetation that have spectral information similar to P. euphratica. The rates of omission errors (OEs), commission errors (CEs), and overall accuracy (OA) for the proposed method were 12.53%, 11.01%, and 89.32%, respectively, representing increases of approximately 9%, 17%, and 13% in comparison with the method using only spectral and index features. The proposed method significantly improved the accuracy of P. euphratica classification in terms of both omission and, especially, commission.

Assessing the Performance of a Handheld Laser Scanning System for Individual Tree Mapping—A Mixed Forests Showcase in Spain

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements.

Wildlife habitat mapping using Sentinel-2 imagery of Mehao Wildlife Sanctuary, Arunachal Pradesh, India

Mehao Wildlife Sanctuary, situated in the state of Arunachal Pradesh, is part of an important biodiversity hotspot in the north-eastern part of India in the Himalayas. The current study deals with the identification of important wildlife habitats in the sanctuary. We used a supervised classification technique to delineate these habitats in the sanctuary, which are used by several mammals and bird species encountered during camera trap and sign surveys conducted between November 2017 and May 2020. Satellite images from Sentinel – 2A were used to classify the land use land cover (LULC) of the sanctuary. The LULC information was generated by using a maximum likelihood classifier. We classified a total of thirteen LULC classes, i.e., water, built-up, agriculture, orchard, grassland, bamboo forest, bamboo-mixed forest, riverbed, barren land, snow, wild banana, riverine forest and mixed forest. LULC classification reveals a high percentage of mixed forest, about 69.9%, followed by wild bananas at 7.2%. The commission and omission error rates, however, are high for riverbed and agriculture (0.5) and bamboo forest (0.5), respectively. The accuracy assessment showed an overall classification accuracy of 88.5% with a Kappa coefficient of 0.87. The abundance of mammals was high in the mixed forest, but Ivlev's electivity index shows that species generally avoided this habitat and preferred specialized forest habitats, such as bamboo forest, bamboo-mixed forest, grassland, riverbed and riverine forest. Our LULC map will provide a baseline for potential planning and monitoring changes of wildlife habitats in Mehao WLS.

Media multitasking is linked to attentional errors, mind wandering and automatised response to stimuli without full conscious processing

ABSTRACT Previous findings on media multitasking, sustained attention and inhibitory control have been mixed with some studies linking heavy media multitasking with impaired sustained attention and inhibitory control whereas others have failed to find any relationship. This study examined the relationships between these factors using a three-state attentional disengagement model that focuses on transient aspect of attention. A sample of 238 participants completed a media use questionnaire and a Sustained Attention to Response Task (SART) with mind-wandering probes. They also completed the Barratt Impulsivity Scale and Boredom Proneness Scales. Heavier media multitasking was associated with higher rates of omission and commission errors and greater response variability in the SART. Heavier media multitaskers tended to mind wander more without awareness rather than with awareness, suggesting a more progressed form of attentional disengagement during the SART. They also made more anticipatory responses that suggest automatic responses to task stimuli without fully processing the stimuli. Higher media use scores were associated with greater self-reported attentional impulsivity and boredom proneness. Media multitasking was not linked to perceived difficulty or interestingness of the task. Taken together, these results provide evidence that heavy media multitasking is linked to poorer sustained attention and inhibitory control.

Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls.

(1) Objective: The aim of this study was to examine the effect of high altitude on inhibitory control processes that underlie sustained attention in the neural correlates of EEG data, and explore whether the EEG data reflecting inhibitory control contain valuable information to classify high-altitude chronic hypoxia and plain controls. (2) Methods: 35 chronic high-altitude hypoxic adults and 32 matched controls were recruited. They were required to perform the go/no-go sustained attention task (GSAT) using event-related potentials. Three machine learning algorithms, namely a support vector machine (SVM), logistic regression (LR), and a decision tree (DT), were trained based on the related ERP components and neural oscillations to build a dichotomous classification model. (3) Results: Behaviorally, we found that the high altitude (HA) group had lower omission error rates during all observation periods than the low altitude (LA) group. Meanwhile, the ERP results showed that the HA participants had significantly shorter latency than the LAs for sustained potential (SP), indicating vigilance to response-related conflict. Meanwhile, event-related spectral perturbation (ERSP) analysis suggested that lowlander immigrants exposed to high altitudes may have compensatory activated prefrontal cortexes (PFC), as reflected by slow alpha, beta, and theta frequency-band neural oscillations. Finally, the machine learning results showed that the SVM achieved the optimal classification F1 score in the later stage of sustained attention, with an F1 score of 0.93, accuracy of 92.54%, sensitivity of 91.43%, specificity of 93.75%, and area under ROC curve (AUC) of 0.97. The results proved that SVM classification algorithms could be applied to identify chronic high-altitude hypoxia. (4) Conclusions: Compared with other methods, the SVM leads to a good overall performance that increases with the time spent on task, illustrating that the ERPs and neural oscillations may provide neuroelectrophysiological markers for identifying chronic plateau hypoxia.

Acute effects of mindful interval exercise on cognitive performance in a higher education setting

Interval exercise (IE) has been shown to have acute facilitating effects on cognition; however, the existing literature has been limited to laboratory settings and has focused on manipulating the parameters of exercise bouts during IE. This study included two classroom-based experiments to (1) investigate the effect of an acute bout of IE delivering mindfulness activity during its recovery intervals (mindful IE) on cognitive performance, and (2) compare cognitive performance following acute bouts of mindful IE with non-mindful IE. Experiment 1: Using a class-based within-subject crossover design, 59 participants completed the Stroop, d2, and trail-making tests to measure inhibitory control, attention, and cognitive flexibility, after a 30-min non-exercise or mindful IE session on separate counterbalanced days. Experiment 2: Using a similar design, 70 participants were assigned to two groups to receive a non-exercise and an IE session with (mindful) or without (non-mindful) mindfulness-based recovery intervals on separate counterbalanced days. Results from Experiment 1 showed superior d2 performance following the mindful IE than the non-exercise session. Although Experiment 2 found exercise-related decreases in commission error rate during the d2 test in both groups, the non-mindful group showed additional decreases in omission and total error rates. Further, higher scores on the nonreactivity facet of dispositional mindfulness were correlated with larger decreases in omission and total error rates during the d2 test for the mindful IE group. No exercise-related effect was found for outcomes of the Stroop and trail-making tests in both experiments. These findings in the selective improvements in d2 test performance are the first to suggest the feasibility of integrating mindfulness activity into the recovery intervals of IE for enhanced cognitive performance that may depend on individual differences in dispositional mindfulness.

Impact of Early Childhood Malnutrition on Adult Brain Function: An Evoked-Related Potentials Study.

More than 200 million children under the age of 5 years are affected by malnutrition worldwide according to the World Health Organization. The Barbados Nutrition Study (BNS) is a 55-year longitudinal study on a Barbadian cohort with histories of moderate to severe protein-energy malnutrition (PEM) limited to the first year of life and a healthy comparison group. Using quantitative electroencephalography (EEG), differences in brain function during childhood (lower alpha1 activity and higher theta, alpha2 and beta activity) have previously been highlighted between participants who suffered from early PEM and controls. In order to determine whether similar differences persisted into adulthood, our current study used recordings obtained during a Go-No-Go task in a subsample of the original BNS cohort [population size (N) = 53] at ages 45–51 years. We found that previously malnourished adults [sample size (n) = 24] had a higher rate of omission errors on the task relative to controls (n = 29). Evoked-Related Potentials (ERP) were significantly different in participants with histories of early PEM, who presented with lower N2 amplitudes. These findings are typically associated with impaired conflict monitoring and/or attention deficits and may therefore be linked to the attentional and executive function deficits that have been previously reported in this cohort in childhood and again in middle adulthood.

Finding what you don’t know: Testing SDM methods for poorly known species

AbstractAimA limitation of species distribution models (SDMs) is that species with low sample sizes are difficult to model. Yet, it is often important to know the habitat associations of poorly known species to guide conservation efforts. Techniques have been proposed for modelling species’ distributions from a few records, but their performance relative to one another has not been compared. Because these models are built and evaluated with small data sets, sampling error could cause severely biased sampling in environmental space. As a result, SDMs are likely to underpredict geographic distributions given small sample sizes. We perform the first comparison of methods explicitly promoted or developed for predicting the geographic ranges of species with very low sample sizes.LocationNorth Carolina, USA.TaxonSouth Mountains Grey‐cheeked Salamander (Plethodon meridianus).MethodsUsing the sparse, existing georeferenced records ofP. meridianus, we built SDMs using a range of methods that previous researchers have argued should work for low sample sizes. We then tested each SDM’s ability to accurately predict independent survey data that were not georeferenced prior to our study. We compared SDMs using omission error and AUC.ResultsRoughly half of the models successfully predicted survey records in the range centre, and all models had high omission error rates in the range exterior. In the range of interior or exterior, the ‘ensemble of small models’ technique produced SDMs with high omission error rates. Spatial filtering had a negligible impact on model performance. Most, but not all, models outperformed predictions using distance from known populations. Using one of the best‐performing methods, we developed an improved range map ofP. meridianus.Main ConclusionsGeographically peripheral populations were difficult to predict for all SDMs, though some methods were clearly inferior for our data set. We recommend that when sample sizes are low, researchers use Maxent with species‐specific model settings.

Fully Connected Convolutional Neural Network in PCB Soldering Point Inspection

In Electronics Manufacturing Services (EMS) industry, Printed Circuit Board (PCB) inspection is tricky and hard, especially for soldering point inspection due to the extremely tiny size and inconsistent appearance for uneven heating in reflow soldering process. Conventional computer vision technique based on OpenCV or Halcon usually cause false positive call for originally good soldering point on PCB because OpenCV or Halcon use the pre-defined threshold in color proportion for deciding whether the specific soldering point is OK or NG (not good). However, soldering point forms are various after heating in reflow soldering process. This paper puts forward a VGG structure deep convolutional neural network, which is named SolderNet for processing soldering point after reflow heating process to effectively inspect soldering point status, reduce omission rate and error rate, and increase first pass rate. SolderNet consists of 11 hidden convolution layers and 3 densely connected layers. Accuracy reports are divided into OK point recognition and NG point recognition. For OK soldering point recognition, 92% is achieved. For NG soldering point recognition, 99% is achieved. The dataset is collected from KAGA Co. Ltd Plant in Suzhou. First pass rate at KAGA plant is increased from 25% to 80% in general.